IoT in Healthcare: Applications and Use Cases

5G Is Here Learn what it means for your enterprise Download PDF The growth of IoT into nearly every business arena from medical devices and healthcare applications to industrial IoT (IIoT) is amazing to behold. Our series highlighting the range of use cases for the Internet of Things illustrates how IoT products and services are being deployed around the globe, by industry. This article focuses on the range of IoT use cases in healthcare today, supporting patients, doctors, medical staff and first responders in achieving better outcomes. Why is IoT in healthcare a fast-growth industry? There are a number of reasons, including the capability of connected devices to monitor health vitals, route data, provide alerts, administer medications and automate critical processes. The medical industry is adopting Internet of Things technologies in everything from medical wearables to patient monitoring and pharmaceutical temperature monitoring in order to improve accuracy, promote efficiency, reduce costs, meet compliance requirements and enhance health and safety. In fact the term "healthcare IoT" or HIoT has been coined to describe this market niche. Digi solutions support development and deployment of a broad range of products and applications in this space.

Let's take a tour of some examples of IoT in medical and healthcare, including Digi customer case studies that help to demonstrate the breadth of IoT applications in healthcare patient support. You can find more examples of applications for a range of industries in the  Customer Stories  section of the Digi site.    

IoT in Healthcare - Promoting Hygienic Hospitals and Clinics

Sterile hospital laboratory

  • Contact tracing
  • Pathogen detection
  • Thermal detection (elevated temperature)
  • No-touch sanitation dispensers
  • Automated hand hygiene 
  • Hygiene monitoring
  • Workspace and floor sanitation
  • Air quality sensors
  • Biometrics scanners
  • Vital signs monitoring
  • Remote patient communications
  • Instrument sterilization
  • Medication dispensing

Here are a few examples of how Digi customers have built healthcare applications supporting sanitation and hygiene.  

Floorbotics

Sanibot Floor Robot

Clean Hands Safe Hands

Hang washing station

Get to Market Faster and Avoid Costly Mistakes: New FDA Guidance for RF Wireless Medical Devices

BOS Technology

BOS Technology device

IoT Wearables: Health Monitoring, Injury Reduction and Contact Tracing

With advances in Bluetooth technology, and the need for immediacy in feedback, wearables are an enormous growth area for IoT in healthcare. In this section, meet some of the Digi customers who are designing wearables for wellness, ergonomics, contact tracing and patient/doctor connectivity.  

Kinetic Wearables

Reflex Wearable Device

Kinetic Updates for Contact Tracing

Additional worker safety concerns surfaced with the Covid-19 pandemic, especially in the close environments of industrial workplaces. Reducing worker virus transmission and quickly and accurately identifying potential risks have become key priorities in keeping industrial employees safe and operations open. “When Covid infections among industrial workers began forcing facility shutdowns, we saw a need to better leverage smart technology to connect essential employees and help protect them from illness,” said Bansal.   

Related content:  Watch the Kinetic video

Lasarrus wearables for physical therapy.

LASARRUS WearMe medical wearable

"In the era of COVID-19, fewer patients or clinicians want to have in-person patient encounters," said co-founder Nelson Emokpae. "We’re recognizing that the LASARRUS WearME can play an important role in fighting the pandemic from a telehealth perspective. First, patients can wear our device from home and enable the clinician to quickly obtain a complete physiological assessment. That will improve patient outcomes without exposing them to unnecessary risk."

case study of health care in iot

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Learn how industry-leading Digi solutions are purpose-built for today’s connected medical devices.

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IoT in Patient Care and Pain Medication Management

Avancen Device

Related content: Learn about the secure, scalable Digi ConnectCore 8 module family

Medical iot: 3d imaging technology.

Eykona imaging device

Pharmaceutical Temperature Monitoring and Compliance

Pharmaceuticals

IoT Use Cases in Emergency Response and Critical Communications

Ambulance emergency response

Related white paper: Mission Critical Communications for Traffic Management

Iot in telemedicine, remote surgery, robotics.

Telemedicine

Related blog post:  How 5G Will Impact the Future of Healthcare

  More IoT Applications and Use Cases In addition to IoT use cases in healthcare, you can find more IoT use cases and examples in these blog posts:

  • IoT Applications for Retail
  • IoT Applications in Smart Cities
  • IoT Solutions for Transportation
  • IoT Applications for Precision Agriculture
  • Digital Signage IoT Applications
  • Connected Vehicle Use Cases
  • IoT Applications in Supply Chain
  • IoT Drones: How the Use Cases are Changing

 How to Connect with Digi:  

  • Contact us to start a conversation about your IoT development or deployment plans.
  • Sign up for our newsletter to keep up with the latest content and new product introductions.
  • Connect with us on LinkedIn , Twitter , or Facebook .

Note: This post was initially published in February of 2019, and was updated in July of 2021.

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Medical Device Security: What OEMs Need to Know

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Internet of Things pp 115–133 Cite as

IoT Applications in Healthcare

  • Qi Lin 6 &
  • Qiuhong Zhao 6  
  • First Online: 14 July 2021

1345 Accesses

2 Citations

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 305))

This chapter aims to review IoT applications in the healthcare domain that are representative and active in practice and research. The chapter introduces the existing IoT products in the healthcare market; reviews the studies on developing, using, and improving IoT healthcare applications; and presents and discusses the recent trend and focus of IoT healthcare applications. First, the chapter describes a general picture of IoT healthcare applications. And then, the chapter studies IoT healthcare applications in three scenarios:

Acute disease care. Three applications are introduced to show how IoT benefits acute care: vital sign monitoring, acute care telemedicine, and IoT-based detection and control of infectious diseases.

Chronic disease care. The chapter focuses on remote health monitoring used for patients with chronic diseases, especially patients with Alzheimer’s disease, diabetes, and heart failure.

Self-health management. The chapter pays attention to the most common representative device for self-health management, smartwatches and analyzes the two main functions of smartwatches on self-health management, sleep monitoring and exercise monitoring.

  • IoT applications
  • Acute disease care
  • Chronic disease care
  • Self-health management
  • Hospital operation management

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Lin, Q., Zhao, Q. (2021). IoT Applications in Healthcare. In: García Márquez, F.P., Lev, B. (eds) Internet of Things. International Series in Operations Research & Management Science, vol 305. Springer, Cham. https://doi.org/10.1007/978-3-030-70478-0_7

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IoT in Healthcare: How Connected Devices are Shaping the Medical Field

  • IoT in Healthcare: How Connected ...

Healthcare is becoming smarter in front of our eyes. This is most evident in the field of IoT in healthcare, where, in recent years, we have seen the emergence of technological innovations such as smart hospitals , medical devices for real-time data analysis, and feature-rich wearable health trackers. But the IoT development doesn’t even think to stop there.

Forecasts for the IoT healthcare market project surge from $128 billion in 2023 to an impressive $289 billion by 2028. That’s huge and tells us that IoT attracts large investments that further fuel innovation in this sphere. Considering the global aging population, the demand for IoT medical devices that monitor patient vitals will only grow. So it would be a big mistake not to seize the opportunity. To help healthcare organizations realize the potential of the Internet of Things, in this article, we will cover what it is, how it impacts healthcare, its benefits, and challenges.

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Table of Contents

What is IoT?

The Internet of Things (IoT) is a network of devices embedded with IoT sensors and connected to the Internet to interact with each other. These devices collect, process, and exchange vast amounts of data with other systems or devices to optimize operations, enhance user experiences, and enable real-time decision-making.

IoT in healthcare, also known as IoMT solution or Internet of Medical Things, takes this concept and tailors it exclusively for the medical field. So, it’s a network of medical devices, software, and tech solutions that can monitor patient health, manage treatments, and even assist in surgical procedures. Also, IoT devices in healthcare are often designed with industry standards in mind, which adds to the accuracy, reliability, and compliance with health regulations. 

They range from wearable health monitors that track vital signs like heart rate and blood oxygen levels to more complex systems. For instance, smart hospital beds can adjust themselves to improve patient comfort and prevent bedsores.

IoT in Healthcare

The Transformative Impact of IoT in Healthcare

The ability of IoT medical devices to gather and analyze massive amounts of real-time data holds huge potential for refining healthcare practices and bringing medicine closer to patients. IoT replaces part of the visits to the doctor with telemedicine solutions and online consultations, saving time and costs. Patients don’t need to go to the hospital without special necessity and spending hours in queues. Instead, they can easily and conveniently get consultations and treatment online and even do medical tests remotely. 

Due to IoT technology, doctors and medical personnel can view and analyze patient reports, ongoing treatments, and medical histories to provide more personalized and effective care. 

Generally, all sorts of patient monitoring systems equipped with IoT sensors continuously track health conditions and provide real-time health status. If certain parameters go beyond the norm, they can communicate with other devices to take necessary actions that would help save someone’s life. At the same time, this data is sent to the cloud so that doctors can swiftly respond to an emergency situation and provide timely care.

Besides improved patient outcomes, the application of IoT in healthcare increases the productivity of medical staff and streamlines hospital workflows. Modern IoT applications help better manage patient data, schedule appointments with physicians, and automatically send health notifications and reminders to patients. So, the game-changing impact of IoT in healthcare is real and beyond doubt.

Impact of IoT in Healthcare

Applications of IoT in Healthcare

IoT reshapes traditional, old-fashioned treatment practices, offering new approaches and opportunities to remove shortcomings in the healthcare system, like outdated processes, supply chain inefficiencies, and lack of interoperability. We’ve picked the most impactful IoT applications in healthcare capable of driving positive changes in the sector.  

Smart Medical Devices

Patients suffering from chronic diseases such as diabetes, asthma, or hypertension can enjoy the benefit of IoT in healthcare through wearable devices that monitor corresponding health metrics. These devices, ranging from fitness trackers and smartwatches to temperature and smart blood pressure monitors, collect essential data such as oxygen levels, blood pressure, and heart rate. This information offers insights into how lifestyle impacts their condition, helping individuals make healthier choices. Upon detecting irregular health indicators, wearables alert users to potential problems and recommend how to deal with them.

Smart Medical Devices

Glucose monitors, blood pressure cuffs, and other home-based IoT devices, for example, can automatically record readings and send this information to healthcare providers. This real-time data enables doctors to alter treatment plans and make medication adjustments, often without the need for an in-person visit.

IoT healthcare companies continuously work on improving devices to make them more accurate and user-friendly so that patients of all ages can easily introduce them into their daily lives.

Hospital Operations and Asset Management

IoT helps medical personnel spend less time on management and routing tasks. Asset tracking is an area that can benefit a lot from applications of IoT in the healthcare industry. IoT-enabled devices monitor the location and status of basic supplies and expensive medical equipment like wheelchairs, defibrillators, and portable monitors. Although it might seem mundane, knowing the status and location of items can minimize costs and efficiency losses, especially when staff are overburdened and unable to find the necessary equipment. IoT can save time, resources, and potentially lives when deployed properly.

Energy management that is more sustainable and efficient is possible thanks to IoT. Imagine how much energy and operational costs hospitals can save with smart sensors and meters that control lighting, heating, and air conditioning systems in real-time. In doing so, healthcare facilities can help create a greener healthcare infrastructure.

Managing patient flow, especially during peak times, is not an easy task, but IoT can help here, too. Hospitals can better manage patient admissions by using IoT devices for patient flow optimization, minimizing waiting times and overcrowding, which improves patient experience.

Telemedicine and Remote Monitoring

Smart connected devices can constantly monitor patients’ vitals, such as heart rate, blood pressure, glucose levels, and more, outside the hospital setting. This is a true relief for chronically ill patients who now don’t need to visit hospitals frequently for routine checkups. Most of their follow-ups, as well as the continuous oversight over their health conditions, can be well managed remotely.

Remote Patient Monitoring

While nothing can replace face-to-face experience, doctors can track patient’s conditions through remote patient monitoring tools and provide medical advice through video consultations. A continuous flow of relevant information allows for personalized care plans, as physicians can adjust treatments based on the received data. Telemedicine solutions offer convenient online consultations for those living in remote areas and elderly patients while also easing the load on healthcare facilities.

Medication Management

Adherence to prescribed drug regimens can mean the difference between recovery and relapse. This challenge has been addressed by an IoT application in healthcare: wearables and devices that remind patients to take their medications on time. For example, smart pill bottles have sensors to track when the bottle is opened and send patients notifications if they miss a dose. Caregivers can also receive alerts on a patient’s missing dose to provide timely support and ensure the continuity of care. By analyzing the patient’s compliance history, doctors can understand the effectiveness of prescribed drugs and adjust treatment plans.

IoT in Healthcare: Real-life Examples

IoT in medical services allows for improving customer experience and saving time. So, it’s not surprising that many companies invest in IoT technology to develop software and devices to bring even more convenience, simplicity, and accessibility to the sector. We have selected the IoT in healthcare examples that have found wide use and made patients’ lives comfortable. 

  • Kinsa Smart Thermometers collect information on individuals’ temperature and send it to a mobile app via Bluetooth. The application keeps the history of temperature records and can thus help identify health trends and potential outbreaks. 
  • Inspiren’s iN Ecosystem . This system intends to improve patient experience and their stay in the hospital. It uses a network of sensors, analytics tools, computer vision, and other technology to track various factors such as room visits by staff, patient movement, and even the time since the last interaction with a healthcare professional. Thus, their IoT-based ecosystem helps create more responsive and attentive care experiences.
  • Dexcom Continuous Glucose Monitoring (CGM) System is an IoT device that provides real-time glucose level monitoring for diabetes management. It consists of a small sensor that measures and wirelessly sends glucose readings to a smart device, alerting users to potential episodes of hypoglycemia or hyperglycemia that require quick corrective actions.

Challenges of IoT in Healthcare

Developing devices with sensors to boost the quality of care, medical adherence, and patient experience is a high-tech solution to very human problems. It can provide doctors with unparalleled insight and help deliver timely care, but this comes with the challenges healthcare organizations should be aware of before adopting the IoT solution.

Challenges of IoT in Healthcare

Data Security . Cybersecurity challenges in using IoT in healthcare are very relevant and sharp. Medical patient information is highly confidential, and its transmission over the Internet increases the risk of breaches. As the number of connected devices that gather and exchange data grows, so does the risk of data leakage. That’s why healthcare organizations should build high protection of patient records to strengthen IoT security in healthcare, which can be achieved by implementing the following security measures:

  • Encrypt information 
  • Implement secure communication protocols
  • Set different levels of access to information
  • Use multi-factor authentication

Interoperability . One of the biggest problems in healthcare is siloed data that hinders comprehensive patient care. Plus, many IT infrastructures are running on legacy systems that complicate seamless integration with modern digital solutions and consequently the information exchange between different platforms. As a result, hospitals can’t benefit from IoT solutions without standardized data-sharing protocols. One viable way to solve this issue is the FHIR standard , which helps achieve data interoperability and easy information sharing.

Ethical Concerns . As IoT technology and its use cases spread, it makes ethical considerations difficult. While IoT devices simplify patients’ lives and reduce the need for in-person visits, their data can be easily collected and analyzed by third parties. Healthcare organizations should establish clear policies so patients understand how their data is used and stay informed about what information is being collected. This way, patients can enjoy the benefits of IoT while hospitals ensure their privacy is upheld.

Regulatory Landscape of IoT in the Healthcare Industry

The healthcare sector is highly regulated. When deploying IoT medical devices, hospitals should ensure their solutions comply with different regional and international regulations connected to patient data protection, medical device certification, and healthcare standards.

IoT regulations

FDA Guidelines

The FDA has been striving to strengthen the cybersecurity of IoT devices in healthcare since 2005. Recognizing the growing usage of medical devices, the FDA’s most recent draft guidance expects security across the entire product lifespan. 

So, manufacturers should now be able to protect and address any vulnerabilities of their products from initial design to post-market surveillance to ensure proper device functionality and patient safety. They must submit detailed plans and prove that the device can be updated, patched, and protected from any potential security issues. As manufacturers implement stronger security controls into their devices from the start, healthcare organizations and patients can gain confidence in the security of IoT devices.

International Regulations

Most countries have rigorous regulatory and approval processes for IoT in healthcare before devices enter the market and can be used by hospitals and patients.

In the EU, the Medical Device Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR) requirements mandate consideration of IoT medical device cybersecurity. Every medical equipment manufacturer seeking to market products in Europe must meet those requirements to ensure their devices are safe, function as expected, and are protected from security threats. The Medical Device Coordination Group (MDCG) regulatory body publishes detailed guidance documents to direct manufacturers on how to meet all the requirements of MDR and IVDR, specifically regarding cybersecurity measures.

IoT in Healthcare: Future Trends 

The impact of IoT in healthcare is already huge and is expected to grow even more in the coming years. As connected devices become more sophisticated and widely adopted, the future of IoT in healthcare is bright and will bring even more transformative changes to the sector. Let’s take a glimpse at what trends we can expect to see in the near future. 

Healthcare data analytics

With more data being generated by IoT devices, hospitals will realize the growing need to analyze this information as soon as possible so that decisions can be made based on the most relevant information. That’s why we will see an increased demand for real-time analytics platforms that can help healthcare organizations turn large volumes of patient data into actionable insights. Big data analysis and machine learning algorithms are widely applied to empower better medical decisions.

AI-Integrated IoT Healthcare Systems

When it comes to combining AI and IoT in healthcare, chances are together they will improve the way doctors approach diagnostics and disease detection. One of the key powers of AI in diagnostics is the ability to analyze a great deal of patient health data gathered by IoT devices quickly and with high precision. Traditional diagnostic methods rely on human interpretation, which can be prone to errors. With such valuable diagnostic capabilities at the early stages of diseases, doctors can intervene earlier to prevent disease progression before it becomes a serious health-threatening issue.

5G’s Impact on Healthcare IoT

5G can bring advancements in remote patient monitoring, communication with doctors, and telemedicine due to high-speed, low-latency Internet connection. This technology provides faster access to health information, improves telehealth services, and supports remote surgeries enabled by high-quality video streaming. IoT devices, such as an internal defibrillator, can immediately alert ER cardiologists to be ready for an incoming patient, with complete information received by the device. 

IoT in the Healthcare Industry: Final Thoughts 

IoT has already deeply penetrated the medical industry through monitoring systems, smart wearables, and medical devices. The application of IoT in healthcare allows medical employees to reduce costs, improve treatment outcomes, and rely on technology for monitoring patient’s health status. The examples of existing IoT in the healthcare market are vast, and its diversity and impact are only set to grow in the future. Medical practitioners who embrace IoT today will win tomorrow. 

So, if you want to join this shift to more connected healthcare, our team of experts can help. We’re a healthcare software development company offering a pool of IoT software development and IT consulting services that deliver as per client requirements. Contact us today to be among the first to benefit from IoT. 

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case study of health care in iot

  • Open access
  • Published: 01 March 2022

Internet of things in healthcare for patient safety: an empirical study

  • Tahera Yesmin 1 ,
  • Michael W. Carter 1 &
  • Aviv S. Gladman 2  

BMC Health Services Research volume  22 , Article number:  278 ( 2022 ) Cite this article

3195 Accesses

5 Citations

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Metrics details

Introduction

This study evaluates the impact of an Internet of Things (IoT) intervention in a hospital unit and provides empirical evidence on the effects of smart technologies on patient safety (patient falls and hand hygiene compliance rate) and staff experiences.

We have conducted a post-intervention analysis of hand hygiene (HH) compliance rate, and a pre-and post-intervention interrupted time-series (ITS) analysis of the patient falls rates. Lastly, we investigated staff experiences by conducting semi-structured open-ended interviews based on Roger’s Diffusion of Innovation Theory.

The results showed that (i) there was no statistically significant change in the mean patient fall rates. ITS analysis revealed non-significant incremental changes in mean patient falls (− 0.14 falls/quarter/1000 patient-days). (ii) HH compliance rates were observed to increase in the first year then decrease in the second year for all staff types and room types. (iii) qualitative interviews with the nurses reported improvement in direct patient care time, and a reduced number of patient falls.

This study provides empirical evidence of some positive changes in the outcome variables of interest and the interviews with the staff of that unit reported similar results as well. Notably, our observations identified behavioral and environmental issues as being particularly important for ensuring success during an IoT innovation implementation within a hospital setting.

Peer Review reports

Technology has come to be employed throughout the healthcare sector to improve patient outcomes and safety while reducing costs and optimizing resource utilization [ 1 ]. The International Telecommunication Union (ITU) projects that the Internet of Things (IoT)—alongside developments in item identification, wireless sensor networks, and embedded systems—will soon connect the world’s many devices in a sensory, intelligent manner [ 2 , 3 ]. The Federal Trade Commission (FTC) states IoT as “an interconnected environment where all manner of objects have a digital presence and the ability to communicate with other objects and people” [ 4 ]. Though the term IoT was first introduced and defined by Kevin Ashton in 1999 as a network of uniquely addressable and interoperable objects with radio-frequency identification (RFID) technology [ 1 ], gradually the modern IoT platform has empowered a steadily increasing number of connected devices, including RFID tags, mobile phones, and actuators to communicate through embedded sensors and relay enormous amounts of data with little to no human interaction [ 5 , 6 ]. These data can then be collected, recorded, and analyzed to improve the care-delivery process. While IoT is a relatively new concept in healthcare, it has long been employed in agriculture, environmental monitoring, food processing, smart grids, traffic management, home automation, firefighting, and mining [ 7 , 8 , 9 , 10 ]. While there are certainly technological challenges regarding privacy, trust, and security in the application of IoT in healthcare [ 11 , 12 ], many studies have explained the working principles of IoT and emphasized that IoT could transform healthcare [ 4 , 13 , 14 ].

Patient safety, one of the six elements of quality of care, is defined as avoiding injuries to patients while implementing care aimed at helping them [ 15 ]. The World Health Organization defines patient safety as the prevention of errors and adverse effects on patients associated with health care [ 16 ]. Globally, adverse events stemming from a lack of patient safety constitute one of the ten leading causes of death and disability; in high-income countries, 50% of these events can be prevented [ 16 ]. According to Morris and O’Riordan (2017), among these adverse events, inpatient falls constitute the most frequent reported safety incident in National Health Service (NHS) hospitals; Agency for Healthcare Research and Quality (AHRQ) reports that 700,000 to one million hospitalized patients experience falls each year [ 17 ]. Another report details the fall rate as 3–5 per 1000 bed-days; about one-third of these falls result in injuries, such as head trauma and fractures [ 18 ]. Healthcare-associated infections (HCAIs) constitute another major adverse event that stems from unsafe care. Nearly 1.7 million hospitalized patients acquire HCAIs while receiving treatments [ 19 ], and several studies have shown that improved hand hygiene can significantly mitigate these infections (by around 50%) [ 20 , 21 , 22 ].

Numerous interventions have been widely applied in hospital settings to improve patient safety. One such method entails the promotion of a culture of safety; another involves improvements to specific aspects of care delivery that staff identify as harmful to patients [ 23 ]. Several studies have assessed the recent trend of utilizing IoT in healthcare to improve patient safety [ 24 , 25 ]. Ahmadi et al. (2019) assert that IoT can be used to achieve several goals in hospital management, including preventing infections [ 1 ]. However, despite its various applications in patient safety, IoT still requires more research and experimentation, as most existing research is in the early stage of testing new methodologies [ 25 ]. Studies on the practical impacts of IoT on patient safety measures in a hospital setting are sparse. Thus, this study aims to explore an advanced IoT-based intervention in a hospital setting and empirically demonstrate its impact on patient safety. This research contributes real-life, application-based evidence to validate the claims in the literature that IoT improves patient safety by showing its impact on patient-fall and hygiene-compliance rates [ 24 , 25 ]. We use both pre-post and time-series analyses to demonstrate IoT’s impact on patient safety. Section 2 provides a comprehensive literature review on the application of IoT to prevent patient falls and improve hand hygiene. Section 3 details the methods used in this study. Section 4 reports our results, and Section 5 offers a discussion and some conclusions.

Related works

Internet of things in healthcare.

The application of IoT in medical fields is consistently expanding. In 2014, Xu et al. conducted a survey to provide a detailed review of the IoT architecture (alongside other key technologies) that is revolutionizing healthcare [ 8 ]. In 2015, Islam et al. surveyed the working methodologies and use of IoT in healthcare, considering various IoT services and applications for both single and clustered medical conditions [ 26 ]. Another review noted that IoT can empower individuals by providing cost-effective and personalized care in both clinical care (for in-patients) and remote monitoring [ 27 ]. One study assessed the underlying architecture of recent IoT applications in healthcare, such as a smart pill that measures medical adherence, ambient assisted living for elderly patients, and interactive m-health for diabetic patients [ 28 ]. Dimitrov detailed how the pharma industry partners with the tech industry to develop IoT-based healthcare systems that improve patient care [ 29 ].

While many researchers focus on the architecture and development of various IoT-based healthcare applications, some studies focus superficially on how IoT impacts certain aspects of hospital environments, such as patient safety and work efficiency. Many studies assert that IoT can significantly improve patient safety, as various alarm systems can alert care providers to evolving patient conditions, enabling them to act accordingly [ 30 , 31 , 32 ].

Internet of things and patient falls

IoT can be applied to reduce patient falls in both hospital and home settings. Kang et al. mentioned that achieving patient safety by reducing patient falls is a very significant application of IoT in hospitals [ 32 ]. Several studies have proposed IoT-based fall-reduction systems, most of which involve two device types: wearable devices and external systems [ 33 ]. In 2016, Vaziri et al. proposed a system to assess a person’s at-home falling risk and deliver a tailored exercise and fall-prevention program [ 34 ]. In 2017, Joshi and Nalbalwar proposed a vision-based fall detection and alert system that uses a single camera to detect features like orientation angle, aspect ratio, center of mass, and Hu moment invariants (calculated from the white pixels extracted from the silhouette of the foreground objects) to detect, document, and alert people to falls [ 35 ]. One study detailed a fall-detection system that provides a centralized system through a mobile application based on the cloud to gather data on all monitored persons [ 36 ]. In 2019, Yee et al. developed a wearable, sensor-based fall-prevention device that can differentiate between falling and non-falling cases with the help of a k-NN classifier [ 37 ]. In another 2019 study, Khan et al. proposed a wearable device consisting of a camera, gyroscope, and accelerometer that remotely detects patient falls [ 38 ]. Similarly, textile-based systems have also been proposed by other researchers [ 39 , 40 ].

Though many studies have proposed new fall-detection approaches, few have included empirical or intervention-based studies in a hospital setting to assess their performance. Vaziri et al. (2016) reported on the user-experience and user-acceptance aspects of their iStoppFalls system among older adults [ 34 ]. Another evaluation was conducted by Balaguera et al. (2017) in a medical-surgical unit of a teaching hospital, where a sensor pad was placed between the mattress and bedsheet of the recruited patients. Their study reported no bed falls over 234 patient days following this system’s implementation among 91 patients. Nursing staff responded to alerts from the fall-prevention system on their mobile phones in an average of 45.9 s. Though this study compared the post-intervention fall rate with the pre-intervention fall rate, it did not consider all of the unit’s patient types [ 41 ]. Table 1 presents a list of articles that applied IoT to detect or prevent patient falls, where ‘Yes’ indicates the presence and ‘No’ indicates the absence of the mentioned attributes in that tables from that study.

Internet of things and hand-hygiene compliance

As hand-hygiene (HH) compliance is one of the most important factors to reduce hospital-acquired infections, accurate measurement of HH compliance among healthcare providers is a vital element of high-quality care delivery. HH compliance is mainly measured manually by hospital auditors. However, studies suggest that the Hawthorne effect—the change in providers’ behavior when they are aware of being monitored—challenges the value of human auditing and encourages the use of automatic HH-measuring devices. Therefore, IoT has great potential to improve HH compliance. Research has shown that one automatic HH-monitoring system can precisely identify the times and locations of hand washing. However, it could not aid in a badge-based system, as it failed to recognize which individuals were cleaning their hands [ 42 ]. In another retrospective study, Xu et al. (2021) measured the effectiveness of an IoT-based management system on HH compliance in an intensive care unit. They found that the new system increased the HH compliance rate among all staff aside from the cleaners [ 43 ]. Similarly, in another study, an IoT-based automatic monitoring system was implemented to collect real-time data and employ gamification to improve HH compliance among nurses in both simulation and clinical environments. While the simulation setting revealed a 100% HH-compliance rate, the nurses showed little interest in considering badges for future improvement [ 44 ].

Interrupted time series analysis (ITS) with segmented regression

Segmented regression is a statistical method that is widely used to estimate intervention effects in ITS studies. Many researchers have used this quasi-experimental method to evaluate intervention impacts [ 45 ]. Wagner, Soumerai, Zhang, and Ross-Degnan (2002) used this method to evaluate an intervention aimed at improving the quality of medication consumption [ 46 ]. In another study, researchers conducted a segmented regression analysis of a four-year interrupted time series to identify the impact of a policy intervention aimed at reducing the inappropriate use of key antibiotics. Their analysis revealed a significant decrease in total use and cost in the two years following the intervention [ 47 ]. Similarly, segmented regression for ITS studies was used in many other healthcare interventions, including to assess incremental costs [ 48 ], evaluate screening effectiveness [ 49 ], and evaluate new strategic interventions [ 50 ].

Diffusion of innovation theory

We have designed our interview questionnaire based on Roger’s Diffusion of Innovation Theory, which is extensively used by the researchers to design studies that report user experiences. Based on this theory, there are four main determinants of the success of an innovation: communication channels, the attributes of the innovation, the characteristics of the adopters, and the social system. The attributes of innovation include five user-perceived qualities, which are relative advantage, complexity, compatibility, observability, and trialability [ 51 ]. Researchers used these five elements in their study to identify the justification of some of the clinical behaviors [ 52 ]e, to analyze nurses’ perceptions toward using a computerized care plan system [ 53 ], and to understand the factors impacting the use and patient acceptance of consumer e-health innovation [ 54 ].

Methodology

Study setting.

This study was conducted at Mackenzie Health (MH) in Ontario, Canada, which has been identified as a leader of smart hospitals [ 55 ]. A pioneer in change, MH has implemented the data-driven concept of IoT in one of their strongest care units: the Mackenzie Health Innovation Unit (MHIU) at their Mackenzie Richmond Hill Hospital (MRHH) site. Established in June 2014, the MHIU has integrated modern technologies to improve the quality and efficiency of care while limiting costs.

Mackenzie health innovation unit: internet of things in healthcare intervention

The MHIU is a first-in-Canada hospital ward with 17 rooms (34 beds) that has embraced IoT to develop safe and efficient care delivery while continually producing real data [ 56 ]. These real data enable the intelligent evolution of care delivery over time. The applied technologies are as follows:

Smart patient beds: Smart beds are implemented to support safety protocols for at-risk patients by reducing harmful events, such as patient falls. Caregivers set the side rails, brakes, and safety alarms before leaving a patient’s room, and the bed (i) notifies caregivers if the patients leave their bed through the “bed exit alarm,” (ii) reminds caregivers to shift the patients’ positions to avoid bed sores through a “patient turn frequency reminder,” (iii) prevents a false bed exit alarm through awareness of when a nurse is in close proximity, and (iv) facilitates patient requests through an integrated call-bell system. This call bell consists of three call-type buttons—normal, pain, and bathroom and has a speaker that allows nurses to remotely communicate with their patients.

Smart beds, though similar in appearance to normal beds, have several sensors to collect and transmit data to a centralized server. They usually collect the following information:

Guardrail status: It is important to know whether the guardrails are lowered or raised to prevent falls.

Patient weight: The bed has pressure sensors to ensure accurate and timely weight measurements, avoiding lifting-related injuries among caregivers.

Bed angle: The elevation of the head of the bed is important for patients with respiratory difficulties.

Smart Hand Hygiene support solutions: Through proximity RFID sensors, staff are monitored for HH practices and are alerted if they forget to wash their hands. HH stations are situated at the entrance and inside of each patient’s room. The HH system records whenever a caregiver uses it or misses an opportunity to clean (e.g., if a staff member enters the patient room without using the HH system). The sensors collect data on two HH moments (before entering and after exiting a room), handwashing locations, and room numbers.

Smart badges: Mackenzie Health assigns RFID badges to caregivers that identify their location. This enables the system to transfer patient calls to the nearest caregiver. Sensors are placed throughout the unit to quickly capture accurate caregiver locations. These badges also enable communication between staff and allow for rapid caregiver response times.

Dome light indicators: Installed outside (above the door) of each patient’s room and synced with the call-bell system, dome light indicators clearly show when a patient is at high fall risk. The status board lights up when a patient has called for assistance and brightly displays an “N” symbol when a caregiver is present in the room.

Wall-mounted call stations and mobile phones: Mackenzie Health has wall-mounted call stations and mobile phones to enable nurses to easily receive calls from patients. Each wall-mounted call station is a static touch-screen device that enables caregivers to answer, hold, or dismiss patient calls, displaying patients’ names, call type, patient room, and bed number. The stations are associated with the unit’s RFID location system. This advanced feature transmits patients’ calls directly to the call station closest to the assigned nurse of the calling patient. If a nurse moves before answering the call, the call is routed to the new nearest station. This system also enables nurses to call one another by viewing their location and making direct calls. The MHIU also provides nurses with mobile phones to accelerate processes pertaining to patient scans and diagnostic tests.

Smart stations: Smart stations are mounted in every patient room and provide specific patient information to their caregiver whenever necessary. This enables caregivers to respond quickly to the needs and requirements of the patients.

MHIU was intended to tests the benefits of IoT before applying these new technologies across their MRHH site and to their newly built hospital named Cortellucci Vaughan Hospital.

Quality-of-care dimensions: patient safety

According to the existing literature, patient safety is fairly sensitive to IoT technologies [ 57 ]. As noted in the literature review, indicators of patient safety such as patient falls and HH compliance have been thoroughly investigated. In line with existing research, this study examines the following outcome indicators [ 58 ]:

(i) Patient-fall rate: A patient fall is defined as an event that results in a person inadvertently coming to rest on the ground or floor [ 18 ]. The patient-fall rate is measured as [ 58 ]:

(ii) The HH-compliance rate is measured as [ 59 ]:

(iii) Staff experience: In addition to these outcome variables, existing research has indicated that staff experience during similar interventions has been important for perceived success among the users [ 22 ]. Therefore, we investigate staff experiences through qualitative semi-structured interviews. We received research ethics board’s approval to perform the interviews and all the interview methods were conducted in accordance with the relevant guidelines and regulations.

Pre-post intervention study

We conducted a pre-post intervention study to evaluate the outcome of IoT implementation in the MHIU. We analyzed two indicators of patient safety, patient falls and HH compliance, using various statistical measures, including t-tests, chi-square tests [ 60 ], Mann-Whitney tests, and interrupted time series (ITS) analysis with segmented regression [ 45 , 46 ]. Data collected between 2012 and 2016 were used to conduct a pre-post time series analysis of patient-focused indicators (patient falls) and a post-intervention analysis of staff-focused indicators (HH compliance). Since the MHIU is the only unit to have undergone this intervention, we could not find another unit or hospital implementing these technologies to use as our control group. However, there is a distinct date on which the MHIU implemented IoT, enabling sound pre-intervention and post-intervention interrupted time series analysis [ 61 ]. Additionally, we analyzed staff experiences by conducting semi-structured qualitative interviews using Roger’s Diffusion of Innovation Theory [ 51 ].

Interrupted time series analysis with segmented regression

We analyzed the relationship between intervention and outcome using segmented regression, which is widely used when different timeframes are used as segments. We used the following formula to identify the impact of the intervention on patient falls [ 46 ]:

Where i. Y t is the mean outcome at time t , ii. β o is the baseline level of the outcome at time zero, iii. β 1 estimates the change in the mean outcome in unit time increase before the intervention (i.e., the baseline trend), iv. β 2 estimates the level change in the mean outcome immediately after the intervention, and v. β 3 shows the slope change following the intervention (post-intervention slope/time is a scaled interaction term to identify the effect of the time after the intervention). The sum of β 3 and β 1 is the post-intervention slope [ 61 ].

Staff experience based on diffusion of innovation theory

We have conducted semi-structured, one-on-one, in-depth [ 62 ] interviews with the staff of MHIU to document their perspectives regarding the intervention’s effectiveness. The research objective of the interviews was to evaluate the MHIU before and after the intervention and identify the recommendations for the future. We created our questionnaire from Roger’s Diffusion of Innovation Theory, including five questions based mainly on (i) relative advantage (what the nurses experienced following implementation), (ii) compatibility of new technologies in their daily work, (iii) complexity, if any, (iv) trialability (problems faced following the implementation), and (v) observable results (changes observed as a result of the implementation). We also had a separate section to ask some demographic questions on gender and age.

Participant inclusion criteria of this interview were: (i) at least one year of full-time work experience at MH-so that they are well aware of the changes of the innovation unit, (ii) experience working for M and one other unit in the hospital- thus they can compare and report the changes of this unit, and (iii) expertise in using all technologies implemented in this unit- this will enable them to demonstrate all the aspects of the requested queries during the interview.

As this was only one unit of the hospital, considering that the total number of nurses work in this unit who meet the inclusion criteria we have interviewed 5 nurses- 3 Registered Practical Nurses (RPN) and 2 Registered Nurses (RN) for 30 min. An information document on the intervention and purpose of the interview and consent form for signature was provided to the nurses beforehand of the interview.

Data collection and preparation

This study also used data on the patients’ age, gender, falls, and staff HH compliance. Though the MHIU was established in June 2014, the IoT technology was not fully functional until August 2014; therefore, data collected prior to August 2014 were analyzed as pre-intervention data, and data collected after August 2014 were analyzed as post-intervention data.

  • Patient falls

Data on patient falls from January 2012 to October 2016 were collected to conduct this study. Incident reports contained information on age, gender, time of fall, and type of fall.

Hand-hygiene compliance

Data from September 2014 to October 2016 on two moments of HH (before entering and after exiting a room), station type, and staff type were collected by sensors situated on each room’s HH system. These data contained a serial number, staff type, room number, and timestamp for each instance in which someone cleaned their hands. While analyzing more than one million rows of information on HH, we performed rigorous data cleaning. Data with any of the following three irregularities were deleted from the dataset:

(i) There should always be paired information on HH upon a caregiver’s entrance into and exit from a room. However, we noticed that, sometimes, there was multiple (usually 2) entrance information at the same time for a single room for the same person. In such cases, we deleted all but one entry.

(ii) Sometimes, HH information on the time of exit has been reported as an entry. As we cannot confirm the actual reason for such entry reports, we deleted these types of data.

(iii) When collecting HH information from sensors in a particular room, we found inputs from different rooms with missing information on the entrance or exit moments. For example, Room 0011 was present in the set of data for room 1133, and we could not find any explanation for this type of behavior. Such information was deleted from the dataset.

Staff experience

Nurses’ responses during these 30-min interviews were recorded on a laptop. Later, each interview was transcribed manually to a word processing document. The transcribed data were grouped based on the nurses’ responses on each technology for each of the five questions. For example, the first question was, “what are the benefits or disadvantages you are experiencing for using smart Beds, dome light indicators, and smart hand hygiene support system”. Their response, on smart beds for example, “it used to be falls every day but now we don’t see many falls in the unit” was grouped under the response for the first questions which is a relative advantage.

Descriptive analysis

From January 2012 to October 2016, 371 inpatient falls were reported in the MHIU; these were classified into 11 different types: bed/crib, chair/wheel, fainted, lost balance, lowered to the floor during transfer, slipped, toilet/commode, transfer, tripped, tub/shower, and other. A detailed descriptive analysis regarding these types is provided in Table  2 . Most of the patients who fell were male (60%) with a mean age of 77.05 years. Of the 68 falls caused by beds (18.3% of the total), 44 occurred during the pre-intervention period (mean rate of 1.27 per quarter) and 24 occurred during the post-intervention period (mean rate of 0.91 per quarter). A statistical analysis using t-test comparing the pre- and post-intervention periods demonstrated no statistically significant change in average falls/quarter rate and average bed falls/quarte rate—however the rate of bed falls declined from 21 to 15% of the total falls following the intervention.

We investigated further to identify the times at which falls are occurring most frequently. Figure  1 illustrates that falls happen during almost every hour of the day; however, 10 p.m. – 12 a.m. featured the highest number of falls.

figure 1

Number of total falls by the time of day

ITS of patient falls

As no statistically significant changes have been found between the pre- and post-intervention periods, we conducted an interrupted times series analysis. Initially, we modeled an ordinary least square regression to detect any change in average patient falls per quarter per 1000 patient days. Though there was a change in trend and slope between the pre- and post-intervention periods, the results were not statistically significant. While checking for serial collinearity with the Durbin-Watson test, we detected serial autocorrelation in the order of lag 1, confirmed by the PACF plot. Therefore, we fit the AR (1) model into our data (Table  3 ).

The results from Table 3 , illustrated in Fig.  2 , show that (i) during the pre-intervention period, the patient bed fall rate per quarter per 1000 patient days was 1.42; (ii) during the pre-intervention period, the rate was decreasing by an average of 0.024 per quarter; (iii) there was an increase in level (intercept) at the onset of the intervention by 0.5 bed falls per quarter; and (iv) there was an insignificant incremental change in slope (− 0.14 day/month) between the pre- and post-intervention periods.

figure 2

Interrupted time series analysis of patient bed falls. The vertical line separates the pre- and post-intervention periods. The green line estimates the fall rate if there had been no intervention; the purple line shows the true conditions

The dotted line in Fig. 2 estimates the quarterly bed falls rate without the intervention. Though the rate empirically shows a downward trend following the intervention, we could not statistically demonstrate causation due to the limited sample size.

Hand-hygiene-compliance rate

We examined HH-compliance (handwashing before entering and after exiting a room) rates from September 2014 to October 2016 using only post-intervention data by considering (i) staff type; (ii) room; and (iii) time of day. Our detailed analysis revealed the following:

(i) HH-compliance rate by staff type

Within the timeframe of our analysis, patient care coordinators (PCCs) had the highest average HH-compliance rate (64%), above physicians (54%), registered nurses (45%), registered practical nurses (48%), and patient care assistants (PCAs) (58%). When we studied the entrance and exit HH-compliance rates separately, we found that—across all staff types—compliance rates were higher during exiting than upon entering. Further analysis revealed that—once more, across all staff types—HH-compliance rates increased from 2014 (creation of the MHIU) to the middle of 2015 before decreasing in 2016, likely due to broken sensors and a lack of awareness among nurses. While nurses initially received daily HH-compliance feedback, that practice was discontinued in mid-2015. Figure  3 shows the HH-compliance rates for all staff types.

figure 3

Compliance rate of different staff types

(ii) HH-compliance rate by room type

Later, we conducted a detailed analysis of the HH-compliance rate for each room in the MHIU. Of the 17 rooms, three were used as isolation rooms (Fig.  4 ). We found that both entrance and exit HH-compliance rates were higher for isolation rooms than for any other room type over the total study period (2014–2016). Still, further analysis with monthly data confirmed that the exit HH-compliance rate is higher than the entrance HH-compliance rate across all room types; it also confirmed that there was a decrease in HH compliance after 2015.

figure 4

HH-compliance rate by room

(iii) HH-compliance rate by time of day

We analyzed HH-compliance rates at different times of the day to identify the hours during which HH opportunities were most frequently utilized. There were HH spikes at 5 a.m., 9 a.m., and 11 a.m.; these times coincide with unit rounds and other time-based tasks (Fig.  5 ).

figure 5

HH-compliance rate by time of day

Based on their responses, the nurses largely agree that smart beds have helped to significantly reduce patient falls and enhance patient care on account of the weighing and quick-inclination features. Nurses also mentioned that the dome light indicators were helpful to achieve fewer falls and locate the proper staff type. They emphasized that, after this intervention, there was a smaller number of falls in the unit. For the HH support system, although most of the nurses were unaware that sensors in the HH dispensers monitored their compliance rates, all of them mentioned that the feedback (which was ultimately discontinued) motivated them to keep their hands clean. Despite all of the advantages, however, nurses also noted the complexities they faced. For example, the beds frequently become unplugged while delivering care; while a light turns on if it’s disconnected, they initially needed training on reconnecting and adjusting all of the bed’s many parameters. Regarding the HH support system, they believe that the timely refilling of the dispensers is important, and that dispenser placement and height are two issues that must be considered.

This paper, to our knowledge, constitutes the first empirical study to assess the impact of IoT interventions on indicators of patient safety, such as patient falls and HH compliance, in a hospital setting. This study examined the effect of IoT intervention (smart beds, HH support systems, RFID badges, dome light indicators, wall-mounted call stations, mobile phones, smart stations) at a hospital unit in Ontario, Canada on patient safety. It highlighted several core messages on IoT implementation in healthcare.

Our study found a reduced number of patient bed falls following the implementation of an advanced IoT-based intervention. This aligns with the literature, which largely asserts that IoT reduces patient falls. Like many other studies, we found that fall incidents were higher among male patients in both the pre- (63%) and post-intervention (67%) periods [ 63 , 64 ]. We also observed that most of the patients who experienced falls in the MHIU were over 75 years old in both the pre- and post-intervention periods, meaning that the group is relatively vulnerable to injuries; once more, this aligns with the existing literature [ 65 ]. Studies have shown that while not all patient falls result in serious injury, the complications associated with falls rise steadily alongside age—they are twice as complicated among patients who are more than 75 years old [ 65 ]. Additionally, we found that most of the falls occurred at night (10 p.m. – 12 a.m.); again, this finding was consistent with the findings of most previous studies [ 66 ].

While we did not find statistically significant results in our descriptive analysis or ITS study for patient falls, our trend analysis did reveal that the proportion of bed falls has decreased from 21 to 15% following the intervention. Staff interviews also indicated that IoT implementation supported patient safety, improving the quality of care delivery by reducing falls while satisfying the staff. According to the nurses in the unit, alerts from smart beds and dome light indicators significantly contributed to the decline in the number of falls during the post-intervention period. Additionally, the nurses indicated that bed falls notably decreased following the intervention. Of course, as the results are not statistically significant, we cannot conclude that the IoT intervention is the only reason for this change. However, as most of the falls occur at night among older male patients in this unit, the smart bed’s alert system may have had a great impact on this improvement, as suggested by the literature [ 34 , 35 , 36 , 41 ].

This study found that the average entrance and exit HH-compliance rates in the MHIU were 43 and 54%, respectively, throughout the study period. Interestingly, a similar study found that the HH-compliance rate rose from 67 to 70% following the intervention [ 67 ]. Our study shows a range in HH-compliance rates from 45 to 68% across the various staff types (PCC, PCA, physicians, registered nurses, and registered practical nurses). A similar range—26 to 64%—was found in an existing study [ 68 ]. Further analysis of staff type revealed that PCCs had the highest HH-compliance rate. We also found that the exit HH-compliance rate was uniformly and consistently higher (54%) than the entrance HH-compliance rate (43%); this finding aligns with previous studies [ 69 ]. One potential reason for such a behavior is that the staff may have entered another room immediately exiting another room—meaning they had just cleaned their hands.

Another finding related to hand hygiene was that isolation rooms had the highest HH-compliance rates across all staff types, which—once again—aligns with the existing literature. The average entrance and exit HH-compliance rates for the three isolation rooms in the MHIU over the study period were 47 and 65%, respectively; the same rates for the 14 non-isolation rooms were 44 and 53%. A similar trend was detected in previous studies, where researchers showed that HH activity was 49% more likely in isolation rooms [ 70 ]. This makes sense, as the purpose of isolation rooms is to prevent the transmission of microorganisms to the staff or other patients. Staff members follow a specific protocol while delivering care to isolated patients. As already noted in the literature, this extra protection likely contributed to the relatively high HH-compliance rate [ 70 ].

Another major takeaway from this study is that proper knowledge of all intervention components among the staff is essential for a successful intervention; this knowledge can be obtained through small training sessions and support during the implementation process. This assessment has also been made in other studies [ 71 , 72 ]. This study showed that the considered technologies need to be actively maintained and adjusted to strengthen the intervention’s impact. For example, we observed the exit HH-compliance rate at 52% in 2014, then 65% in 2015 but 44% in 2016. Similarly, the entrance HH-compliance rate went from 35 to 56% and 38% in 2014, 2015, and 2016, respectively. Both HH-compliance rates declined starting in mid-2015, partially due to broken sensors and the end of daily feedback. Both of these explanations behind declining HH-compliance rates align with the literature; one study analyzed 1120 survey responses and found broken sensors to be a core issue hampering HH compliance [ 73 ]. Using feedback to improve HH compliance has been reported numerous times. Researchers note that individual feedback significantly improves HH-compliance rates; one study found that compliance among nurses increased from 43 to 55% following the provision of individual feedback [ 74 ].

Our analysis of the smart beds revealed that a frequent call type that was unrelated to patients was the “bed-disconnected” call. This call alerts caretakers when a bed is unplugged and is very quiet. Therefore, it often goes unnoticed, potentially contributing to patient fall rates.

Limitations

Our study has some important limitations to note. Regarding the clinical endpoints, such as patient falls, our sample size was quite small due to a lack of data availability. Though studies involving ITS have no minimum data-point requirements, the power of the analysis is increased if the data points are equally distributed across the pre- and post-intervention periods—this was not possible in our case [ 61 ]. Our small number of data points skewed toward the post-intervention period may have contributed to the lack of statistical significance. While we observed a declining fall rate and heard comments to that effect from the nurses, we cannot conclude that this stems entirely from the intervention, as we did not consider any other internal (e.g., demographics, clinical information) or external (e.g., time, weather) factors. However, it is worth noting that the reporting of insignificant results is not uncommon in the literature [ 75 ]. Our participant number for the interview was small as well. Considering that this intervention was applied only to one unit, it was not possible to recruit a higher number of staff.

Additionally, we could not compare the post-intervention HH-compliance rates with those of the pre-intervention period, as historical HH-compliance rates were manually audited periodically and were inordinately high. Therefore, in line with the literature, we suspect a relationship between manual periodic audits and high compliance rates [ 76 ].

We have reported compliance rates across different times of day, staff types, and rooms; however, we did not measure whether this HH support system had any impact on the unit’s infection rates due to a lack of data.

We conducted a thorough analysis of the impact of the IoT-based interventions on patient safety and found a positive impact on various aspects of patient safety. Though our study could not find any statistically significant changes in the mean patient fall rates, however qualitative interviews with nurses stated reduced patient falls and improvements in direct care time. This study also studied the HH compliance rates, where an increase in the first year was reported, followed by a decrease in the second year. While this study details promising benefits of IoT in patient safety, further analysis that includes recent data on patient falls, HH compliance, and infection rates would provide further findings.

Availability of data and materials

The authors underwent a vigorous privacy approval from different departments of the hospital to obtain the data. Therefore, it is not publicly available. However, on any reasonable request, the aggregate datasets that have been used during this study can be provided from the corresponding author with approval from the hospital.

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Acknowledgments

The authors express sincere gratitude to Richard Tam, Executive Vice President and Chief Administrative Officer, Mackenzie Health and Diane Salois-Swallow, CIO at Mackenzie Health for providing the opportunity to embark on this project and their continuous support and valuable insights in this evaluation study. The authors are sincerely grateful to the nurses and other staff of the Mackenzie Health Innovation Unit and the technical team of Mackenzie Health for their continuous support and help in this project. The authors are also grateful to Nicole Mittmann, Chief Scientist and Vice-President of Evidence Standards, CADTH, and Assistant Professor, Pharmacology & Toxicology, University of Toronto, Toronto, Canada for her valuable input to the study.

The project was funded by the Ontario Centres of Excellence (OCE).

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TY, MC wrote the proposal and designed this study. TY, MC, AG contributed to the methods and data collection. TY, MC contributed to the analysis. TY, MC, AG drafted and revised the manuscript. All authors have read and approved the final manuscript.

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Yesmin, T., Carter, M.W. & Gladman, A.S. Internet of things in healthcare for patient safety: an empirical study. BMC Health Serv Res 22 , 278 (2022). https://doi.org/10.1186/s12913-022-07620-3

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case study of health care in iot

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  • Published: 11 March 2024

Anomaly detection in IoT-based healthcare: machine learning for enhanced security

  • Maryam Mahsal Khan 1 &
  • Mohammed Alkhathami 2  

Scientific Reports volume  14 , Article number:  5872 ( 2024 ) Cite this article

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  • Computer science
  • Information technology

Internet of Things (IoT) integration in healthcare improves patient care while also making healthcare delivery systems more effective and economical. To fully realize the advantages of IoT in healthcare, it is imperative to overcome issues with data security, interoperability, and ethical considerations. IoT sensors periodically measure the health-related data of the patients and share it with a server for further evaluation. At the server, different machine learning algorithms are applied which help in early diagnosis of diseases and issue alerts in case vital signs are out of the normal range. Different cyber attacks can be launched on IoT devices which can result in compromised security and privacy of applications such as health care. In this paper, we utilize the publicly available Canadian Institute for Cybersecurity (CIC) IoT dataset to model machine learning techniques for efficient detection of anomalous network traffic. The dataset consists of 33 types of IoT attacks which are divided into 7 main categories. In the current study, the dataset is pre-processed, and a balanced representation of classes is used in generating a non-biased supervised (Random Forest, Adaptive Boosting, Logistic Regression, Perceptron, Deep Neural Network) machine learning models. These models are analyzed further by eliminating highly correlated features, reducing dimensionality, minimizing overfitting, and speeding up training times. Random Forest was found to perform optimally across binary and multiclass classification of IoT Attacks with an approximate accuracy of 99.55% under both reduced and all feature space. This improvement was complimented by a reduction in computational response time which is essential for real-time attack detection and response.

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Introduction

The Internet of Things (IoT) is a major technology that is the basis of several upcoming applications in the areas of health care, smart manufacturing, and transportation systems. IoT relies on the use of various sensors to gather information about humans, devices, and the surrounding environment. This information is passed to the cloud server regularly and as a result, application administrators can make various decisions to improve the efficiency of applications. Similarly, AI techniques can be utilized to automatically control the applications based on the collected data 1 .

Healthcare is one major application of IoT where patients are provided with wearable devices to collect data related to body vitals. Examples of such data could be body measurements such as oxygen level, blood pressure, sugar level, heart rate, etc. Without using IoT, these vital measurements can not be recorded continuously and sent to the cloud for processing. Thus, IoT-enabled health care is an important use case with a huge impact on human lives.

Since IoT-enabled health care involves the recording and sharing of critical data that is linked to human safety, it is vital to design efficient techniques to make sure that the data recording and sharing are reliable and secure. Healthcare systems can be subject to several security attacks that can lead to a loss of confidence in received data. In several cases, wrong decisions can be made on the malicious data, thus leading to the collapse of IoT-enabled healthcare applications.

There are several types of security attacks in healthcare systems such as Denial of Service (DoS) attack in which malicious users aims to deny the wearable or to share data with the cloud. This can be achieved by sharing incorrect data with high frequency towards the wearable or, thus blocking its access to the wireless medium. Similarly, spoofing is another common cyber attack in which malicious users hide their identity to get access to the critical health-related data of patients. Another example of a cyber attack is a brute force attack that tries to crack the password of users’ wearable devices and gain access to the sensor’s data. In addition, there are many other attacks such as data integrity and eavesdropping that can reduce the reliability of IoT health care applications.

This paper focuses on developing anomaly detection techniques for IoT attacks using the publicly available dataset. Following are the major contributions of the paper.

The authors in 2 have applied Machine Learning (ML) algorithms in an imbalanced dataset, producing models with high accuracy and low precision scores. The research motivation is to balance the dataset and train ML algorithms accordingly.

To evaluate supervised machine learning algorithms across inary (2-Class) and multiclass (8 and 34-Class) representations on the balanced dataset.

To evaluate the computational response time of machine learning models via feature reduction.

To determine which features are essential for the generalization of machine learning models.

The paper is organized as follows. " Literature review " Section describes the literature review and recent work done in the area of IoT security and anomaly detection and briefly describes the ML algorithms used in the study and how they are evaluated. The problem of an imbalanced dataset and the strategy to resolve it through oversampling techniques is also included in this section. " Methodology " section describes the system model and utilized IoT attack dataset including the methodology and anomaly detection framework of the current study. The result and discussion are presented in " Results and discussion " section. Finally, conclusions are described in " Conclusion " section.

Literature review

In this section, we present an overview of different intrusion and cyber-attack detection techniques in an IoT network and provide a brief description of different datasets that are used to analyze these attacks. The section also provides information on the Machine learning (ML) algorithm used in the study along with the standard performance metrics used for the evaluation of the ML models. Finally, the section describes the problem with ML models trained on imbalanced datasets and strategies to overcome them.

Review of different intrusion detection techniques

Table  1 lists different intrusion detection techniques focused on IoT networks. In 3 , authors utilize Deep Neural Network (DNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) techniques to identify the abnormalities in the data. A key feature of the proposed technique is the use of the Incremental Principal Component Analysis (IPCA) technique for reducing the features in the dataset. The proposed technique also uses dynamic quantization for efficient data analysis. The work achieves improved accuracy of intrusion detection and reduced complexity of the model.

The work in 4 is focused on efficient cyber attack detection. The main idea of the proposal is to use federated learning for improved privacy and distributed model development. The proposed technique uses a Deep Neural Network (DDN) for attack detection. The work also contributed towards reducing the features and balancing of the data. Results show that the proposed technique improves the accuracy of attack detection as well as the privacy of the system.

In 5 , another intrusion detection for IoT networks is proposed. The focus of the work is on two key factors, one is removing the redundancy in dataset features, and the second is mitigating the imbalance in the dataset. By using these two factors, the proposed technique improves the F1 score of intrusion detection.

The work in 6 proposes a cyber-attack detection mechanism. The class imbalance problem is handled by the proposed technique. Authors apply DNN on the balanced dataset to perform training and testing. A bagging classifier mechanism is used to improve the performance of the system. The proposed technique achieves improved accuracy and precision.

In 7 develops an adaptive recommendation system to improve the efficiency of intrusion detection. The main feature of the proposed technique is the development of a self-improving mechanism that autonomously learns the intrusion knowledge. A pseudo-label-based voting system is also used in the proposed technique, thus resulting in improved intrusion detection performance.

The work in 8 develops an explainable AI-based intrusion detection system. Authors utilize the DNN technique in conjunction with explainable AI mechanisms such as RuleFit and Shapley Additive Explanation. Results show that the developed model is simple and easier to understand while providing improved efficiency.

Cyber attack and intrusion detection data sets in IoT

There are various publicly available data sets related to cyber attacks and intrusion detection in IoT as shown in Table  2 . In 9 , the CIC IDS 2017 attack data set is provided by the Canadian Institute of Cyber Security. A 5-day network traffic data was collected using CIC Flow meter software. The data included normal traffic as well as different types of attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS), Brute Force, Cross-Site Scripting (XSS), Structured Query Language (SQL) injection, Infiltration, Port Scan, and Botnet.

The N-BaIoT data set in 10 was collected by the University of California, Irvine. Nine Linux-based IoT machines were used to generate traffic. Two IoT Botnets were used, one was BASHLITE and the other was Mirai. The generated security attacks included Acknowledgement (ACK), Scan, Synchronize (SYN), and User Datagram Protocol (UDP) flooding.

In 2 , the CICIoT data set was provided by the Canadian Institute of Cyber Security. 105 IoT machines were used to generate diverse security attacks. The generated attacks were divided into 33 attacks and 7 major categories.

The NSL-KDD data set 11 was provided by Tavallaee et al. The data set is an improved version of the KDD data set and removes duplicate entries. The attacks included in the data set are DoS, User to Root, Root to Local, and Probing.

In 12 , the UNSW_NB-15 data set was provided by the University of New South Wales. A synthetic attack environment was created including normal traffic and synthetic abnormal traffic. Several attacks were generated including Fuzzers, Analysis, Backdoors, etc.

Another data set named BoT-IoT was generated by the University of New South Wales 13 . This data set was based on a realistic environment of traffic containing both normal as well as Botnet traffic. The attack traffic included DoS, DDoS, Operating System (OS), Service scan, keylogging, and data exfiltration.

Motivation to use CICIoT 2023 dataset

The author 2 introduced the CICIoT2023 dataset, which is composed of thirty-three different attacks (categorized into seven classes) executed against 105 IoT devices with well-documented processes defined. So far, the study provides a comprehensive and wide variety of attack types as compared to other reported in literature. Moreover, the main motivation of using the CICIoT2023 dataset is that it has been released recently and there exist only one publication using the dataset. In 3 only two attacks (Mirai, DDoS) were focused on the study. There exists no article on the use of various intelligent machine learning models in identification of all types of malicious anomalous IoT attacks namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. The present study hence contributes to this direction.

Machine learning algorithms

There exist numerous supervised, unsupervised, and reinforcement-based machine learning algorithms. The research study only investigates the application of supervised ML algorithms in IoT attack detection. The performance of five ML algorithms is tested in the present research work and a brief description of these algorithms is provided herewith.

Random forest (RF): Multiple decision trees are combined in the ensemble learning technique known as RF. For the classification task, the RF’s output is the statistical mode while for the regression task, average of the predictions made by each tree. Applications for RFs are numerous and include image analysis, finance, and healthcare. Their usefulness, usability, and capacity to manage high-dimensional data are well-known attributes.

Logistic regression (LR): It is the type of regression that determines the likelihood that an event will occur and is used for classification. Statistics is used to predict a data value given the previous observations of a data set. The output is discrete. LR operates on a logistic sigmoid function, which accepts any real input and outputs an integer between zero and one.

Perceptron (PER): As a linear classifier, the PER performs best in situations when there is a linear separation of the classes. It uses the perceptron learning rule to update its weights and makes adjustments in response to the misclassifications. Simple and effective, the scikit-learn Perceptron class may not converge on datasets that are not linearly separable. Under such circumstances, more sophisticated algorithms, like support vector machines or neural networks should be used.

Deep neural network (DNN): An artificial neural network with several layers between the input and output layers is called a Deep Neural Network (DNN). Deep learning models are a subclass of neural networks distinguished by their capacity to acquire intricate hierarchical data representations. A deep neural network’s layers are made up of linked nodes or neurons, and these layers are generally divided into three categories: input layer, hidden layer, and output layer. Key characteristics of a DNN include the use of non-linear activation function, deep architectures, and backpropagation algorithm for training weights of the network for locating an optimal solution.

Adaptive boosting (AB): AB creates a powerful classifier by combining several weak classifiers. Training instances are given weights by the algorithm, which then iteratively updates them. A weighted sum of the individual weak classifiers yields the final prediction.

Machine learning performance metrics

In machine learning classification problems, several performance metrics are commonly used to evaluate the performance of a model. These metrics include accuracy, precision, recall, and F1-score, each of which measures different aspects of classification performance.

Accuracy : Accuracy measures how accurately a classification model is applied overall. It determines the proportion of accurately predicted occurrences to all of the dataset’s instances and is mathematically computed using Eq. ( 1 ), where

TP (True Positives) is the number of correctly predicted positive instances.

TN (True Negatives) is the number of correctly predicted negative instances.

FP (False Positives) is the number of instances that were actually negative but were incorrectly predicted as positive.

FN (False Negatives) is the number of instances that were positive but were incorrectly predicted as negative.

Precision : Precision measures the accuracy of positive predictions made by the model. It calculates the ratio of true positives to the total number of positive predictions expressed in Eq. ( 2 ).

Recall : Recall measures the ability of the model to correctly identify positive instances. It calculates the ratio of true positives to the total number of actual positive instances, expressed in Eq. ( 3 ).

F1-score : The F1-Score is the harmonic mean of precision and recall. It provides a balance between precision and recall and is particularly useful when dealing with imbalanced datasets, expressed in Eq. ( 4 )

Imbalanced datasets

An imbalanced dataset has a distribution of classes (categories or labels) that is severely skewed, indicating that one class has significantly more samples or instances than the other(s). The occurrence of the dataset is frequently seen in machine learning. In binary classification problems, one class is the majority class and the other is the minority class while in multiclass classification, class imbalance can arise when one or more classes have disproportionately fewer samples than the others. In applications where the minority class is of great importance, such as fraud detection, medical diagnosis, and rare event prediction, addressing class imbalance is essential for reliable predictions. Two major concerns in using ML on an imbalance dataset includes 14 , 15 :

Biased model training : Machine learning algorithms are often biased in favor of the dominant class when one class outweighs the others significantly. The model may prioritize correctly predicting the majority class while ignoring the minority class because its goal is frequently to minimize the overall error. The model may have trouble making precise predictions for the minority class based on unobserved data because it hasn’t seen enough examples from that group resulting in poor generalization of the problem.

Misleading evaluation metrics : In unbalanced datasets, standard accuracy becomes a misleading statistic. Even if a model that predicts the majority class in every instance can still be highly accurate. The sensitivity (true positive rate) of the model for the minority class is fairly low in unbalanced datasets. This indicates that a large number of false negatives could result from the model missing a significant number of cases from the minority class.

Several tactics and strategies can be used to reduce the problems caused by class imbalance. These include resampling techniques such as oversampling of minority class and under-sampling majority class 16 ; synthetic data generation techniques like SMOTE 17 , Adaptive Synthetic Sampling(ADASYN) 1 , cluster-based techniques 18 to name a few. The authors in 2 have applied Machine Learning (ML) algorithms in an imbalanced dataset, producing models with high accuracy and low precision scores. The motivation of this research is to balance the dataset and then apply the ML algorithms to generate generalized models with marked improvements in the evaluation metrics.

Synthetic minority over-sampling technique: balanced dataset generation

Synthetic Minority Over-sampling Technique (SMOTE), is a well-known pre-processing approach in the area of machine learning and data preparation that deals with the issue of class imbalance in classification problems. Class imbalance happens when one class in a binary or multi-class classification problem has significantly fewer samples than the other(s), resulting in an inaccurate model that tends to bias the dominant class. To address this problem, Chawla et al. developed the SMOTE algorithm in 2002 17 . It balances the class distribution by creating artificial examples of the minority class, which improves the learning algorithm’s performance and lowers the likelihood of a biased model. Mathematically expressed as in Eq. ( 5 ).

where, x is the original minority class instance. neighbor is one of the k nearest neighbors of x within the minority class. \(\lambda \) is a random value between 0 and 1, controlling the amount of interpolation.

The SMOTE method has multiple versions, each with unique adjustments to handle various facets of the class imbalance issue. A few variations of the SMOTE algorithm include e.g. Borderline-Smote which applies SMOTE to instances near the decision boundary 19 ; ADASYN that generates samples based on the local density of the minority class 1 ; SMOTE-with Edited Nearest Neighbour(ENN) which removes noisy samples using ENN 20 , 21 ; SMOTE-Tomek Links combines SMOTE with Tomek Links undersampling technique to remove noisy samples 22 ; SMOTE-Boost that combines SMOTE with AdaBoost ensemble method to oversample minority class in each iteration of AdaBoost 23 for improving performance. Different versions of the SMOTE algorithm provide different strategies for increasing minority class samples and reducing noisy data. In the current research study, the conventional SMOTE algorithm is used as a starting point to observe the change in performance metrics after applying the SMOTE algorithm to the CICIoT dataset.

Methodology

Ciciot2023 dataset.

In the current research study, we use the publicly available IoT attack dataset namely CICIoT2023 2 . The dataset was created to encourage the creation of security analytics applications for use in actual IoT operations. The authors executed 33 different attacks in an IoT topology of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. The dataset consists of 169 files in two different file formats PCAP and CSV. The CSV files are PCAP-processed files generating 46 attributes that indicate the different types of attacks. The number of recorded samples per category is not uniform, whereas Web-Based and Brute-Force have far-low representation—a classic sign of an imbalanced dataset. Figure  1 displays the research study’s workflow. The dataset is pre-processed and balanced to ensure credibility in the evaluation of the machine learning models. The data features are further reduced, to improve predictive performance and training times of the ML models across both binary and multiclass representation of the dataset. Further explanation is ahead. The algorithm of the methodology is shown in 1 .

figure 1

Methodology of the research work applied on the CCIoT2023 Dataset.

figure a

Performance of ML algorithms on balanced representation of CCIoT2023 dataset.

Dataset preprocessing

Data cleaning is a crucial step in the ML pipeline. Data cleaning includes handling missing or noisy data or dealing with outliers or duplicates. The dataset consists of 33 different classes of IoT attacks with forty-six numerical features. Features with no variation across the thirty-four classes are removed from the dataset. Hence out of 46 features, 40 features are processed ahead. These features are normalized using a standard scalar method which is a common requirement for many machine learning algorithms.

Feature scaling is particularly important for algorithms that use distance-based metrics, as differences in scale can disproportionately impact the influence of certain features on the model. This pre-processing step helps in improving the performance and convergence of ML algorithms. There are two methods of scaling the features in a dataset (1) Normalization (2) Standardization. Normalization is the process of scaling the features within a certain range e.g. [0–1] and standardization is the process of scaling features to a mean of zero and standard deviation of 1. Many of the ML algorithms including linear regression and Neural networks converge faster in the standardized feature space. In the current study, the forty features obtained after cleaning are normalized using a standard scalar method.

Data balancing

This is the important block of the methodology and requires balancing the dataset using either random undersampling or oversampling via the conventional SMOTE algorithm, described in " Synthetic minority over‑sampling technique: balanced dataset generation " section. The process of dataset generation for binary and multiclass classification is explained below.

2-Class representation : In this scenario, the thirty-three malicious classes are labeled as one category ‘Attack’. Approximately 50% of the data, which captures the different types of malicious representations, from each of the 169 CSV files is randomly extracted and a balanced data set is created. No SMOTE algorithm is used in this particular scenario. The total number of samples per class in the integrated dataset was 8450.

8-Class representation : The data samples from all the different type of attacks i.e. 34 subcategories has been used in the construction of the 8 Class dataset. The process of random undersampling in the majority class and SMOTE-based upsampling of the minority class is executed to produce a uniform representation of the dataset samples. The total number of samples per class in the integrated dataset was 33,800.

34-Class representation : For the 34 classes in the CICIoT dataset, it has been found that two classes namely BruetForce and Web-based have less representative samples in the dataset. The process of random undersampling in the majority class and SMOTE-based upsampling of the minority class is executed to produce a uniform representation of the dataset samples. The total number of samples per class in the integrated dataset was 84,500.

The IoT topology deployed to produce the CICIoT2023 dataset comprises 105 IoT devices. 33 different types of IoT attacks were modeled. In the dataset, the number of rows captured per attack is not uniform, e.g. the attack type DDoS-ICMP Flood contains 7,200,504 data rows representing a majority class whereas WebBased-Uploading Attack is a minority class with 1252 data rows. Applying ML algorithm directly on an imbalanced dataset with non-uniform data-rows across the different attack classes would impact the generalization and performance of a ML model e.g. the authors in 2 have produced models with high accuracy and low precision scores. Hence, the main motivation and contribution of this research is to balance the dataset and generate ML models that are unbiased with non-misleading evaluation metrics.

Feature reduction

For feature engineering, model selection, and general data analysis in machine learning, the Pearson correlation coefficient (PCC) is significant since it offers a clear indicator of the relationship between variables. PCC facilitates the creation of more accurate predictive and descriptive models by assisting in the decision-making process over which variables to include in models and how they interact. Many applications have been devised where eliminating highly correlated features has reduced model complexity without compromising the predictive performance. The formula for calculating the Pearson correlation coefficient r between two variables, X and Y, with n data points, is given shown in Eq. ( 6 ).

where \(X_i\) and \(Y_i\) are the individual data points for variables X and Y respectively and \(\bar{X}\) and \(\bar{Y}\) are the means of variables X and Y respectively.

As mentioned, the pre-processed dataset consists of forty features. The PCC of the forty characteristics is calculated, and Fig.  2 a shows the absolute correlation coefficient heat map. Darker shades in the figure display highly correlated features. A PCC value of 0.9 or higher, in the current study, is regarded as a highly correlated feature, and it is eliminated from the feature collection. Hence a total of thirty-one features are analyzed in the reduced feature space. Referred to Fig.  2 b, a heat map of the reduced feature set and related PCC values is displayed.

figure 2

Heat map plot of ( a ) Pearson Correlation Coefficient of forty features in the CCIoT2023 dataset, ( b ) thirty-one Pearson Correlation Coefficients after removing high correlated features, absolute Correlation threshold set to 0.9. Darker shades represent a high absolute correlation coefficient.

Model generation and evaluation

Any binary or multiclass classification problem is modeled through the application of supervised machine learning algorithms. Five popular and powerful supervised ML algorithms (Random forest RF , Adaptive Boosting AB , Logistic Regression LR , Perceptron PER and Deep Neural Network DNN ); are studied on the balanced dataset with both full features and reduced feature set respectively. The datasets are split into 80% training and 20% testing as followed in the research study 2 for a fair comparison. Standard performance metrics for evaluating supervised algorithms, discussed in " Machine learning performance metrics " section, are computed and reported in Table  3 for 2-Class, 8-Class, and 34-Class respectively.

Results and discussion

Table  3 , shows the performance of ML algorithms on the balanced dataset across three defined classification scenarios i.e. 2-Class, 8-Class, and 34-Class. The ML models generated are evaluated based on Accuracy, Precision, Recall, and F1-Score details which have been explained in " Machine learning performance metrics " section. Overall, RF has been found to perform better than other ML models across the different scenarios. In the 2-Class task, all of the ML models perform with an accuracy of \(\ge 98\%\) , while it decreases with increasing complexity of the problem i.e. 8-Class and 34-Class label identification. There is a slight improvement in accuracy for the ML models trained in the reduced feature e.g. 0.06% in RF and DNN models. With balanced dataset representation across the three classification tasks, improvement in precision, recall, and f1-score from the ones reported in literature 2 is obtained.

To visualize the performance of the RF models across the different class categories, confusion matrices are observed. In Figure  3 , for the binary classification problem, out of the 1690 test samples per category i.e. benign or attack, benign prediction is found to be more accurate than the attack ones in both scenarios. This might be attributed to the fact that the 33 variations of attack are labeled as one category. The f1-score of the RF-model is found to slightly improve in the reduced feature space i.e. from 99.49 to 99.55% respectively.

figure 3

Confusion matrices of RF models on a binary classification task i.e. Attack versus benign, using the CICIoT2023 dataset across ( a ) all features and ( b ) reduced features, respectively.

Figure  4 shows the confusion matrices of the multi-classification eight-class problem where 33,800 samples per category were tested by the RF model under both scenarios. Two attack categories in particular Recon and Spoofing were found to be poorly recognizable (with an f1-score of 90%) by the RF models despite being trained on real samples. SMOTE-based synthetic samples generated for BruteForce and Web were found to be in good agreement with the original training samples. Further analysis is required to understand Spoofing and Recon attack characteristics.

figure 4

Confusion matrices of trained RF models on a multiclass classification task with 8-class labels, using the CICIoT2023 dataset across ( a ) all features and ( b ) reduced features, respectively.

In the multi-classification 34-class problem, 16,900 samples per category were tested. Confusion matrices for the RF models under both scenarios (all features and reduced features) are shown in Fig.  5 . In the test set, 16,900 samples per category were tested on the trained model. 31 of the classes produced an f1-score greater than 85% while three classes, DNS-Spoofing , Recon-PortScan and Recon-OSScan had an f1-score of 83%, 82% and 79%. These subclasses belong to Recon and Spoofing IoT attack category, which was also found harder to classify than other class labels in the 8-Class task.

figure 5

Confusion Matrices of trained RF models on a multiclass classification task with 34-class labels, using the CICIoT2023 dataset across ( a ) all features and ( b ) reduced features, respectively.

An additional tool for comprehending important characteristics in the dataset is a feature importance graph, which is produced through RF models. The feature significance graph from the RF models for the three classification tasks is displayed in Fig.  6 , where (a) shows the RF models when all features are used and (b) shows the RF models when a reduced feature set is used. The top features identified in the binary classification tasks under both scenarios were \(urg_{count}\) and AVG . \(urg_{count}\) is the number of packets with urg flag set and AVG represents the average packet length. For both of the multi-classification tasks, IAT was found to be the top feature. IAT measures the time difference between the current and the previous packet. The statistical measurements e.g. Header Length, Min, Max, Average, covering the right side of the feature graph in Fig.  6 were more frequently chosen than the other features.

figure 6

Feature significance graphs, extracted from the RF models across ( a ) all features and ( b ) reduced features in the CCIoT2023 Dataset for 2-Class, 8-Class and 34-Class classification tasks.

Figure  7 a, c and e displays the training time in seconds and Fig.  7 b, d and f shows the testing time in seconds of the ML algorithms on all and reduced feature sets for 2-Class Fig.  7 a and b, 8-Class Fig.  7 b and c and 34-Class classification Fig.  7 e and f tasks respectively. As the feature set is reduced, we can see a reduction in the training time of all the models. For the DNN model performance in 2-Class classification, Fig.  7 a and b, training time across all features was approximately 8.6s while in the reduced space it was 6.6s respectively. Similarly, as the feature set is reduced in almost all cases there is a reduction in response time of the models. For the RF model in 8-Class classification, Fig.  7 d, testing time across all features was approximately 13.08 s while in the reduced feature space was 6.64 s secs respectively. All these steps are carried out in the development environment with Intel Core i7 7820HQ-processor, 32 GB DDR4 RAM, and Windows 10 operating system.

figure 7

Time is taken, in seconds, to train and test supervised ML algorithms, with and without feature reduction. The figure shows training and testing time for ( a ,  b ) 2-Class, ( c ,  d ) 8-Class, and ( e ,  f ) 34-Class multiclassification tasks, respectively.

The CICIoT2023 dataset has been recently released and there exists not much literature using the dataset. The reported best models in the study are compared with the best models produced by the authors in 2 and are shown in Table  4 . The optimum performing model metrics are highlighted in bold. The results of the existing study have performed better than the ones reported. The dataset originally was imbalanced hence models generated have low recall values. Recall values can be seen improved due to balancing the data samples across the different classification tasks.

The use of Medical Internet of Things (IoT) devices in healthcare settings has made automation and monitoring possible e.g. in enhanced patient care and remote patient monitoring. However, it has also introduced a host of security vulnerabilities and risks including identity theft, unauthorized alteration of medical records, and even life-threatening situations. Furthermore, it is becoming more challenging to secure each device entry point in real-time due to the growing usage of networked devices.

Machine learning has the potential to detect and respond to attacks in real-time by identifying anomalies in the data captured by IoT devices. The current study explored the potential of supervised machine learning algorithms in identifying anomalous behavior on a recently published dataset, CCIoT2023. The dataset consists of 33 different categories of IoT attacks represented by 46 features, with a varying number of data samples. The dataset is imbalanced, i.e., it has a non-uniform sample distribution. The study explored improving machine learning models by employing a balanced approach to data distribution using the SMOTE algorithm. Classification models for three strategies of ‘IoT Attack’, two-class, eight-class, and thirty-four class, were investigated. Random Forest was found to excel in all three defined classification problems and performed better than what has been reported so far in the literature. Eliminating strongly correlated features slightly improved the performance of the model but reduced computational response time and enabled real-time detection.

The feature importance graph depicted \(urg_{count}\) -number of urg flags in the packet and AVG -average packet length in 2-Class and IAT – time difference between packet arrival time, as an important feature in discriminating various attack categories in multiclassification problem. Moreover, certain IoT attacks e.g. Spoofing and Recon require further analysis and feature expansion to be able to discriminate these classes and their corresponding sub-classes further.

Data availability

Details of data is available in the paper.

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The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research through the project number IFP-IMSIU-2023046. The authors also appreciate the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) for supporting and supervising this project.

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case study of health care in iot

IoT in Healthcare: Benefits, Use Cases, Challenges, and Future

  • IoT in Healthcare:...

Internet of Things for Healthcare

How iot works in healthcare, iot devices in healthcare, benefits of iot in healthcare., challenges of iot in healthcare., why the internet of medical things is the future of healthcare , future of iot in healthcare.

Healthcare IT solutions will be a greater priority among IoT service providers once the disruptions caused by the COVID-19 slow down. Although remote sensing medical devices have existed for over more than two decades, and telemedicine has already been around for a while, the underlying technology has evolved 100x through the years. No more doctor visits. Today we have an interconnected network of intelligent devices capable of making decisions, work as groups, and send information to the cloud — Internet of Things. 

iot applications in healthcare

In this article, we will take a closer look at Internet of Things (IoT) for Healthcare. 

  • Internet of Things for Healthcare.

What exactly is IoT and why it is important in healthcare? 

  • IoT in healthcare examples .
  • IoT Devices in Healthcare.
  • Future of IoT in Healthcare.

Let's get to business.

The market of IoT in healthcare is predicted to exceed $10 billion by 2024, according to a Brandessence market research . This growth forecast is also impacted by other important technologies. IoT is slowly getting traction and evolving alongside the new ultra-fast 5G mobile wireless, Artificial Intelligence (AI), and Big Data. Combing this powerful technologies with the Internet of Things will likely revolutionize the healthcare industry. IoT in healthcare using 5G wireless and AI could, for example, completely transform the way patients are monitored and treated remotely.

Still, IoT will not only help with the patient’s health, but also improve the productivity of the healthcare industry workers. 

iot in healthcare industry

In a nutshell, IoT is the concept created around the idea of full ubiquitous computing, which is the processing of information linked with external activity or objects. Ubiquitous computing involves connecting electronic devices with microprocessors and sensors to talk to each other. IoT is a ubiquitous network except that all of those electronic devices have access to the Internet.  

IoT in the healthcare industry is a great example of this omnipresent computing. For example, hundreds of intelligent electronic devices can be set up in a hospital to monitor patients’ health status 24/7, talk to each other, make decisions, and upload information to a healthcare cloud platform.

IoT in Healthcare Examples

How IoT can be used in healthcare effectively? Let’s explore three workable Internet of Things healthcare examples below. 

  • Sensing and uploading up-to-date patient information to the cloud in emergency situations, from the ambulance or even from home. 
  • Medical devices capable of performing self-maintenance. IoT healthcare devices will sense their own components, detect low thresholds, and communicate with medical personnel and manufacturers.  
  • IoT and wearables can help home patients and elderly communicate directly with a healthcare facility.  
  • Telemedicine can be considered a “primitive” form of an Internet of Things in healthcare example. With IoT, a patient can be observed and in some cases treated remotely through video cameras and other electronic actuators. 

iot in healthcare examples

To understand how the Internet of Things in Healthcare works, let’s see how IoT works in general. As discussed above, an IoT unit can be considered as a device with a sensor that can interact with the physical world and send information to the Internet. 

In healthcare, these devices can gather different patient data and receive inputs from health practitioners. An Internet of Things Healthcare example is continuous glucose monitoring for insulin pens that works effectively for patients with diabetes. 

All these devices are able to communicate with each other and in some cases take important actions that would provide timely help or even save a life. For example, an IoT healthcare device can make intelligent decisions like calling the healthcare facility if an elderly person has fallen down. After collecting passive data, an IoT healthcare device would send this critical information to the cloud so that doctors can act upon it — view the general patient status, see if calling an ambulance is necessary, what type of help is required, and so on. 

Thus, Internet of Things Healthcare can greatly improve not only a patient’s health and help in critical situations, but also the productivity of health employees and hospital workflows. 

How IoT helps in healthcare — Process 

Let’s explore an IoT healthcare workflow example:

  • A sensor collects data from a patient or a doctor/nurse inputs data. 
  • An IoT device analyzes the collected data with the help of AI-driven algorithms like machine learning (ML). 
  • The device makes a decision whether to act or send the information to the cloud. 
  • Doctors, health practitioners, or even robots are enabled to make actionable and informed decisions based on the data provided by the IoT device. 

Although not all IoT devices should have a sensor, they at least need to have a radio and a given TCP/IP address to enable communication with the Internet. As long as a device has access to the Internet, it can be considered an IoT device. 

iot devices in healthcare

So, every smartphone is an IoT device. A smartphone with the right set of healthcare apps can help you detect diseases and improve your health. Some examples of these are skin cancer detection apps that use your camera and AI-driven algorithms to map moles on your skin. Other examples would be sleep, yoga, fitness, and pill management apps. 

Still, smartphone is a smartphone. Monitoring healthcare is not its primary application. A dedicated healthcare IoT device can do significantly more.  

  • Smartwatch. Wearables sold at consumer electronics stores come with a sensor and Internet connection. Some of them (like iWatch Series 4) can even monitor your heart rate, control diabetes, help in speech treatment, aid in improving posture, and detect seizures. 
  • Insulin Pens and Smart CGM (Continuous Glucose Monitoring). These devices can monitor blood glucose levels and send the data to a dedicated smartphone app. Patients with diabetes can use these devices to track their glucose levels and even send this data to a healthcare facility. 
  • Brain Swelling Sensors. These tiny sensors are implanted within the cranium to help brain surgeons keep track of severe brain injuries and avoid further deathly swelling. They measure pressure on the brain and are able to dissolve by itself in the body without further medical interference.
  • Ingestible Sensors. Prescribed medication is swallowed with a tiny digestible medical sensor that sends a small signal to a wearable receiver on the patient, which, in turn, sends data to  a dedicated smartphone app. This sensor can help doctors ensure patients take their medication at all times. 
  • Smart video pills. A smart pill can travel through a patient’s intestinal tract and take pictures as it travels. It can then send the collected information to a wearable device, which in turn would send it to a dedicated smartphone app (or straight to the app). Smart pills can also help visualize the gastrointestinal tract and colon remotely. 

IoT in the healthcare industry has countless benefits. However, the most important is that treatment outcomes can be significantly improved or maximized, as the data gathered by IoT healthcare devices is highly accurate, enabling informed decisions. 

Health facilities and practitioners will be capable of minimizing errors because all patient information can be measured quickly and sent to a board of doctors or a healthcare cloud platform. AI-driven algorithms running on these IoT devices could also help make intelligible decisions or suggestions based on existing data.

how iot helps in healthcare

Another great benefit of IoT in healthcare is reduced costs. With IoT in healthcare , non-critical patients will be able to stay at home while various IoT devices monitor and send all important information to the health facility — meaning less hospital stays and doctor visits.

With detailed information received from lots of IoT devices, health facilities will also be able to improve their disease management. They’ll have more data in real-time coming in than ever before. Still, this entails a number of challenges.

Although IoT in healthcare provides many great benefits, there are also some challenges that need to be solved. The Internet of Things Healthcare solutions cannot be considered for implementation without acknowledging these challenges. 

  • Massive inputs of generated data. Having thousands of devices in a single healthcare facility and a thousand more sending information from remote locations — all in real-time — will generate huge amounts of data. The data generated from IoT in healthcare will likely make storage requirements grow much higher, from Terabytes to Petabytes. If used properly, AI-driven algorithms and cloud can help make sense of and organize this data, but this approach needs time to mature. So, creating a large-scale IoT healthcare solution will take a lot of time and effort.
  • IoT devices will increase the attack surface. IoT healthcare bring numerous benefits to the industry, but they also create numerous vulnerable security spots. Hackers could log into medical devices connected to the Internet and steal the information —  or even modify it. They can also take a step further and hack an entire hospital network, infecting the IoT devices with the infamous Ransomware virus. That means the hackers will hold patients and their heart-rate monitors, blood pressure readers, and brain scanners as hostages.
  • Existing software infrastructure is obsolete. IT infrastructures in many hospitals are obsolete. They will not allow for proper integration of IoT devices. Therefore, healthcare facilities will need to revamp their IT processes and use new, more modern software. They will also need to take advantage of virtualization (technologies like SDN and NFV), and ultra-fast wireless and mobile networks like Advanced LTE or 5G. 

healthcare and internet of things

IoT in healthcare industry can improve components, such as medical gadgets or services. It can also enhance healthcare applications, such as telemedicine, patient monitoring, medication management, imaging, and overall workflows in hospitals.It can also create new ways of treating different diseases. 

The Internet of Things for healthcare will not only be used by hospitals or facilities, but also by surgical centers, research organizations, and even governmental institutions. 

IoT in healthcare industry does not stand alone. All IoT devices and their networks need to be combined with other technologies to help healthcare facilities transform in a meaningful way. As mentioned before, IoT will revolutionize the healthcare industry but it also needs data, high-speed communication, and proper security and compliance. 

5G will provide the ultra-low latency speeds and mobility that the IoT in the healthcare industry needs. In turn, AI-driven solutions will make sense of the data lakes gathered from a collection of devices. Big Data strategies will use such AI algorithms to analyze data in real-time and make critical health decisions. Virtualization will help to reduce or get rid of old infrastructure in hospitals. 

IoT along with medical ERP software help healthcare evolve, and this evolution will only continue. Sooner than later, healthcare and Internet of Things will become inseparable, completely transforming how we approach our healthcare.

We, at Intellectsoft, empower companies and their healthcare workforce with cutting-edge transformative solutions and data-driven insights. Are you and your organization ready to shift the mindsets and get the most out of innovations? Talk to our experts and find out more about the topic and how your business or project can start benefiting from it today!

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7 Amazing Use Cases of IoT in Healthcare

7 Amazing Use Cases of IoT in Healthcare

Table of content:, introduction.

When we think of IoT in healthcare, we mainly tend to think of smart sensors and smart hospitals. But the solutions go beyond these. It is evident from the fact that spending on IoT solutions in healthcare will reach $1 trillion by 2025!

IoT promises to help healthcare organizations in providing personalized, accessible, and up-to-the-point healthcare services at a lower cost. From remote health monitoring to transmitting real-time alerts, there are several areas where healthcare IoT finds its use.

7 Exciting IoT Use Cases in Healthcare

Let’s look at some of them to get a better perspective:

Remote Patient Care

In many parts of the world, residents live miles away from the nearest hospital. As such, when there is an emergency, it takes time for them to reach the healthcare facilities. Similarly, for healthcare providers, it becomes difficult to visit patients with chronic conditions frequently. The issue with time-consuming commute can be solved with remote patient care powered by the IoT.

The connectivity can allow healthcare professionals to assist patients with prescriptions, medication, and also measure their biometrics using sensors and remote equipment. For instance, patients can connect any wearable or portable device to the cloud and update the data in real-time.

Some of the IoT devices can also facilitate face-to-face talk over the internet. This can provide healthcare professionals with the necessary information to prepare care plans while the patients are on their way to the hospital. Or even without them needing to visit the hospital in the first place! For chronic patients, this helps create a roster of the patients’ day-to-date health update.

The collected data can form charts and diagrams to be easily visualized by healthcare professionals.

Live video and audio streaming can be used to monitor patients’ present condition without the need for the commute.

Emergency Care

Emergency care outputs are based on the time, accuracy, and the availability of contextual information. Moreover, it also depends on the quality of the data received during the emergency call and the information collected while the patient is being transported for immediate care at the healthcare facilities. Also, the entire process of collecting, storing, processing, and retrieving the data during that time is laborious and time-consuming. IoT can help in collecting data accurately, which can be accessed by emergency care staff such as paramedics or staff in the ER for quick and better medical assistance. This data can be also be transmitted to ER staff in real-time while the patient is on her way to the hospital – allowing the hospitals to be better prepared for the care.

Tracking of Inventory, Staff, and Patients

Healthcare organizations are all about increasing the efficiencies of their workforce and reduce operational costs. This is true for both small and large institutions that include several staff members, patients, and inventory. Using IoT devices in the form of wireless ID cards, hospitals can manage admissions, increase the security, and measure the overall performance of the staff. BLE (Bluetooth Low Energy) beacons and RFID tags can be used to track the location of the inventory and also trace the staff members in case of any urgency.

Moreover, IoT and RTLS (Real-Time Location Systems) together can facilitate asset tracking. This is one of the most inexpensive ways to keep track of the equipment, drugs, and free resources, who can then spend more time on patient care.

Must Read: How IoT Is Transforming Healthcare

Augmenting Surgeries

When it comes to healthcare, IoT has penetrated operating rooms as well. Think of connected robotic devices, which are powered by Artificial Intelligence and are used to perform various surgeries. These operations are all about increased precision brought forth by robot-assisted surgeons. Moreover, connected devices and IoT applications can perfectly streamline the activities of the medical staff at both pre and post-operating stages. In both cases, IoT sensors can be used to collect, transmit data, and analyze it. This helps record the tiniest details and therefore, is useful in preventing surgical complications.

Virtual Monitoring of Critical Hardware

It is a given that all the modern healthcare facilities require state-of-the-art hardware and software to function. When these are not taken care of in the best possible manner, the hardware can pose various risks and threats. Think of power outages, system failures, or even cyber-attacks. Since no healthcare organization would want these mishaps to occur, they opt for the best IoT driven solutions. A case in point is that of e-Alert by Philips , which can virtually monitor critical medical hardware. If there is an anomaly in any equipment, the solution alerts the hospital staff, so that a failure can be avoided by preventive maintenance.

Pharmacy Management

The pharmacy business is worth millions of dollars and is quite complicated. Since there are several steps in transferring and managing the drugs from plant to storage facilities in a hospital, there are several preservation issues that may be associated with them.  IoT can help combine the best safety approaches and the latest technology to ensure faster drug delivery, safer operations, and better patient care.

For instance, take the example of smart fridges, which can be used to store vaccines and keep them from getting damaged during handling, storage or transfer.

IoT-enabled pharmacies can ensure greater efficiencies and effectiveness in operations, error-free medical dispensing, security, and overall enhanced patient satisfaction.

Must Read: Key Challenges with Enterprise IoT Applications

IoT devices, in the form of wearables, can let the care teams collect numerous data points about the patient’s sleep patterns, activity, heart rate, temperature,  and so on. These wearables can offer real-time information to caregivers and patients. Think of a situation in which a heart patient has an elevated heart-rate. The wearable will immediately transmit the signal to the nursing staff and allow them to provide immediate and timely assistance to the patient. This can also help in remote health monitoring of elderly patients who are outside the hospital premises but need constant monitoring.

IoT in healthcare has tremendous potential and can prove to be immensely beneficial for healthcare providers and patients. It is set to transform patient care and organizational efficiencies. Several hospitals around the world have already leveraged the power of IoT under their smart hospital initiatives. At Pratiti, we have helped several healthcare organizations with the design and development of their smart healthcare software development solutions.

Connect with us  to know how we can help you improve patient care, reduce complexity, improve efficiency, and empower decision-makers with actionable insights at the point of care.

Frequently Asked Questions

How is iot used in healthcare.

Remote monitoring in the healthcare industry is now possible thanks to IoT devices, which have the ability to keep patients safe and healthy while also enabling healthcare providers to provide improved treatment. As communication with doctors has gotten easier and more efficient, it has also boosted patient interaction and satisfaction. Furthermore, remote monitoring of a patient’s health helps to shorten hospital stays and avoid re-admissions. IoT in healthcare has a big impact on lowering healthcare expenses and improving treatment outcomes.

What are the Advantages of IoT in Healthcare?

The advantages of internet of things in healthcare applications lies in remote use cases. For example, in the event of a medical emergency, real-time remote monitoring via connected IoT devices and smart notifications can detect illnesses, treat diseases, and save lives.

Smart sensors monitor health status, lifestyle choices, and the environment to suggest preventative steps that will limit the occurrence of diseases and acute states.

Medical data accessibility allows patients to receive high-quality care while also assisting healthcare providers in making the best medical decisions.

What are the Challenges of IoT in Healthcare?

Although there are numerous advantages as seen in internet of things in healthcare examples, there are also challenges.

Healthcare providers are frequently tasked with ensuring the security of several sites as well as vast data repositories.

Moreover, moving an entire facility to a new system and practice takes time, and the initial investment and installation costs can be prohibitive, particularly for smaller healthcare facilities and rural clinics.

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case study of health care in iot

Milind Barve

Milind has 25 years of experience in building Enterprise Software. He is an Alumni of Indian Institute of Technology, Mumbai. He is currently the co-founder & CTO of Pratiti Technologies Driving technology implementations in IoT, Digital enterprise and Healthcare domains. Instrumental in building Business applications and data platform for Industry 4.0 application stack. He also has experience in building IoT applications for renewable energy asset monitoring, discrete manufacturing, logistics Smart Home platform etc. He has a vast Exposure to building enterprise solutions for large industrial customers, Enterprise Integrations & Data exchanges. He also holds expertise in designing and Manufacturing Engineering solutions.

case study of health care in iot

Stefan Jockush

Stefan is Services Innovation Leader for Philips. In this role, he leads a team of over 400 software engineers and product managers to develop and deliver the remote and IoT capability for Philips devices. The team also provides innovative, data and software driven services to enable the digital transformation of Philips customers.

Before joining Philips, Stefan has served in several senior business and technology leadership roles at Siemens. He was head of strategy for the Digital Industries Software business, facilitating the expansion of portfolio and the transition to a SaaS business model, and head of the automotive industry practice, introducing solutions to tackle key challenges of the industry.

Stefan is a dual citizen of the US and Germany with broad international experience, born and raised in Germany and living in the US since 2001. He holds a PhD in Physics.

case study of health care in iot

Rajiv Sivaraman

Rajiv in his current role as VP Global alliances with Siemens digital industry software business unit, Rajiv is involved with driving global strategic partnerships to help customers realize their digital enterprise aspirations. Rajiv comes with over 30 years’ cross Industry and cross-cultural experience that includes global and regional P&L leadership responsibilities across manufacturing, energy & infrastructure sectors and holds an honors degree in electrical & electronics engineering. In his tenure with Siemens, Rajiv has been instrumental in incubating startup business around Industrial IOT & Cyber security, built vertical market practice for data center vertical and developed the ISV ecosystem around the IOT platform MindSphere. Rajiv has also been a founding member & past director of the European association for Data Centers and is the chairman of executive committee for not-for-profit Industrial IoT business community (MindSphere world) in North America which he helped set up. Rajiv is part of the advisory board for the Georgia Institutes of Informatics’, University of Georgia.

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Prashant Anaskure

Prashant is Co-founder and COO at Pratiti Technologies. Throughout his career, his passion has been to help improve and transform business processes, by delivering NextGen products leveraging latest technologies. In the first 15 years, his focus was on the manufacturing industry and technologies such as 3D CAD/CAM. Since the last 10 years, his focus has been on applying Digital technologies such as cloud, IoT and AI in a wide range of industries such as healthcare, energy and manufacturing. Prashant is an alumnus of IIT Kharagpur and IIM Calcutta. He will be our moderator for the session today.

case study of health care in iot

In his 25+ years of career, Nitin has played key roles in building software solutions right from ideation to deployment. As a Co-founder and CEO of Pratiti Labs Inc., he is committed to applying his management learning as well as the passion for building new solutions to realize end-client innovation with certainty. He is also the Co-founder and CEO of Helios IoT Systems – an energy analytics solution company that developed a patented digital twin, IoT and advanced analytics based solution – Apollo. He recently drove a strategic technical partnership between AVEVA and Helios IoT Systems and led Apollo solution to the ‘Next Gen Product of the Year’ recognition. Through his unique leadership style and innovation mindset, Nitin has also led Pratiti to several awards and recognitions from the partner ecosystem including ‘Siemens Partner of the Year’ as well as leading analyst organizations like Frost & Sullivan, Zinnov and NASSCOM. Through his many achievements in product engineering and leading two successful organizations, Nitin has gained impeccable and tangible skills in crafting and leading custom digital solutions to solve business problems across various industries.

case study of health care in iot

Girish Dhakephalkar

Girish is the founder of Shoonya and an expert in delivering Effective Business Solutions using Immersive Technologies. He has a Master’s degree in Computer Games Technology from the University of Abertay, Scotland.  After working in the Video Games industry for 5 years in Scotland, he came back to India in 2009 and founded Shoonya, with the aim of exploring applications of Immersive Technologies beyond Video Games. He has been through the evolution of Immersive Technologies like Augmented Reality & Virtual Reality right since their inception and has a deep understanding of how to effectively deliver AR/VR solutions that provide real business value. Over the past 13+ years, Girish has worked on several projects in industry sectors including Aviation, Transportation, Real Estate, Automobile, Energy, Telecom and Manufacturing, with clients across India, UK, Europe, Middle East and Singapore.

case study of health care in iot

Ron Fritz is CEO and co-founder of Tech Soft 3D, the company that supplies underlying 3D technology to more than 700 companies developing engineering software, including Autodesk, Siemens, Dassault, PTC, Bentley, Trimble, ANSYS and many others. More recently, Tech Soft 3D has been providing technologies for CAD import to Digital Twin players including Unity, Unreal and nVidia Omniverse. Ron had led the company since it was founded in 1996 with a focus on business strategy and growth initiatives. The company is headquartered in Oregon in the United States with R&D offices in California, France and Norway.

case study of health care in iot

Deepak Konnur

Deepak Konnur has 40+ years of experience in the Energy Utility business. Presently he is engaged with Adani Transmission as Advisor Digitalization helping technology and business teams to increase their productivity, predictability, reliability, efficiency through improved data and analytics backed informed decision making and to achieve enhanced returns on the assets deployed. He had worked as Chief Digital Officer, Tata power and was responsible for evangelizing and accelerating digitization initiatives & interventions across all entities of Tata Power. Prior to Joining Tata Power, Deepak was working with IBM as Vice President – Energy & Utilities Industry Solutions Deepak has long background in OT & IT Management and has developed and deployed OT & IT plans that achieve strategic business goals. Deepak is an Electrical Engineer and has done post graduation in Business Administration.

Technology Team Transition At Zero Cost for Organizations Affected by Russia Ukraine War

As part of the global technology community and driven by our core values, we are offering free transition of the product engineering technology teams for organizations affected by the Russia Ukraine War. We are extending these services to all the Software Technology ISVs, Product Start-Ups and Industrial Customers who had their technology centres of excellence or had outsourced their product development work to Ukraine/Russia. Pratiti team can set-up and transition technology teams from Ukraine/Russia in its Indian centre at zero cost for such affected organizations to support in their business continuity. If you are looking for a technology partner to take over your product engineering work or need support in scaling / transitioning your operations and are unable to reach out to your existing partners from Russia/Ukraine, we invite you for a free session with our Pratiti Leadership Team (CEO, COO or CTO) to help you transition your technology teams at zero cost in these uncertain times with certainty.

At Pratiti, we are neither politically aligned with any parties, nor do we support violence of any form. We remain deeply concerned at the worsening ongoing situation and call for immediate cessation of violence and end to hostilities. As a member of technology community, we remain committed to our industry team members, customers and partners. We are completely operational in a hybrid model, full to our capacity during this crisis – which remains a health and humanitarian crisis.

case study of health care in iot

Sujit Chakrabarty

Founder, smit.fit.

Sujit is a digital visionary whose skillsets have stemmed from understanding and solving customer challenges.Being an industry veteran in the engineering and business analysis space, he has tangible knowledge and insights on how a digital product should be crafted for end-users. His technical and business perspectives have enabled various clients to provide value-driven solutions to end users.He is now leading the strategy and innovations at Smit.fit, a user-empathy-driven product that helps end-users take control over their fitness lifestyles. Prior to founding Smit.fit, Sujit was a Partner at McKinsey.

case study of health care in iot

Co-founder & CEO, Pratiti Technologies

Nitin is a postgraduate from IIT, Mumbai and in his 25 years of career, has played key roles in building software solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize customers’ innovation with certainty. Prior to co-founding Pratiti, Nitin was the Global COO for Geometric (now acquired by HCL).

case study of health care in iot

Cofounder & COO

Prashant loves technology. While this passion helped him top his Masters class at IIT, he wanted to ensure his contribution remains ‘applied’ rather than ‘theoretical’. He is very excited by the convergence of technologies such as SMAC & IoT, and the Digital Transformation they are driving. In his current role, Prashant leads & manages key software projects at Pratiti to deliver higher value & strategic advantage to the clients.

case study of health care in iot

Stephen Bashada

Ceo, bashada software consulting.

Stephen retired from Siemens in April 2020 as the CEO for the Siemens global IIoT business. Over the past 40 years, Stephen has always been grounded in a solid technical framework from hands on software troubleshooting to creating business outcomes from technology selections and business models.In his current role, he provides software advisory consulting through board positions, software projects and strategic software development practices. During his stint at Siemens, Stephen has received many accolades and is highly regarded for his contributions in MindSphere & Teamcenter.

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Cyberattack Paralyzes the Largest U.S. Health Care Payment System

The hacking shut down the nation’s biggest health care payment system, causing financial chaos that affected a broad spectrum ranging from large hospitals to single-doctor practices.

A portrait of Molly Fulton, who sits in the waiting room of one of the urgent care centers she runs. She wears a blazer over a black blouse with her hands folded in her lap.

By Reed Abelson and Julie Creswell

An urgent care chain in Ohio may be forced to stop paying rent and other bills to cover salaries. In Florida, a cancer center is racing to find money for chemotherapy drugs to avoid delaying critical treatments for its patients. And in Pennsylvania, a primary care doctor is slashing expenses and pooling all of her cash — including her personal bank stash — in the hopes of staying afloat for the next two months.

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Open this article in the New York Times Audio app on iOS.

These are just a few examples of the severe cash squeeze facing medical care providers — from large hospital networks to the smallest of clinics — in the aftermath of a cyberattack two weeks ago that paralyzed the largest U.S. billing and payment system in the country. The attack forced the shutdown of parts of the electronic system operated by Change Healthcare, a sizable unit of UnitedHealth Group, leaving hundreds, if not thousands, of providers without the ability to obtain insurance approval for services ranging from a drug prescription to a mastectomy — or to be paid for those services.

In recent days, the chaotic nature of this sprawling breakdown in daily, often invisible transactions led top lawmakers, powerful hospital industry executives and patient groups to pressure the U.S. government for relief. On Tuesday, the Health and Human Services Department announced that it would take steps to try to alleviate the financial pressures on some of those affected: Hospitals and doctors who receive Medicare reimbursements would mainly benefit from the new measures.

U.S. health officials said they would allow providers to apply to Medicare for accelerated payments, similar to the advanced funding made available during the pandemic, to tide them over. They also urged health insurers to waive or relax the much-criticized rules imposing prior authorization that have become impediments to receiving care. And they recommended that insurers offering private Medicare plans also supply advanced funding.

H.H.S. said it was trying to coordinate efforts to avoid disruptions, but it remained unclear whether these initial government efforts would bridge the gaps left by the still-offline mega-operations of Change Healthcare, which acts as a digital clearinghouse linking doctors, hospitals and pharmacies to insurers. It handles as many as one of every three patient records in the country.

The hospital industry was critical of the response, describing the measures as inadequate.

Beyond the news of the damage caused by another health care cyberattack, the shutdown of parts of Change Healthcare cast renewed attention on the consolidation of medical companies, doctors’ groups and other entities under UnitedHealth Group. The acquisition of Change by United in a $13 billion deal in 2022 was initially challenged by federal prosecutors but went through after the government lost its case.

So far, United has not provided any timetable for reconnecting this critical network. “Patient care is our top priority, and we have multiple workarounds to ensure people have access to the medications and the care they need,” United said in an update on its website .

But on March 1, a bitcoin address connected to the alleged hackers, a group known as AlphV or BlackCat, received a $22 million transaction that some security firms say was probably a ransom payment made by United to the group, according to a news article in Wired . United declined to comment, as did the security firm that initially spotted the payment.

Still, the prolonged effects of the attack have once again exposed the vast interconnected webs of electronic health information and the vulnerability of patient data. Change handles some 15 billion transactions a year.

The shutdown of some of Change’s operations has severed its digital role connecting providers with insurers in submitting bills and receiving payments. That has delayed tens of millions of dollars in insurance payments to providers. Pharmacies were initially unable to fill many patients’ medications because they could not verify their insurance, and providers have amassed large sums of unpaid claims in the two weeks since the cyberattack occurred.

“It absolutely highlights the fragility of our health care system,” said Ryan S. Higgins, a lawyer for McDermott Will & Emery who advises health care organizations on cybersecurity. The same entity that was said to be responsible for the cyberattack on Colonial Pipeline, a pipeline from Texas to New York that carried 45 percent of the East Coast’s fuel supplies, in 2021 is thought to be behind the Change assault. “They have historically targeted critical infrastructure,” he said.

In the initial days after the attack on Feb. 21, pharmacies were the first to struggle with filling prescriptions when they could not verify a person’s insurance coverage. In some cases, patients could not get medicine or vaccinations unless they paid in cash. But they have apparently resolved these snags by turning to other companies or developing workarounds.

“Almost two weeks in now, the operational crisis is done and is pretty much over,” said Patrick Berryman, a senior vice president for the National Community Pharmacists Association.

But with the shutdown growing longer, doctors, hospitals and other providers are wrestling with paying expenses because the steady revenue streams from private insurers, Medicare and Medicaid are simply not flowing in.

Arlington Urgent Care, a chain of five urgent care centers around Columbus, Ohio, has about $650,000 in unpaid insurance reimbursements. Worried about cash, the chain’s owners are weighing how to pay bills — including rent and other expenses. They’ve taken lines of credit from banks and used their personal savings to set aside enough money to pay employees for about two months, said Molly Fulton, the chief operating officer.

“This is worse than when Covid hit because even though we didn’t get paid for a while then either, at least we knew there was going to be a fix,” Ms. Fulton said. “Here, there is just no end in sight. I have no idea when Change is going to come back up.”

The hospital industry has labeled the infiltration of Change “the most significant cyberattack on the U.S. health care system in American history,” and urged the federal government and United to provide emergency funding. The American Hospital Association, a trade group, has been sharply critical of United’s efforts so far and the latest initiative that offered a loan program.

“It falls far short of plugging the gaping holes in funding,” Richard J. Pollack, the trade group’s president, said on Monday in a letter to Dirk McMahon, the president of United.

“We need real solutions — not programs that sound good when they are announced but are fundamentally inadequate when you read the fine print,” Mr. Pollack said.

The loan program has not been well received out in the country.

Diana Holmes, a therapist in Attleboro, Mass., received an offer from Optum to lend her $20 a week when she says she has been unable to submit roughly $4,000 in claims for her work since Feb. 21. “It’s not like we have reserves,” she said.

She says there has been virtually no communication from Change or the main insurer for her patients, Blue Cross of Massachusetts. “It’s just been maddening,” she said. She has been forced to find a new payment clearinghouse with an upfront fee and a year’s contract. “You’ve had to pivot quickly with no information,” she said.

Blue Cross said it was working with providers to find different workarounds.

Florida Cancer Specialists and Research Institute in Gainesville resorted to new contracts with two competing clearinghouses because it spends $300 million a month on chemotherapy and other drugs for patients whose treatments cannot be delayed.

“We don’t have that sort of money sitting around in a bank,” said Dr. Lucio Gordan, the institute’s president. “We’re not sure how we’re going to retrieve or collect the double expenses we’re going to have by having multiple clearinghouses.”

Dr. Christine Meyer, who owns and operates a primary care practice with 20 clinicians in Exton, Pa., west of Philadelphia, has piled “hundreds and hundreds” of pages of Medicare claims in a FedEx box and sent them to the agency. Dr. Meyer said she was weighing how to conserve cash by cutting expenses, such as possibly reducing the supply of vaccines the clinic has on hand. She said if she pulled together all of her cash and her line of credit, her practice could survive for about two and a half months.

Through Optum’s temporary funding assistance program, Dr. Meyer said she received a loan of $4,000, compared with the roughly half-million dollars she typically submits through Change. “That is less than 1 percent of my monthly claims and, adding insult to injury, the notice came with this big red font that said, you have to pay all of this back when this is resolved,” Dr. Meyer said. “It is all a joke.”

The hospital industry has been pushing Medicare officials and lawmakers to address the situation by freeing up cash to hospitals. Senator Chuck Schumer, Democrat of New York and the chamber’s majority leader, wrote a letter on Friday, urging federal health officials to make accelerated payments available. “The longer this disruption persists, the more difficult it will be for hospitals to continue to provide comprehensive health care services to patients,” he said.

In a statement, Senator Schumer said he was pleased by the H.H.S. announcement because it “will get cash flowing to providers as our health care system continues to reel from this cyberattack.” He added, “The work cannot stop until all affected providers have sufficient financial stability to weather this storm and continue serving their patients.”

Audio produced by Jack D’Isidoro .

Reed Abelson covers the business of health care, focusing on how financial incentives are affecting the delivery of care, from the costs to consumers to the profits to providers. More about Reed Abelson

Julie Creswell is a business reporter covering the food industry for The TImes, writing about all aspects of food, including farming, food inflation, supply-chain disruptions and climate change. More about Julie Creswell

  • Systematic Review
  • Open access
  • Published: 02 June 2023

Successes, weaknesses, and recommendations to strengthen primary health care: a scoping review

  • Aklilu Endalamaw   ORCID: orcid.org/0000-0002-9121-6549 1 , 2 ,
  • Daniel Erku   ORCID: orcid.org/0000-0002-8878-0317 1 , 3 , 4 ,
  • Resham B. Khatri   ORCID: orcid.org/0000-0001-5216-606X 1 , 5 ,
  • Frehiwot Nigatu 6 ,
  • Eskinder Wolka 6 ,
  • Anteneh Zewdie 6 &
  • Yibeltal Assefa   ORCID: orcid.org/0000-0003-2393-1492 1  

Archives of Public Health volume  81 , Article number:  100 ( 2023 ) Cite this article

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Metrics details

Primary health care (PHC) is a roadmap for achieving universal health coverage (UHC). There were several fragmented and inconclusive pieces of evidence needed to be synthesized. Hence, we synthesized evidence to fully understand the successes, weaknesses, effective strategies, and barriers of PHC.

We followed the PRISMA extension for scoping reviews checklist. Qualitative, quantitative, or mixed-approach studies were included. The result synthesis is in a realistic approach with identifying which strategies and challenges existed at which country, in what context and why it happens.

A total of 10,556 articles were found. Of these, 134 articles were included for the final synthesis. Most studies (86 articles) were quantitative followed by qualitative (26 articles), and others (16 review and 6 mixed methods). Countries sought varying degrees of success and weakness. Strengths of PHC include less costly community health workers services, increased health care coverage and improved health outcomes. Declined continuity of care, less comprehensive in specialized care settings and ineffective reform were weaknesses in some countries. There were effective strategies: leadership, financial system, ‘Diagonal investment’, adequate health workforce, expanding PHC institutions, after-hour services, telephone appointment, contracting with non-governmental partners, a ‘Scheduling Model’, a strong referral system and measurement tools. On the other hand, high health care cost, client’s bad perception of health care, inadequate health workers, language problem and lack of quality of circle were barriers.

Conclusions

There was heterogeneous progress towards PHC vision. A country with a higher UHC effective service coverage index does not reflect its effectiveness in all aspects of PHC. Continuing monitoring and evaluation of PHC system, subsidies to the poor, and training and recruiting an adequate health workforce will keep PHC progress on track. The results of this review can be used as a guide for future research in selecting exploratory and outcome parameters.

Peer Review reports

A comprehensive primary health care (PHC) allows all members of the population to access essential health services without financial catastrophe [ 1 ] that is given in district hospitals, health centres, clinics and health posts [ 2 , 3 , 4 ]. PHC is a ‘whole system approach’—to deliver health promotion, disease prevention, curative and rehabilitative care—supported by medical supplies, multidisciplinary health teams, health governance and financing [ 5 , 6 , 7 ]. Moreover, it delivers health care services which have gotten attention since 1978 at ‘Alma-Ata’ declaration [ 8 ] and other prioritized services through time, like public health emergencies, common eye-nose-throat and oral health problems and mental health services [ 7 , 9 , 10 ].

PHC in its first inception aimed for ‘Health for All by the Year 2000’. Eventually, PHC is amenable to any global and national health policies, and most recently, it is a roadmap for achieving universal health coverage (UHC) by 2030 [ 11 ]. As a result, the global leaders and country representatives proclaimed a renewed action on PHC towards UHC in an international conference held in Kazakhstan, in October 2018 [ 12 ].

However, the World Health Organisation (WHO) projected that only 39% to 63% of the global population would be covered for essential health services by 2030 [ 13 ]. Hence, to take corrective actions and support government investment in PHC, health policy needs evidence about the challenges and effective strategies. In 2013, a review paper reported the impact of PHC delivery models [ 14 ] that discussed PHC models in improving access, quality and care coordination. However, it did not address PHC success, strategies, weaknesses, or challenges. Capacity building, human resources for health, technology, financing, and empowering individuals and communities complement the health system [ 8 , 12 , 15 , 16 , 17 , 18 ].

This study synthesized successes, strategies, weakness, and barriers of PHC dimensions. Therefore, the current study’s findings will be crucial to supplement PHC-related policy design, implementation, and evaluation.

The review was conducted per Levac and colleagues’ [ 19 ] five-step approach, including identifying research questions, identifying and selecting relevant studies, extracting data, and summarizing and reporting results. In addition, we followed the PRISMA extension for scoping reviews checklist to report this review ( Additional file ).

Search strategy

The required data were collected by searching on 4 May 2022 in the PubMed database and hand search by using the Google Scholar search engine. The search was updated on 28 April 2023. The key search terms or phrases used for searching articles fitted to PubMed were ("primary health care"[Title]) OR ("primary healthcare"[Title]) OR ("primary health-care"[Title]) Filters: English.

Inclusion and exclusion criteria

We included all types of articles that evaluated primary health care. These articles are quantitative, qualitative, mixed, or review by using data from clients, communities, document or article reviews, or health institutions. The types of articles were identified during the screening and data-extraction phase. Quantitative articles are estimated and presented the results mathematically, while qualitative articles are perspectives, in-depth interviews, focus-group discussions, and observations in which results are presented in texts. A review was any types of one or more principles of PHC. We considered mixed studies when quantitative and qualitative approaches are integrated into a single study. Since primary care is a subset of primary health care, we focused on the core principles of primary care in this synthesis. When the success and weakness of PHC researched its core principles i.e., accessibility, quality of care, effectiveness, cost-effectiveness, coordination, continuity, comprehensiveness, efficiency, equity and patient-centredness, we included all these as well. There were no time and place restrictions.

Articles with abstract or title only, letters to editors, perspectives, commentaries, conference abstracts and studies that do not have reported relevant findings to the current objectives were excluded. Articles published other than in English were also excluded.

Study selection and data extraction

Title, abstract and full-text screening was conducted by two authors (AE, DE) and the third author was involved whenever disagreement happened (YA). Then, appropriate data was extracted from included articles. These are: first author, publication year, country (study setting), study approach, study population, attributes, and objectives are displayed in the supplementary file (Table S 1 ), and the main findings are presented in the result section.

Statistical analysis and synthesis

UHC effective service coverage index of countries mentioned in the included articles are presented using the Choropleth map. We generated Choropleth map using R-software. Data of UHC effective service coverage index was taken from the Global health observatory [ 20 ]. UHC effective service coverage index is a composite of a single summary indicator estimated from the coverage value of 14 tracer indicators, mainly from infectious diseases (tuberculosis and HIV/AIDS); reproductive, maternal, neonatal and child health services; non-communicable disease treatments (hypertension control); service capacity and access [ 20 ]. As a ‘whole-of-society’ context, leadership, financial system, human resources and other facilitators or barriers were identified. The result synthesis is in a realistic approach i.e., showing which strategies and challenges were identified in which country, in what context and why it happens. Then, the strategies and barriers of PHC dimensions is summerised in figure.

Search results

Using search strategy, 10,323 articles were found in PubMed (9,466 on 04 May 2022 and 857 on 28 April 2023) and 233 from Google Scholar. A total of 569 remain after title screening. Following excluding title only, abstract only and unrelated abstract, 219 were eligible for full-text review. Letters, editorials, commentaries, perspectives, and full-text articles with unrelated findings were screened further. Finally, 134 articles were included for the result synthesis. Most studies (86 articles) were quantitative followed by qualitative (26 articles), and others (16 reviews and 6 mixed methods) (sT1).

Primary health care success, weakness, strategies, and barriers

We can see UHC as an immediate outcome of PHC. The choropleth map shows the UHC effective service coverage index of 45-countries (Fig.  1 ). The average UHC effective service coverage index was 67.6; the minimum was 37 in Albania and Niger, while the highest value was 89 in Canada. The UHC effective service coverage index value for each country is in the supplementary file (Table S 2 ). Additionally, country-specific progress to specific primary care core principles and long-term health system outcomes.

figure 1

Choropleth map for UHC effective service coverage index in 2019

Success and weakness

PHC from an accessibility and quality of care point of view scored positive progress per countries contexts. Accessibility matters of how services are available, waiting time to receive care (timeliness), travel time or distance to reach PHC health institutions (geographic accessibility) and the affordability access. Reduced length of hospital stay in the Netherlands [ 21 ] and high continuity of care in India [ 22 ] was taken as exemplary lessons. Once increased accessibility, a more equitable distribution of health resources was achieved in Kazakhstan [ 23 ]. PHC Specialized reference clinics decreased health problem burdens by reducing waiting time and health care cost, and increased client satisfaction in Saudi Arabia [ 24 ].

There were an increased number of PHC facilities in Argentina [ 25 ]. Australia improved health care services accessibility for prisoners during their release [ 26 ]. Primary care was also evaluated for the provision of quality of care. Quality of care was assessed with client satisfaction, services outcome or in a logic-system process. There were diverse achievements of high quality health care for children in Brazil [ 27 ] and older people in Poland [ 28 ], for immunization, maternal health and epidemic disease control in Saudi Arabia [ 29 ], high patient satisfaction in Albania [ 30 ] and high patients perceived quality-care in privately owned institutions in Sweden [ 31 ].

From cost-effectiveness perspective, an evaluation of the cost-effectiveness of PHC projects in the USA showed that the non-physician service providers ratio were cost-effective [ 32 ]. The reason for this difference was not explicitly explained to confirm whether the variation was due to productivity or salary differences. A cost-efficiency measure of PHC in Indonesia showed that community health worker services were less costly than clinic-based care [ 33 ] because community services focus on preventive health care. A tool is important to monitor and evaluate the released fund or to generate a new fund. A new health service-related cost monitoring and evaluation tool was developed for fund raising purpose in Bangladesh [ 9 ]. High level of coordination, continuity of care and comprehensiveness of PHC in Brazil [ 34 , 35 , 36 ], high level of understanding of patient-centredness care in Uganda [ 37 , 38 ] and presence of better patient-centred care in private clinics in Thailand [ 39 ] were successes. India scaled-up comprehensive PHC using ‘Ayushman Bharat’ program in India [ 10 ].

There were diverse progress towards narrowing disparity in PHC such as reduced disparities in immigrant populations’ health [ 40 ], the presence of inclusive interventions for diverse populations with adequate government budgets in different countries [ 41 ] and promotion of health equity (e.g., include equity statement in all health policy) in Australia [ 42 , 43 ], Canada [ 44 ] and in China [ 45 ]. Furthermore, policy inclusiveness implemented in some countries through including community engagement in the policy strategy (e.g. Mexico [ 46 ], Italy [ 47 ] and Kenya [ 48 ], engagement of donor agencies and high female representation (e.g., in Nigeria [ 49 , 50 ] and the UK [ 51 ]. Additionally, community oriented and poor-based services in Asia [ 52 ] and migrant health volunteer participation in Thailand [ 53 ] indicate successful initiation to narrow the gaps. In addition, the higher service readiness has resulted in better effectiveness in Mozambique [ 54 ].

There were observed gaps as weaknesses in various countries. For instance, weak continuity care, low accessibility score of comprehensiveness of PHC and community participation in Brazil [ 34 , 55 ] and a declined continuity of care from 2012 to 2017 in England (due to the unsatisfactory appointment system for patients) [ 56 ] wear weakness. Clinics in metropolitan areas and capital cities were less comprehensive as these facilities provided more specialized care and treat medical problems referred from lower health care settings in South Korea [ 57 ]. Ineffective PHC reform due to a lack of prior or timely monitoring and evaluation procedures for PHC activities [ 58 ] and technical inefficiency in Greek [ 59 ], inefficient management in China [ 60 , 61 ], and lower level of technical efficiency in Spain [ 62 ] were weaknesses. PHC services and facility disparities based on geography, education and income status, race, ethnicity and citizenship in Sweden [ 63 , 64 ], Ghana [ 65 ], Nigeria [ 66 ], the UK [ 67 , 68 ] and the UAE [ 69 ], South Africa [ 70 ], Poland [ 71 ] and Brazil [ 7 , 27 , 34 , 72 , 73 ]. To mention, high population density area in China [ 74 ] and people live in far distance did not have access to PHC in Ghana [ 75 ]. There was lower service coverage in certified facilities compared to non-certified institutions in Philippines [ 76 ].

Strategies to improve primary health care

There are several leaderships, health workforce, technology, health financing, service delivery and contextual-related strategies and barriers. Transactional and transformational leadership styles [ 77 ] facilitated the success of PHC management system. In addition, struggling to shift from a hierarchical to a more relational style in South Africa [ 78 ] improved PHC. More comprehensive primary-care improved quality of care and efficiency in the USA [ 79 ]. Iceland approached telephone services where no telephone service difference in private and community-owned clinics [ 80 ] (Table 1 ).

Barriers of primary health care

Principles of PHC affected one another. For example, problem in ‘access’ and ‘non-comprehensiveness services’ [ 27 , 106 , 113 ], uncoordinated care in Brazil [ 113 ] and China [ 114 ] and continuity of care in China [ 114 ] impaired quality of care. Additionally, accessibility problems (unavailability and timeliness [ 115 ], financial inaccessibility) in Burkina Faso [ 116 ] affects the quality of care. Similarly, a high proportion of walk-in care and high patient volume in Canada [ 95 ], problems in accessibility and community orientation in the UK [ 117 ] interrupted continuity of care (Table  2 ). Figure  2 shows the conceptual frameworks to practice, policy, and researchers on the comprehensive PHC based on the main strategies and barriers.

figure 2

Strategies and barriers of Primary Health Care. Supporting information: additional file, characteristic of studies (Table S 1 ) and UHC effective service coverage index (Table S 2 )

There was heterogeneous progress towards PHC vision. This review identified effective leadership, financial system, diagonal investment, health workforce development, expanding PHC institutions, after-hour services, telephone appointments, contracting with NGOs, a ‘Scheduling Model and a strong referral system and tools effective strategies to PHC achievement. High health care costs, client’s bad perception to health care, health workers inadequacy, language barrier and lack of quality of circle that barred PHC progress.

The leadership/governance functions greatly impacted PHC. One of its functions is working with NGOs. Working with NGOs improved PHC system because it strengthen the health system [ 142 ]. Effective leadership constructing appropriate health care infrastructure expanded municipality areas certainly improves PHC [ 143 ] because it would be inclusive to all individuals (e.g., disabled) and up-to-date technologies for health [ 144 , 145 ]. Effective leadership also allows a bidirectional management system to improve accountability, community participation and support participatory decision-making process in PHC. When people become more responsible, accountability is more likely to be kept in human mind [ 146 ]. Effective leaders are also proactive in reviewing health system policy, and monitoring and following health policy inclusiveness [ 42 , 47 ]. Countries should be curious about their health system reform because ineffective health system reform dismantled the existing PHC system [ 58 , 60 ]. Health policy reforms depends on how, when and by whom the reform is implemented, and requires public understanding and support, continuous monitoring and evaluation before, during and after implementation[ 147 ].

Expanding the municipality or institution of PHC was another effective strategy. The presence of primary health care institutions near to the community can be a prior strategy to PHC performance. It is important in reducing direct, indirect and intangible costs. Walking short distance to health institution reduce transport cost, food cost and productivity loss because clients and care giver (client supporters) can receive service shortly and return to their job. Traveling short distance to health institutions can also prevent/reduce intangible cost, which could happen if clients may not return to work for long time due to long travel. It is supported by providing low-cost services, offering outreach services, providing free transportation to the poor [ 14 , 84 ] and reaching poor geographical areas improved the accessibility of PHC [ 89 ].

A strong Health financing system supported the PHC system. Provision of free transportation to and from PHC institutions to clients (the poor) and availing low-cost services improved PHC [ 14 ]. This requires an adequate health budget and sustainable financing [ 41 , 45 ]. The diagonal investment was a successful strategy for filling the gap due to the comprehensive nature of PHC. A diagonal approach to scale-up of PHC system effectively improved maternal and child health [ 148 ]. This approach was also effective in the progress of UHC to care for chronic illness in the overall health system [ 149 ].

Adequate health workforce development accelerates PHC progresses [ 150 ]. Improving health workforce adequacy, like numbers with different skills, education, engaging interpreters and gender-concordant providers improved PHC. In a country where interpreters were included in the health workforce, PHC performance was improved. However, a PHC system should be careful in recruiting and using interpreters. For example, an interpreter may provide much information to patients with lower English proficiency at a time, but a patient may not grasp all information at once [ 151 ]. Gender-concordant health care providers improved PHC. Patient-physician gender concordance might impact patients’ perception (felt treated with respect), especially during sensitive health issues [ 152 ]. Despite its effectiveness, the disparity of PHC team composition between regions or institutions, lack of qualified health workers in the community, unbalanced population-to-physician ratio, and health workers’ lack of training interrupted the provision of continuous, coordinated and quality PHC. The absence of a quality circle interrupted PHC continuum of effective progress. In the absence of ‘quality of care circle’, there could be no way to a group of health team who meet regularly to discuss how to adhere with the standard of care, and quality of PHC is disrupted as a result [ 135 ]. Inadequate incentives for health workers also impeded accountable health care providers [ 153 ].

After-hour service is helpful when medical problems are addressed by few professionals or when health professionals are few due to high health care demands. When working hours are extended beyond eight hours per day, clients can get skilled personnel at PHC centre at any time. As a result, after-hour services reduced demand for acute care and reduced costs [ 154 ].

A ‘Scheduling Model’ improved PHC performance through accessibility and quality of PHC by which clients make an appointment to care based on their preference for the type of care and skilled personnel. It also has the power to change the perception of clients whereby clients perceived as they received better care [ 107 ]. Similarly, the probabilistic patient scheduling model was effective in a hospital by increasing annual cumulated profit, and decreasing waiting list and waiting times [ 155 ]. A scheduling model is an important procedure in a patient-referral system. Approaching this model helps primary care providers not to refer a patients to a physician with numbers of clients on the waiting queue [ 156 ].

A strong referral system shape health care system functionality and community perception of care. Moreover, the presence of referral system prevents health care service interruption [ 157 ]. In advancing technology, transition from paper-based referral to e-referral system partly solve conundrum of health workforce by using skewed physicians [ 158 ].

Telephone access and telephone appointments maintain an effective PHC system. Health technology and supply are the building blocks of health system [ 159 ]. Therefore, the absence of health technologies and lack of health system digitalisation lagged behind the successful progress of PHC [ 61 , 121 ].

The availability of appropriate tools, indicators and data supports the PHC system. Health information-related strategies allow measuring and disseminating health-related data that improves the PHC system [ 160 ]. In addition, it is known that offering tools and creating feedback mechanisms for the community reinforce the PHC system [ 161 ]. Therefore, a need to have agreed method of PHC cost measurement tool is required, for example, in Australia [ 6 ].

Community participation was an effective strategy. It is taken as a specific strategy in capacitating core principles of primary care and improving PHC outcomes. It helps to provide culturally safe care that promotes patients to attend health services for the next care [ 162 ]. Community participation improves clients’ perception towards care. In the current review, having better perception and client’s trust to health services supported PHC capacity, whereas bad perception found in contrast.

As to policy implication, a well-functioning health system—health leadership and governance, health finance, appropriate health workforce and availing proper health technology—pushes forward the PHC progress and maintains enacted PHC systems. Researchers can further examine the techniques to solve barriers and advancing emerging strategies. For example, ‘Quality of Circle’, ‘Scheduling Model’ and ‘Diagonal investment’.

Studies exclusively published in English are included in this review. This review might lack the chance of getting more advantageous by including non-English language articles. This scoping review, due to its design nature, lacks a quality appraisal of the included documents, and the current results may need caution in interpretation. Furthermore, a search from a single academic database (PubMed) may miss some important articles in other databases.

A country with a higher UHC effective service coverage index does not reflect its effectiveness in all aspects of PHC. Strengths of PHC are less costly community health workers services, presence of quality indicators and improved quality of care (e.g., maternal and child health), increased health care coverage, improvement of health outcome due to community participation, provision of comprehensive care and improved resource and service efficiency.

PHC is, beyond the technical practice given at health care spots, a system thinking that entertains multiple strategies towards health system impacts. Continues investment in PHC infrastructure, sustainable financing to reduce health care costs, appropriate workforce planning and training, construction of new PHC institutions in regions of low accessibility and institutionalizing quality of circle will accelerate PHC progress. A valid and agreed measurement tool for PHC attributes is also relevant. Additionally, the research did not address the wholistic concepts of PHC; almost all studies on PHC were only on integrated public and essential health services.

Availability of data and materials

The data set is available within this manuscript.

Abbreviations

Non-governmental Organisations

Primary Health Care

United Kingdom

United States of America

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AE and YA conceptualised the study design, retrieved relevant articles, screening and data extraction, analysed, interpreted the results, and drafted the manuscript. RBK and DE contributed to the research aim and manuscript draft, and critically revised the drafted manuscript. AZ, EW and FN contributed to critically revised the drafted manuscript. All authors read and approved the final manuscript.

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Additional file 1:.

Supplementary file 1. Preferred reporting items for systematic reviews and meta-analyses extension for scoping reviewschecklist.

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Table S1. Characteristics of articles.

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Table S2. UHC effective service coverage index for countries included in the review.

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Endalamaw, A., Erku, D., Khatri, R.B. et al. Successes, weaknesses, and recommendations to strengthen primary health care: a scoping review. Arch Public Health 81 , 100 (2023). https://doi.org/10.1186/s13690-023-01116-0

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case study of health care in iot

case study of health care in iot

Microsoft makes the promise of AI in healthcare real through new collaborations with healthcare organizations and partners

Mar 11, 2024 | Robert Dahdah - CVP, Health & Life Sciences

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A nurse sits with their patient and consults.

Just over a year ago, the healthcare industry was energized by the debut of generative AI and the promise this new technology held for delivering real-world outcomes that positively impact clinicians, patients, health systems, and the broader health and life sciences ecosystem. Since then, it has been a catalyst for the development of new use cases, opening possibilities for an entirely new era of innovation — and this shows no signs of slowing down. We continue to see AI adoption within healthcare grow, with 79% of healthcare organizations reporting that they’re currently using AI technology, according to a Microsoft-commissioned study through IDC [i] . AI also has a demonstrable business value, with healthcare organizations realizing a return on their AI investments within 14 months, along with an average return of $3.20 for every $1 they invest in AI [ii] .

Working alongside healthcare organizations, Microsoft is making the promise of AI real by empowering the industry to tackle its biggest challenges and create a real difference in the lives of clinicians and patients. At the 2024 HIMSS Global Health Conference & Exhibition , we are highlighting how providers are adopting generative AI solutions and the impact the technology is making.

  • Stanford Medicine and Microsoft announced the enterprise-wide deployment of Nuance Dragon Ambient eXperience Copilot (DAX Copilot), providing conversational, ambient and generative AI to Stanford Medicine’s clinicians. This deployment aligns with Stanford Medicine’s mission to alleviate physician burnout and enhance patient care by streamlining administrative tasks. Stanford Medicine’s commitment to innovation, coupled with DAX Copilot’s ability to automate clinical documentation, has led to significant improvements in efficiency and patient-focused care. DAX Copilot enables healthcare organizations to adopt AI-powered clinical documentation applications at scale, leveraging existing investments in our trusted and extensible Dragon solutions. Stanford Health Care clinicians who used DAX Copilot reported through a preliminary survey that 96% of physicians stated that it was easy to use, and 78% reported that it expedited clinical notetaking. About two-thirds reported that DAX Copilot saved time.
  • WellSpan Health announced its widespread adoption of DAX Copilot, enhancing patient-physician interactions during exam room and telehealth visits. Leveraging generative AI, DAX Copilot automates clinical note drafting, allowing physicians to focus entirely on patients without the distraction of manual documentation. WellSpan’s decision to adopt DAX Copilot builds upon its successful use of Nuance solutions to streamline clinical workflows and improve patient care. Surveys indicate high satisfaction among physicians and patients, with DAX significantly improving the quality of interactions and reducing documentation burdens. This initiative reflects WellSpan’s commitment to delivering exceptional care experiences and addressing physician burnout by providing innovative tools to enhance efficiency and personalize care delivery.
  • Providence and Microsoft announced a strategic collaboration aimed at accelerating AI innovation in healthcare. Leveraging Microsoft Cloud for Healthcare and Azure as a standard platform, the collaboration focuses on delivering AI-powered applications to improve interoperability, generate clinical insights and enhance care delivery. Past successes from this relationship include Providence’s migration to cloud solutions and the adoption of AI-powered applications like Nuance’s DAX Copilot. By leveraging their combined expertise, the collaboration aims to rapidly scale existing solutions and create more personalized experiences for patients and clinicians. Through this initiative, Providence aims to transform healthcare delivery and improve outcomes by harnessing the power of the cloud and advanced AI technologies.

Reinforcing our commitment to Responsible AI

As incredible as AI – and all its potential – is, the important role clinicians play in determining its use and enabling responsible AI guidelines is vital. That’s why we remain steadfast in our commitment to our Responsible AI principles , which help to ensure safe, fair and responsible use of the technology. As part of this ongoing commitment, Microsoft has joined a consortium of healthcare leaders to announce the formation of the Trustworthy & Responsible AI Network (TRAIN), creating one of the first health AI networks aimed at operationalizing responsible AI principles to improve the quality, safety and trustworthiness of AI in health.

Serving as the technology-enabling partner for TRAIN, Microsoft is working with members that include AdventHealth, Advocate Health, Boston Children’s Hospital, Cleveland Clinic, Duke Health, Johns Hopkins Medicine, Mass General Brigham, MedStar Health, Mercy, Mount Sinai Health System, Northwestern Medicine, Providence, Sharp HealthCare, University of Texas Southwestern Medical Center, University of Wisconsin School of Medicine and Public Health and Vanderbilt University Medical Center – to share best practices and provide tools to enable measurement of outcomes associated with the implementation of AI. Additionally, OCHIN , which serves a national network of community health organizations with solutions, expertise, clinical insights and tailored technologies, and TruBridge , a partner and conduit to community healthcare, will work with TRAIN to help ensure that every organization, regardless of resources, has access to the benefits the network offers.

Additionally, we continue to take the necessary steps to ensure healthcare organizations can implement technology in compliance with the highest levels of security and privacy in mind. We recently announced the preview of healthcare data solutions in Microsoft Fabric , which enables healthcare organizations to break down data silos and harmonize their disparate healthcare data in a single unified store where analytics and AI workloads can operate at-scale. We are also pleased to share that Fabric now supports HIPAA (Health Insurance Portability and Accountability Act) compliance , allowing our U.S. healthcare industry customers and partners to compliantly use Fabric to store, process and analyze data.

Driving innovation through the Microsoft partner ecosystem

Microsoft’s unmatched global ecosystem of trusted partners is one of the key components that helps drive our innovation forward. This week, Cognizant announced that its TriZetto Assistant on Facets will leverage Microsoft Azure OpenAI Service and Semantic Kernel to provide access to generative AI within the TriZetto user interface. This new collaboration will help increase productivity and efficiency for healthcare payers and providers, while ensuring timely responses and improved care for patients.

Additionally, Microsoft for Startups announced a new collaboration with the American Medical Association’s (AMA) Physician Innovation Network . The Physician Innovation Network is a powerful match-making tool developed by the AMA to connect physicians, care team members, business liaisons and entrepreneurs in a shared mission to enhance healthcare. The collaboration extends the reach of the Physician Innovation Network to all startup founders in the Microsoft for Startups Founders Hub, so whether they’re driven to improve healthcare, collaborate with industry leaders or learn from healthcare experts, they will have access to a unique space for connection and innovation.

Without a doubt, these are incredibly exciting times, and we are proud to see our customers and partners adopting Microsoft’s generative AI solutions and putting them to use in the real world to make a meaningful impact in the lives of clinicians and patients.  We look forward to continuing to play a leading role in fostering innovation with generative AI, and empowering healthcare providers and partners across the entire health and life sciences industries with leading-edge and responsible AI technologies that contribute to better experiences and outcomes in healthcare.

[i] IDC InfoBrief, sponsored by Microsoft, The Business Opportunity of AI: How Leading Organizations Around the World Are Using AI to Drive Impact Across Every Industry, IDC #US51364223, Nov. 2023.

[ii] IDC Resource Map Document: IDC Business Value of AI Survey, sponsored by Microsoft, IDC #US51331223, Nov. 2023.

Tags: AI , healthcare , Microsoft Copilot , Microsoft Fabric , Microsoft for Startups , Responsible AI

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case study of health care in iot

New Graduate Nurse Transition into Rural Home: A Case Study

Affiliation.

  • 1 Laurie Generous MN, BScN, RN, is a Clinical Nurse Specialist, Island Health, Victoria, British Columbia.
  • PMID: 38437043
  • DOI: 10.1097/NHH.0000000000001241

The global shortage of nurses and high attrition rates for newly graduated nurses along with the shifting demand for home care has created a critical need for retention strategies that address the specific challenges of rural settings. The effectiveness of structured transition or mentoring programs are primarily studied in acute care settings, making it difficult to translate to the unique context of rural home care nursing. The complexities of the independent nature of home care nursing practice and limited resources to address transition shock make it difficult to successfully transition newly graduated nurses to rural home care. A case study supports mentorship facilitation as a readily available, effective strategy that can overcome the challenges of rural home care settings. A comparative analysis will link Duchscher's (2008) transition shock theory to mentorship as an effective strategy for supporting NGNs' transition in home care nursing. Recommendations offer rural care leaders practical strategies bundled with mentorship to optimize the successful transition and retention of newly graduated nurses in their workplaces.

Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.

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Follow our news, recent searches, caregivers face not only burden, but reward and benefits in their journey: study, advertisement.

A total of 278 caregivers took part in the Duke-NUS study, which also identifies the different types of caregivers.

An elderly caregiver in Singapore. (Photo: Calvin Oh/CNA)

This audio is AI-generated.

case study of health care in iot

Calvin Yang

SINGAPORE: One in five caregivers in Singapore find meaning in looking after their loved ones, despite facing a high caregiving burden , according to a Duke-NUS Medical School study.

As Singapore faces an ageing population, supporting caregivers has become vital . 

The wide-ranging study hopes to better support caregivers by understanding their different needs, which can help the sector to come up with more targeted assistance. 

FINDING PURPOSE IN CAREGIVING

Findings on the extent of burdens and benefits of caregiving were shared on Wednesday (Mar 13) during a family caregiver symposium. 

The event was organised by the Duke-NUS Medical School’s Centre for Ageing Research and Education (CARE) and Tsao Foundation, an organisation working with ageing issues.

“Burdens can be in various facets. It could be psychological burden, it could be physical burden, it could be financial burden, or burden in the form of lost social relationships and lost family ties,” said Dr Rahul Malhotra, deputy director and head of research at the Duke-NUS Medical School’s CARE.

“Benefits are mostly psychological, in the terms of satisfaction and the growth that one perceives as an individual when you provide care to a loved one.”

A total of 278 caregivers took part in the Duke-NUS study, which also identifies the different types of caregivers. 

Dr Malhotra said the research is “relatively new, compared to the disproportionate focus on the burden of caregiving”. 

case study of health care in iot

Caregivers also value good feedback, and affirming them is crucial in their care journey, he noted. 

“It's important for families, clinicians and other people who interact with caregivers to not only provide instrumental support or help with tasks, but also the emotional support to caregivers, validate their efforts, affirm their efforts, (and) give them a word of praise,” Dr Malhotra told CNA’s Singapore Tonight on Wednesday. 

“Small things like these do matter, and increase the affirmation and validation that caregivers get from something that we take for granted otherwise.”

SUPPORTING CAREGIVERS 

The research is being translated into innovative interventions by the Tsao Foundation. 

Ms Susana Harding, senior director of the International Longevity Centre at Tsao Foundation, said the study shows that caregivers are motivated and find purpose in doing so. 

case study of health care in iot

Flexible work arrangements, paid leave among items on caregivers’ wishlists

case study of health care in iot

Commentary: How many of those with mental health conditions were caregivers themselves?

“For the caregivers, there are burdens, but there are actually benefits and rewards in the caregiving journey, throughout the caregiving journey,” she added. 

“There is a sense that they're able to manage, cope with the everyday caregiving needs, anticipate future challenges, and that they're able to do self care of their own well-being and work towards a balanced life.”

Previously, the focus of caregiving has always been about the burden that they face, said observers. 

Ms Harding said: “It was always about the stress. But now with this study, we're able to also identify that there's the positive side to caregiving.” 

She added that the project can also help to change their mindset of clinicians, so they can get to know the caregivers early on. 

On how this study can help Singapore plan better, Ms Harding said it can be used to engage stakeholders such as policy makers to pay more attention “on the caregivers, not just always the care recipients”, and how support could be channelled to help caregivers identify their aspirations and needs early on and amplify the rewards. 

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A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective

Hadi habibzadeh.

Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203

Karthik Dinesh

Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627

Omid Rajabi Shishvan

Andrew boggio-dandry, gaurav sharma, tolga soyata, associated data.

The algorithms described in the previous subsection require a significant amount of well-structured clinical data to achieve useful levels of accuracy. Providing such data presents multi-faceted challenges, specifically, with respect to widespread data availability and heterogeneity.

A major data-related challenge in clinical HIoT is assuring the generality of results. Most available pilot studies in the literature are developed and tested on small datasets (relative to the actual patient population size) [ 180 ]. Therefore, their applicability and efficacy in real-world scenarios remain uncertain; especially, considering that health and disease states can be highly correlated with many personal parameters such as gender, age, and ethnicity. This problem is even more acute for rare diseases. Consequently, data analytics approaches have the best chances for success if clinics and hospitals share their data with algorithm developers. The flow of such information is, however, fraught with unresolved legal and ethical complications. An example of this is the collaboration between Google’s DeepMind and the British healthcare provider, Royal Free, which shared its acute kidney injury dataset with DeepMind without acquiring patients’ explicit consent. The decision led to official scrutiny, which concluded that the deal was illegal [ 181 ], [ 182 ].

To take full advantage of the existing medical knowledge, it is common sense to use historical medical data as input to inference algorithms. Combined with the data that is being streamed from the HIoT platforms, this amalgamation of data can provide an effective data source for modern inference algorithms. However, historical data sources are diverse and highly unstructured as part of the data must be inferred from EHRs, insurance claims, medical textbooks, and scientific papers [ 183 ]. The language used in these documents can be vague, out of chronicle order, and incomplete. To cope with these challenges, NLP is often used. However, regardless of their remarkable progress in recent years, current NLP technologies are not efficient enough in these applications, even when supported by the resources of tech giants [ 180 ].

This data heterogeneity problem is exacerbated when we consider the fact that even the data obtained from the same HIoT platform can be highly-heterogeneous within itself. For example, the MC10 sensors used in our case study continuously evolve and additional clinically relevant attributes can be measured with every new generation of sensors, which effectively adds a new ”input dimension” to the existing data. Therefore, algorithms must be designed to cope with not only an increasing volume of data, but also an increasing variety.

In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.

I. INTRODUCTION

A strong synergy exists between the technological advances in Internet of Things (IoT) and the emerging needs and directions of healthcare applications. With a rapid expansion in the deployment of IoT devices and increasing desire to make healthcare more cost-effective, personalized, and proactive, IoT is poised to play a strong role in all aspects of health management, and for our discussion, we refer to this important segment of IoT as the Healthcare Internet of Things (HIoT). HIoT can be broadly classified into two sub-categories: personal and clinical. Personal HIoT includes devices such as activity/heart rate trackers, smart clothes and smartwatches (e.g., Fitbit [ 1 ], Apple watch [ 2 ]) that are used by consumers for self-monitoring. These general purpose devices are not strictly regulated and are intended for use by consumers without involvement/guidance from physicians. Clinical HIoT devices are built specifically for health monitoring under the guidance —and with the involvement— of a physician. Examples include smart continuous glucose monitors [ 3 ] and connected inhalers [ 4 ]. These devices are intended for use in either clinical or home environments and are strictly regulated and approved for use only after clinical validation. This paper surveys the emerging field of clinical HIoT.

A confluence of social and technological trends is motivating the adoption of IoT in the clinical setting. On the social side, aging populations in the West are straining clinical institutions and healthcare costs are rising at significantly higher rates than baseline inflation [ 5 ]. On the technology side, most healthcare institutions are already connected to the internet and are, therefore, well poised to take advantage of the increasing availability of high bandwidth connectivity, inexpensive cloud storage and computation, and large-scale data analytics. HIoT technologies are attractive in this emerging scenario because they allow personalization of clinical healthcare enabling not only significant cost reductions but also improved outcomes through higher responsiveness, customization, and effective exploitation of aggregated data. HIoT can reduce the time taken to diagnose a health condition [ 6 ], provide efficient high-quality care, and help to reduce hospitalization costs [ 7 ] as well as the chance of readmission for the same health issue [ 8 ], [ 9 ]. Being connected with HIoT enables patients to provide continuous feedback to the doctors and monitor their own progress, which enhances patient engagement and satisfaction. Rich collections of longitudinal data from heterogeneous sources, made possible by HIoT adoption, also opens up new avenues for augmenting traditional diagnostic approaches employed by physicians. Specifically, data analytics can automatically flag physiological anomalies for further investigation and visualization technologies can summarize salient trends, without cognitively overloading physicians and interfering with their patient interactions in the clinic.

Several recent surveys address specific aspects of HIoT systems. Medical IoT devices focusing on personalized healthcare systems [ 10 ], and applications with toddlers/kids [ 11 ] have been reviewed. Applications of remote health monitoring systems such as body temperature monitoring and elderly care [ 12 ], and applications focusing on healthcare solutions using smartphones, ambient assisted living (AAL), and wearable devices have been studied in detail [ 13 ]. HIoT applications in rural healthcare focusing on improving healthcare in developing countries have also been reviewed [ 14 ]. Other survey papers include studies focusing on communication [ 15 ], security, and data privacy aspects of HIoT systems [ 16 ], [ 17 ]. HIoT in smart homes [ 18 ], high-risk environments, and safety industries [ 19 ], and HIoT based services for mental health [ 20 ] have also been the focus of several reviews. Distinct from these prior surveys, the present paper focuses particularly on —and provides end-to-end coverage for— the clinical side of HIoT. Additionally, the ideas discussed are concretely illustrated throughout the paper by using an example from the clinical setting, specifically from neurology.

The rest of the paper is organized as follows. Section II presents a discussion of trends, challenges, and application requirements that IoT adoption in the clinical settings faces. To motivate the subsequent discussions, example IoT-based health management applications are presented in Section III . This section also introduces a case study from the neurology field, which is used as a running example throughout the paper to concretely highlight the concepts presented in the paper. In Section IV , the overall architecture of a health management system is presented and its components, data sensing, acquisition and communication, aggregation, and pre-processing are further discussed in Section V and Section VI , respectively. Analytics and inference in healthcare applications are surveyed in Section VII , followed by a discussion of medical data visualization techniques in Section VIII . Section IX investigates security and privacy considerations of HIoT. Finally, Section X concludes the paper with our vision for the future.

II. TRENDS, APPLICATION DEMANDS, AND CHALLENGES

The majority of prior surveys on smart healthcare limit their discussion to specific aspects of the field such as sensing [ 21 ], [ 22 ], communication [ 23 ]–[ 25 ], data processing [ 26 ], and security [ 27 ]. Taking full advantage of the smart healthcare concept is contingent upon understanding the synergy of multiple mega-trends happening concurrently in the smart healthcare ecosystem [ 28 ] and how these trends affect the clinical practice of medicine and healthcare. In this section, we first highlight some of the major technological and societal trends that are driving HIoT adoption and then summarize the demands and challenges of HIoT applications.

A. Technological and Societal Trends

Data acquisition.

Unobtrusive, inexpensive, and accurate sensors are the work-horses for smart healthcare systems. Such sensors can replace the current practice of in-clinic sporadic sensing with continuous monitoring. Although the problem of building ideal smart healthcare sensors is far from being solved, several recent advances in sensing technologies have alleviated many of the existing challenges. Continuously diminishing feature sizes, integrated circuits have reduced the physical dimensions and power consumption of on-chip sensing devices while offering impressive computational capability.

Ambient energy harvesting proposals enable practical in-vivo sensors that no longer require a battery [ 29 ], [ 30 ]. Radio Frequency-based (RF-based) ambient sensors are working toward the measurement of multiple biomarkers such as respiration [ 31 ], [ 32 ], heartbeat [ 33 ], and motion detection [ 34 ], [ 35 ], although practical designs are still an active research area.

The first generation of personal health monitoring devices, such as smartwatches [ 2 ], has addressed several system integration issues and a software-user ecosystem has emerged that can be re-purposed to effectively integrate HIoT into clinical healthcare to enable better-informed decisions and care. In an important development, illustrated in Fig. 1 , such data is also being complemented by data collected from alternative sensors —that are not necessarily designed for healthcare applications, e.g., ambient sensors— can be fused with information from personal health monitoring devices to provide context awareness .

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Modern smart healthcare applications are intricate multidimensional systems that not only focus on the personalized acquisition of physiological data but also incorporate information from various external sources such as past records of patients from their hospitals, research and educational resources, and even environmental information from smart city applications.

Data Communication

Low-delay, high-throughput, and low-power communication is an integral requirement of many smart healthcare applications. Two factors are crucial to the evolution of HIoT: (i) hierarchical network structuring (e.g., using cloudlets [ 36 ]) (ii) and the maturity of Wireless Body Area Networks (WBANs) [ 37 ]. A complete review of healthcare communication technologies can be found in [ 15 ].

Data Processing

By generating an unprecedented volume of information, burgeoning IoT services have significantly contributed to the big data phenomenon. Advances in parallel computation architectures, such as Graphics Processing Units (GPUs), promise to substantially reduce the amount of time necessary to perform sophisticated computations on acquired data. Furthermore, advances in data analytics and inference promise to extract information that can open the door to new cures for diseases and drastically improve diagnostic quality by providing superior decision support to healthcare professionals. ML algorithms have specifically made it feasible to predict the onset of fatal incidents such as seizures and heart attacks [ 38 ]. The impact of these advances has reached far beyond data processing; for example, signal processing and ML algorithms can now be used as an effective defense against noise [ 39 ], [ 40 ], offsetting the imperfections in data acquisition and communication.

Security and Privacy

Security and privacy considerations in smart healthcare systems have long been overshadowed by the design objectives of other system components; application-oriented services often trade off security and privacy considerations for shortened design time. However, the focus is returning to security and privacy in the wake of a surge in large scale cyberattacks targeting a vast range of IoT services. It is now evident that the lack of cybersecurity in different components of smart healthcare (and other IoT applications) is manifested as multi-faceted security flaws with ramifications ranging from privacy violations to endangering patients’ health. Consistent implementation of security and privacy preservation measures is now the primary trend in the field, as it is now known that one-dimensional communication security protocols do not provide immunity against complicated attacks. Researchers are constantly working to enhance the security of data acquisition (in areas such as countermeasures against physical attacks, side channel attacks, and malicious firmware overrides [ 41 ]) and data processing (in areas such as cloud-oriented data privacy and security).

Societal Trends

In parallel to the aforementioned technological advances, emerging societal trends are also driving HIoT adoption. These technological and societal developments are indeed the systole and diastole of modern healthcare infrastructure; the former assures feasibility and practicality, while the latter promotes HIoT from a mere nicety to an absolute necessity. At a societal level, the requirement for personalized and continuous healthcare is chiefly fueled by worldwide population aging [ 42 ]–[ 46 ], which is expected to further increase ever-soaring healthcare expenses. While the impact of aging populations is being felt in most societies, developed countries are facing this most urgently.

In parallel to these demographic changes, two additional societal trends further drive the demand for HIoT. The first of these is the expansion of middle-class families worldwide [ 47 ], particularly in countries such as China, India, and Brazil. With education and continuous access to the information resources of the Internet, this technologically savvy class is becoming more and more cognizant about personal healthcare. This directly translates to a growing market for a variety of devices spanning from smartphone-based services to smart homes, smart wearables, Air Quality (AQ) monitoring, etc. A second trend involves the increasing concentration of physicians and medical care facilities in large urban areas leaving sparse coverage in large geographically spread rural areas. In these latter settings, HIoT technology can effectively expand the geographic footprint of clinics and provide effective remote health management solutions.

B. Application Demands and Challenges

While HIoT systems should be specifically designed to fulfill the requirements of their target application, system designers should be aware of the limitations that will act as a barrier to proper functionality. We now highlight some of the most common demands and restrictions of HIoT devices, which may or may not apply to every possible application.

Physiological and Environmental Signals

The first demand of any HIoT system is determining the type(s) of physiological/environmental signals required for its intended application(s). A healthcare management system inherently requires a certain level of accuracy for acquired signals, and clear boundaries for the noise imposed on those signals [ 48 ]. Some of the major physiological attributes used in more common applications are shown in Fig. 1 , though it should be noted that different applications generally require various types of signals and data.

Decision Support

Decision support plays a crucial role in an HIoT management system. The data collected from various sources should be analyzed by the machine and presented to healthcare professionals in a comprehensive format, making the machine a support tool for the professionals [ 49 ]. The type(s) of decision support provided by an application can vary based on its purpose. For example, an application may require an automated warning system that issues an alert on critical conditions. This alert may be issued to the healthcare organization (HCO), to the doctor, to the patient’s caregiver, or directly to the patient.

Latency Tolerance

Latency tolerance can also affect the design of a system. Applications that deal with patients in critical condition, who need constant real-time monitoring, must be able to issue alerts with minimal delay [ 50 ]. Other applications targeted for less critical conditions, however, may be able to tolerate higher delay.

Computational Intensity

The volume of data gathered by the system requires a proportional amount of computation power to analyze it. Factors such as the number of sensors, signal sampling frequency, sample accuracy, and overhead imposed by encryption schemes all affect the computational power necessary for the system [ 51 ]. In addition, applications requiring lower latency need higher computational capabilities. Machine learning algorithms used in a system may also impose additional computational intensity, independent from the absolute volume of data; for example, two algorithms working on the same input data may require different computational intensity based on their nature.

Power Consumption

The sensors used to gather physiological signals are generally wearable, meaning they will be powered by batteries. Optimizing power consumption to extend battery life (using both hardware configurations and energy-aware algorithms [ 52 ], [ 53 ]) is a necessary step toward ensuring continuous signal acquisition and monitoring.

Data Communication Rate

In most HIoT systems, physiological signals are transmitted to a local concentrator through a WBAN [ 54 ]–[ 57 ]. The communication link between the WBAN and the concentrator has a bandwidth restriction, imposing a limit on the amount of data acquired per unit time. Some pre-processing may need to be performed on the acquired data before transmission to a local concentrator. and, in some cases, the size of the data after pre-processing can be greater than when it was transmitted from the WBAN (e.g., some encryption schemes impose a significant overhead).

III. HIOT EXAMPLE APPLICATIONS

We now highlight some common HIoT management applications and introduce a case-study that we will use for highlighting the relevance of HIoT in the clinical healthcare setting.

Activity Recognition

Activity recognition is prevalent in various areas of the healthcare domain, where multiple techniques are used for this purpose such as computer vision [ 58 ], active sensor beacons, passive radio-frequency identification (RFID) [ 59 ]–[ 62 ], WiFi [ 63 ], radar [ 64 ], etc. Most traditional activity recognition platforms, however, suffer from a high rate of false-positives when detecting abnormal activity. Applying learning techniques, such as Support Vector Machines (SVMs) [ 65 ] or dictionaries [ 66 ], can help mitigate this problem. Some examples of activity detection applications include: fall detection [ 34 ], [ 67 ], [ 68 ], fitness tracking [ 69 ], [ 70 ], human localization and tracking [ 71 ], [ 72 ], posture recognition [ 73 ], and gait abnormality detection [ 74 ], and multiuser activity recognition [ 75 ].

Stroke Rehabilitation

Rehabilitation among stroke patients is an important task that has been studied in many aspects. Recent trends have increasingly turned to self-managed rehabilitation [ 76 ]. Providing a virtual environment for patient rehabilitation [ 77 ], [ 78 ], predicting the strength of muscles based on kinematics [ 79 ], and monitoring a patient’s activities to provide feedback and assess the patient’s recovery process [ 80 ] are all examples of how HIoT can help with stroke rehabilitation.

Blood Glucose Monitoring

According to the Centers for Disease Control and Prevention (CDC), approximately 30.3 million people (or 9.4% of the US population) across all ages are currently living with diabetes [ 81 ]. Blood glucose monitoring can be especially useful for diabetic patients, as it can provide important information related to managing the disease [ 82 ]. In addition to regular monitoring, IoT devices can issue warnings in extreme/dangerous cases [ 83 ], provide suggestions on adherence to treatment regimens [ 84 ], and even help track patients’ meals or make healthy eating suggestions [ 85 ].

Cardiac Monitoring

One out of three deaths in the US are associated with cardiovascular disease, making it the leading cause of death in the nation [ 86 ]. Personalized and continuous cardiac monitoring (as opposed to conventional in-hospital monitoring) plays an integral role in lowering the fatality rate and associated expenses of cardiovascular diseases [ 87 ]. Existing studies investigate the efficacy of in-vivo and exvivo sensors in prediction and detection of various cardiac hazards such as arrhythmia [ 88 ], [ 89 ], long QT syndrome [ 90 ], and sudden cardiac arrest (SCA) [ 91 ]. These efforts have resulted in the recent emergence of noninvasive Food and Drug Administration (FDA)-cleared cardiac monitoring systems that can help diagnose a variety of heart rate variations (HRV)-related syndromes [ 92 ]. Regardless of their increasing commercial popularity, cardiac monitoring devices have recently been subject to scrutiny due to their potential security and privacy flaws. New regulations are expected to emerge to enforce more strict requirements on these systems [ 93 ].

Respiration Monitoring

Patient respiratory activity is a clear indicator of their overall health. Respiration monitoring devices are categorized as either contact (most common [ 94 ]) or non-contact [ 95 ]. Monitoring newborns for sudden infant death syndrome [ 96 ], monitoring the effects of medication(s) on respiration [ 97 ], and asthma patient monitoring [ 98 ] are all examples of IoT respiration monitoring devices.

Sleep Monitoring

Monitoring patients during sleep is another useful implementation of IoT devices. Some applications include monitoring and classifying sleep stages [ 99 ], monitoring vital signs during sleep [ 100 ], and detecting sleep disorders such as obstructive sleep apnea [ 101 ], [ 102 ].

Blood Pressure Monitoring

High blood pressure currently impacts approximately 45.6% of US adults [ 103 ] and, as a result, is a crucially-important health concern to address. IoT-based general hypertension monitoring systems are already in use [ 104 ], while some applications can even take control of hypertension decision-making processes [ 105 ]. Other blood pressure related issues, such as hypotension (low blood pressure), can also be monitored by IoT devices [ 106 ].

Stress Monitoring

Many systems have been developed to monitor various types of stress, and even intervene if necessary. While low-stress levels are normal, high-stress levels can lead to serious health issues [ 107 ]. Some applications attempt to reduce stress by offering suggestions on a mobile device [ 107 ], [ 108 ], monitoring post-traumatic stress disorder patients [ 109 ], and helping people on the autism spectrum manage their emotions [ 110 ].

Medical Adherence

Ensuring that patients adhere to their healthcare/medicinal regimen is a grand challenge in healthcare [ 111 ], [ 112 ]. Monitoring adherence for elderly patients [ 113 ], people with dementia [ 114 ], or general medical adherence monitoring [ 115 ]show how IoT devices can help ensure that regimens are followed properly.

Alzheimer’s Disease (AD) Monitoring

AD affects 5.3 million people in the US and incurs an estimated annual cost of $200 billion [ 116 ]. AD patients generally require constant, round-the-clock care. IoT-based devices can provide considerable assistance to caregivers in many areas, such as early detection of dementia [ 117 ], reporting anomalous activities [ 118 ], monitoring patient location, and providing task reminders [ 119 ].

Parkinson’s and Huntington’s Disease (PD/HD) Monitoring (Case Study)

Parkinson’s and Huntington’s are neurological diseases characterized by movement disorders. It is estimated that more than 900,000 individuals in North America will suffer from PD by 2020 [ 120 ] while HD currently affects more than 20,000 individuals in the US [ 121 ]. Body worn HIoT sensors provide an effective mechanism for monitoring the movement symptoms associated with PD/HD and are an promising option for assessing disease status, progression, and medication efficacy.

To provide concrete examples of the ideas discussed throughout the paper, we use a PD/HD case study from our recent and ongoing research [ 124 ], [ 125 ]. Motivating background information on these disease conditions and the related demands and challenges are summarized here. Subsequent sections in the paper highlight examples of relevant components from the case study through brief remarks.

Typical symptoms of PD/HD are depicted in Fig. 2 . Parkinson’s is characterized by rest tremors, slowness in movement, rigidity, and postural instability. Huntington’s is a genetic disease marked by jerky movements in the body (referred to as chorea), unsteady gait, and cognitive impairments [ 126 ]. Both diseases are progressive; after onset, patients encounter increasingly severe symptoms as time advances.

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Graphics showing typical symptoms of Parkinson’s (left) and Huntington’s (right) disease that are the focus of the multisensor case study that we will use to illustrate the ideas discussed in this paper. Based on [ 122 ], [ 123 ].

PD and HD are currently considered incurable. Although medications can be used to manage symptoms, their effect is neither universal nor complete. The overall quality of life is often severely degraded for PD/HD subjects. Disease progression and efficacy of medications are currently assessed subjectively by physicians via in-clinic-tests that assign patients a score on the Unified Parkinson’s Disease Rating Scale (UPDRS) [ 127 ] or the Unified Huntington’s Disease Rating Scale (UHDRS) [ 128 ]. These ratings are subject to variability because they are based on sporadic observations that sample relatively short durations of time and because of inherent variability in human assessments. There is, therefore, a strong desire to develop sensor-based quantitative measures and scales that can be used to objectively assess disease progression and efficacy of treatments. This clinical application is ideally suited for HIoT because miniaturized unobtrusive sensors for movement have become ubiquitous and cheap due to the smartphone revolution, as have circuits for low power wireless communication and Internet connectivity infrastructure.

IV. SYSTEM ARCHITECTURE

The realization of inexpensive, unobtrusive, and reliable systems that can meet the requirements discussed in Section II – B necessitates a robust and inclusive design framework. To this end, various HIoT system architectures have been discussed in the literature [ 129 ]–[ 133 ]. In this section, we investigate the general architecture of typical HIoT systems [ 10 ] (as seen in Fig. 3 ) and outline each of the layers. Each layer is then discussed in more detail in subsequent sections.

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High-level system architecture illustrating HIoT integration into clinical healthcare. IoT sensors record measurements for a range of physiological and health-related physical attributes. The data is communicated over the network and aggregated in the cloud by IoT concentrators. Cloud-based analytics and inference algorithms operating on the data provide decision support to physicians via visualization interfaces, dashboards, and real-time alerts to individual users. Security and privacy solutions must be implemented to include other components, ensuring data protection from acquisition to storage.

Data Acquisition, Sensing, and Transmission

The first HIoT component is data acquisition, where IoT devices and sensors measure physiological and environmental signals. These devices are connected to a WBAN, generally through an intermediate data aggregator such as a smartphone [ 36 ]. The primary function of the sensors is to sense and gather data, but many are also now able to preprocess data before transmission. Data acquisition is further detailed in Section V .

Data Aggregation/Preprocessing Cloudlet

The data gathered in the acquisition and sensing layer are transmitted to an IoT concentrator through a WBAN (using mediums such as WiFi, Bluetooth, or ZigBee Standard). These concentrators are responsible for gathering all the sensed data and transmitting it to the HCO data center within the delay tolerance requirements of the application. To fulfill this task, concentrators need to be more computationally capable (compared to devices in the previous layer) and must have a certain amount of local storage for data. We detail data aggregation and preprocessing in Section VI .

Cloud Processing and Storage

Once the data arrives at the HCO data center, it can be processed using advanced algorithms to provide decision support including data summarization and creation of visual representations. Since the cloud processing and storage layer is responsible for various tasks, we discuss it in multiple sections. Analysis of data using ML algorithms to extract useful insights is studied in Section VII , and data visualization is explored in Section VIII .

Privacy, Security, and Quality of Service (QoS) Management

As highlighted in Fig. 3 , the entire system must provide robust security and privacy guarantees while satisfying the QoS requirements of applications. Overemphasizing security within a single component (typically communication) does not translate to improved overall security because adversaries can exploit any susceptibility to infiltrate not only the HIoT system but also other connected services. The implementation of such an inclusive security structure, however, is challenging. Aside from the common limitations of IoT applications (such as meager energy budget, on-site deployment, and large-scale), HIoT services are subject to domain-specific complications, such as, the inherent sensitivity of health data, highly dynamic roles of stakeholders, heterogeneity in electronic health records (EHRs), and potentially grave consequences from failures. Section IX further lays out the details of HIoT security.

V. DATA ACQUISITION AND SENSING

In this section, we review some of the most commonly used sensors and investigate their invasiveness, cost, and accuracy.

A. Activity Detection Sensors

Activity detection is typically conducted through Inertial Measurement Units (IMUs), which are composed of multiaxis accelerometers, gyroscopes, magnetometers, and force sensors. IMUs can be implemented as wearable sensors [ 134 ] or sensors integrated into the environment [ 135 ]. Wearable sensors are typically inexpensive and can cater to a broad range of applications; multiple wearable sensors can easily be deployed to determine the relative position of body parts [ 134 ]. In some cases, wearable sensors are inconvenient for long-term use as their bulk and weight, coupled with their requirement to be in close proximity to the patients, impede the acquisition of continuous and real-time data. In these situations, ambient sensing techniques can be employed (e.g., cameras and RF sensing). Cameras are relatively easy to set-up and employ, as they do not require extensive changes to the environment. They, however, suffer from limited line-of-sight and pose privacy concerns.

B. Respiration Sensors

Respiratory motion can be captured using accelerometers or piezoelectric materials. The solid-state nature of piezoelectric sensors enables them to be reliable, small, low-power, and non-invasive. Authors in [ 136 ] demonstrate a complementary metal-oxide-semiconductor (CMOS) sensor for respiration monitoring that is implemented using a polyvinylidene-fluoride (PVDF) material to ensure bio-compatibility. The sensor consumes up to 800 μ W and, since direct contact with the skin is not required, can be embedded inside a jacket or worn around the chest. Alternatively, camera-based sensing solutions apply image processing algorithms to videos, capturing patients’ respiratory motions. Instead of tracking the slight motions of the torso (which has been demonstrated to be a demanding task), the majority of the proposed works rely on analyzing variations in the ambient light caused by body movement [ 137 ], [ 138 ].

C. Heart Beat Monitoring Sensors

Generally, heartbeat monitoring techniques are classified into four groups: (i) electrocardiography (ECG) (ii) ballistocardiography (BCG) (iii) phonocardiography (PCG), and (iv) photoplethysmography (PPG).

Holter devices [ 139 ] are common tools that capture ECG data through electrodes over long periods of time with various sampling frequencies. Non-contact, capacitive-coupled electrodes are also commonly used for ECG signal acquisition as they do not require direct contact with the skin, enabling their use as wearable sensors embedded in clothing [ 140 ]. These less invasive methods mean trading accuracy for comfort/wearability. While BCG signals can be captured in numerous ways, accelerometers are typically the primary choice [ 141 ]. Accelerometers can be used as wearable sensors if worn in proximity to the heart. While PCG signals are typically captured by microphones, piezoelectric sensors installed on the throat [ 142 ] and fiber optic sensors [ 143 ] can also be used. Finally, due to low-cost, non-invasiveness, and high reliability, pulse oximeters are generally used to capture PPG signals. Various physiological parameters, including Heart Rate Variability and even ECG signals, can be extracted from PPG signals [ 144 ]. Pulse oximeters, however, are not suitable for long-term continuous monitoring as they are typically worn on a fingertip.

D. Blood Pressure Sensors

Authors in [ 145 ] propose a wireless and battery-less sensor to be surgically implanted in the femoral artery to monitor hypertension patients. A second, bulkier device needs to be worn by the patient to power and interrogate this in-vivo sensor. Although invasive, the proposed solution is comfortable and suitable for long-term monitoring. Less intrusive techniques, which operate based on fundamentals of wave propagation dynamics in fluids, are also proposed in the literature. These techniques typically measure Pulse Wave Velocity (PWV) or Pulse Transit Time (PTT) to calculate blood pressure (BP). Data samples are collected using cuffless PPG, ECG, and Impedance Cardiography (ICG) sensors, which can be used as wrist bands and/or be worn around the chest [ 146 ], [ 147 ]. Relying on PTT poses additional challenges to BP monitoring; one challenge arises from the dependence of PTT on heart rate, age, gender, and body shape of the patients. These obstacles, however, can be partially addressed by employing suitable signal processing solutions [ 147 ], [ 148 ].

E. Blood Glucose Monitoring Sensors

Blood Glucose Monitoring (BGM) can be categorized into two different classes: (i) electrochemical-based and (ii) optical-based. The former analyzes the chemical content of interstitial fluids, while the latter involves spectroscopy techniques. Electrochemical sensors are typically more accurate but are more invasive. Common models of electrochemical sensors require blood samples, typically obtained from the fingertips of patients. Electrochemical-based sensors can also be implanted underneath the skin [ 149 ]. Because of the correlation between glucose level in sweat and blood, less invasive monitoring can be implemented by deploying flexible, small, and stretchable sensing patches that analyze sweat samples [ 150 ].

Spectroscopy is widely used as an alternative technique to provide a less invasive but less accurate BGM solution. Chemical composition of the blood (including glucose level) changes its ability to absorb, reflect, and scatter light beams. Spectroscopy uses this to estimate the blood glucose level. Therefore, optical-based spectroscopy sensors typically encompass a Near Infra Red (NIR) light source and a photon counter, which are installed on opposite sides of the tissue [ 151 ]. Table I provides a summary of aforementioned sensing technologies along with their primary strengths and shortcomings.

A list of commonly used sensors in various clinical HIoT applications. The qualities listed under ‘characteristics’ are relative. For example, for activity detection applications, RF-based sensing typically yields lower accuracy than wearables. Hence, it includes the ‘low accuracy’ attribute. This should not imply that RF accuracy is not practical.

F. Wearable Multisensors in the PD/HD Clinical Study

The PD/HD case study, outlined in Section III provides an excellent example of how multiple sensing modalities can be combined in one sensor. In this study, BioStampRC sensors from MC10 Inc. are used, which are lightweight (≈ 7 grams) and unobtrusive devices capable of operating in multiple modalities. The sensor (specifications shown in Table II ) operates with various sampling rates and dynamic ranges with high-power and long-duration recording capabilities. The sensors are applied to subjects’ body at five different locations as depicted in Fig. 4 . We primarily utilize the sensors’ accelerometer to obtain data, sampled at 31.25Hz, over the duration of 46 hours.

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A graphic showing five different locations on the body for applying BioStampRC sensors in our PD/HD case study [ 152 ] (left) and a participant wearing sensors at these locations for in-clinic assessment [ 125 ] (right).

C apabilities of the B io S tamp RC sensor from MC10, I nc. used in our case study . T he sensor operates in 6 modes . g indicates the acceleration due to gravity (9.81 m /s 2 ).

VI. DATA COMMUNICATION, AGGREGATION, AND PRE-PROCESSING

In this section, we investigate two main aspects of HIoT communication: (i) connectivity, and (ii) data aggregation.

A. Data Communication

A WBAN consists of multiple low-power, resource-constrained devices that are connected to a more computationally capable device, such as an Access Point (AP), through a low-range and low-rate wireless link. The AP performs multiple pre-processing , data aggregation , and data-fusion operations on the collected data. Most importantly, it provides Internet connectivity. Broad trends in communications for HIoT have been surveyed in [ 15 ]. The majority of implementations in healthcare applications rely on either BLE [ 153 ]–[ 155 ] or ZigBee [ 156 ], [ 157 ].

BLE specifically targets low-rate, low-power, and low-range IoT communications [ 158 ]. It operates in the 2.4 GHz Industrial, Scientific, and Medical (ISM) frequency band, and can provide up to 2 Mbps bandwidth. To suppress the adverse effects of interference and fading, the protocol leverages adaptive frequency hopping techniques. Many existing portable devices such as smartphones and laptops are shipped with embedded BLE modules [ 159 ]. BLE, however, does not provide multicast communication, which can be crucial to many applications. Furthermore, as it only supports single-hop star topology, it cannot be adopted by multi-level hierarchal architectures. This limits its scalability and raises security concerns [ 158 ].

ZigBee is developed atop the IEEE 802.15.4 standard. It is designed for low-power, short-range, and low-rate data connectivity. Unlike BLE, ZigBee supports mesh architecture, which results in more robust implementations. Furthermore, it supports multicast [ 158 ]. Table III provides a comparison between ZigBee and WiFi in the context of smart healthcare applications.

A comparison of two commonly used communication protocols for WBAN implementation, along with with their advantages and disadvantages within the context of smart healthcare.

Note that the composition of this network should be defined prior to the application and verified after the setup. In our PD/HD case study, the number of sensors, their locations on the body, recording modes, and sampling rates are determined through the Investigator application before the network setup and their functionality is verified after the sensors are attached.

B. Data Aggregation and Pre-processing: Front-end

For data aggregation and pre-processing, an aggregator (typically added as a functionality of the AP) is used to collect and combine all sensed data before transmission. This step also includes performing preliminary computations on the data. This concept has seen more interest recently, especially with the introduction of fog computing [ 165 ]. Fog computing provides multiple benefits including low latency in some applications (more critical in emergency medical situations) [ 166 ], mobility support and location awareness [ 167 ], and reduced power consumption by replacing cloud communication with local computation (as communication consumes orders-of-magnitude more power than computation) [ 168 ].

Other roles and benefits of this stage include condensing data from multiple sensors into single packets to reduce communication overhead, removing redundant data that do not provide useful information for the system, and representing data from multiple sources using a single value (such as their arithmetic mean or median). For example, in a 12-lead ECG data acquisition system, it is shown that the same intervals from different leads can be median filtered to provide a final, single value, thereby reducing data volume by a factor of 12 [ 90 ]. Additionally, in many applications, data from many different sources complement each other to provide a bigger and better picture of the situation. The aggregation process keeps data from all sources in sync with each other so that the concurrency of events recorded by different sensors are maintained. The aggregation and pre-processing stage tends to lose information.

C. Data Aggregation and Pre-processing: Back-end

Many HIoT applications resort to cloud-based solutions not only to circumvent the challenges of processing large data volumes but also to take full advantage of cloud’s compatibility with off-the-shelf data analytics, always-on property, scalability, and affordability [ 169 ]. Particularly, the cloud can provide permanent data storage services (ultimate data aggregation), which are the basis of data analytics and inference. Long-term data storage is also a prerequisite for history-based verification mechanisms that can evaluate the veracity of data by comparing them to the expected values. Long-term data are also valuable assets to HCOs and other entities. For example, health insurance agencies use such data to evaluate the overall health of applicants and charge them accordingly [ 170 ]. Cloud storage also provides a point of access to retrieve information.In our PD/HD case study, all of the data recorded by the sensors are eventually transferred to and stored in the cloud. After the data is uploaded to the cloud, researchers can access, preview, or download it through the Investigator portal on demand, as seen in Fig. 5 . Each subject’s information can easily be downloaded without any loss of information.

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Screen shot of MC10 web portal used in our PD/HD case study (named Sensor-MD Condensed 2 ). We can identify six subjects with unique subject ID (005), sex (M/F), and age (years) indicated on the right side. Total duration of recorded data is indicated on the right of an icon with a green tick mark. Clicking on the green tick marked icon downloads the data which can be utilized for analysis. On the far right, we can observe the statistics of the study showing the total number of subjects in the study followed by graphs showing number of male and female participants and age distribution.

HCOs can opt for public , private , or hybrid cloud services. Public servers can simultaneously host multiple applications from different entities. This reduces operational expenses but increases the risks of privacy leakage. Alternatively, HCOs can set up and maintain their private servers trading off additional expenses for improved security. As a compromise between the two implementations, it is also feasible to outsource demanding computations to public servers while processing sensitive data locally in a private setup. From another perspective, cloud-based servers can be centralized or distributed. The latter can reduce maintenance costs by taking advantage of geo-diversity. However, distributed servers are known to be more difficult to manage and require more complicated task distribution and resource allocation [ 171 ].

VII. ANALYTICS & INFERENCE

The volume of healthcare data generated in recent years from bio-sensors, EHRs, computerized physician order entry, social media, and administrative entries, was estimated to be 153 Exabytes in 2013 and is expected to reach 2000 Exabytes in 2020 [ 172 ]. This impressive accumulation of data in HIoT has created a conducive environment for data analytics and inference algorithms, which are now used in a variety of healthcare applications for anomaly detection, prediction of future health events, early detection of diseases, cost reduction, improved accuracy in clinical diagnosis, and clinical decision support [ 173 ], [ 174 ].

Despite its potential to revolutionize clinical HIoT, integration of data analytics and statistical inference techniques in clinical practice has been slow. The few documented successful examples are localized within HCOs with rich datasets. We dedicate the remainder of this section to providing an analysis of the two key drivers, which can enable a widespread adoption for clinical HIoT. In Section VII – A , we provide a brief overview of the existing and emerging algorithms that can form the backbone of clinical HIoT, followed by Section VII – B , where we study the issues arising due to the availability of the data that can be used in these algorithms.

A. Algorithms

Data analytics and inference algorithms can make HIoT an indispensable decision support tool for healthcare professionals. Although a good portion of the algorithms used today has existed for decades (e.g., Support Vector Machines and decision trees), they have not seen widespread use until the recent explosive growth in the computational capabilities of computing hardware [ 175 ]. This growth made extremely computationally-intensive algorithms, which were previously considered unusable, practical in cloud computing platforms with vast resources (e.g., Amazon EC2). Application of these algorithms has also been accelerated due to the wide availability of open-source software toolboxes that provide rapid development environments. Most of these algorithms are not only good at discovering explicit relations among data but also the latent and hidden features that are very difficult to detect by human specialists [ 176 ].

Most of the algorithms that existed in the previous decade required intricate knowledge of the features that were associated with a health condition; for example, the study in [ 90 ] attempts to use inference algorithms to determine the existence of known cardiac conditions in patients. They start their application by extracting features from ECG signals. This implies that the success of the inference depends strongly on the understanding of the features. The emerging deep learning (DL) network-based inference techniques largely eliminate this feature extraction step by utilizing a network that not only provides inference, but also extracts the features. DL-based inference applications have seen significant recent success in clinical practice [ 175 ], [ 177 ], however, their success has been restricted to applications that can provide the vast amount of data that deep networks demand to achieve acceptable accuracy.

Algorithmic nuances of data analytics and inference techniques pose additional obstacles against their commercialized use in clinical HIoT. A major challenge concerns the interpretability of an algorithm’s decisions. In fact, physicians are often more interested in the algorithm’s thought process as opposed to its final decision as this enables them to better assess the reliability of the inference [ 178 ]. Additionally, the reliance of analytics and inference techniques on statistical analysis sometimes limits their ability to model outliers, which are often of interest in the medical diagnosis of rare conditions. The evolving nature of progressive learning algorithms also complicates approval processes for clinical deployment; although the FDA has recently passed new regulations, where algorithms are cleared based on their developing teams [ 179 ].

Data analytics for our PD/HD case study involved both classification and regression [ 125 ]. The former involves categorizing patient activity into one of four classes: {Lying down, Sitting, Standing, Walking}. In our study, activities were classified every five seconds and the activity states for each interval was identified by determining the dominant acceleration axis (the axis with the largest mean acceleration). Based on the dominant orientations in chest and thigh sensors, activity states were categorized as lying down, sitting, and upright. A normalized auto-correlation-based analysis [ 124 ] was performed on the upright state intervals, which were further classified upright durations into standing and walking intervals. Figure 6 shows the proportion of time (over the full duration) subjects spent in different activity states.

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Classification of the activity states in our PD/HD case study as percentage of time spent (total duration ≈ 46 hours) lying down, sitting, standing, and walking for Control, PD, HD, and prodromal Huntington’s disease (pHD) participants. “n” is the number of participants analyzed [ 125 ].

Our PD/HD case study uses regression to analyze (poor) inter-limb coordination in HD subjects. Sensor data from a 10-meter walk test was used. A normalized cross-correlation of recorded acceleration from the left and right leg sensors indicated coordination between the legs while walking. As shown in Fig. 7 , for the control participant, the strong peaks in the cross-correlations at the 1-step and 2-step durations are indicative of the rhythmic nature of normal walking, whereas the HD participant’s peaks are much smaller and poorly defined due to lack of coordination between the legs [ 124 ]. Apart from quantifying and visualizing leg coordination, this example specifically illustrates the benefit of using multiple body-worn sensors for the analysis.

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Data analysis performed for our PD/HD case study to assess inter-leg coordination. The plot shows the normalized vector cross-correlation [ 124 ] of the recorded accelerations from left leg and right leg sensors as a function of time lags for a HD and a control participant. A 2-step cycle is annotated and highlights stronger peaks for the controls at the one and two step intervals compared with participants with HD.

B. Data Availability

Viii. visualization.

The primary objective of visualization is to present the results of data analytics and inference algorithms in the form of intuitive tables, charts, graphs, etc. to enable rapid and intuitive absorption and interpretation of the patient data by healthcare professionals. Compared to sporadic in-clinic measurements, HIoT datasets are large and visualization is an absolute necessity. Physicians seeing between 20–40 patients a day have no way to absorb and interpret the reams of numerical data that HIoT sensors can generate, effectively presenting the results via visualizations is also a challenging task [ 36 ]. Including too much detail can distract the attention of the user from critical information, while vital data can be omitted in aggressive summarization. Although visualization in clinical HIoT applications is primarily directed toward physicians, there is also a need to visually present some information to patients to facilitate understanding and improve engagement.

Visualization schemes can be static or interactive . Static visualization techniques range from lists/tables, plots/charts, graphs/trees, and pictograms to more complex formats showing spatial data, multidimensional data, or causal relationships [ 184 ]. Interactive methods are especially useful when visualizing information that simultaneously incorporates multidimensional temporal signals and generic static information.

A. Static Visualization

Systems such as hGraph [ 185 ] and its related programming libraries (such as the one introduced in [ 186 ]) provide visualizations by combining in-clinic, activity, sleep, blood pressure, and nutrition data. Other systems such as TimeLine [ 187 ] provide an EHR visualization, which shows all related static data (such as a list of medical problems, general information, and patient status) on a timeline. Another study targeting clinical information is presented in [ 188 ], where a real-time bedside graphic display is developed. The display includes personal information, lab results, vital signs for specific periods of time (e.g., over the past 24 hours), and Intensive Care Unit (ICU) information.

Choosing the proper visualization format ensures that medical professionals do not miss critical information. In our PD/HD case study, patient tremors over the period of one hour are plotted using a radial plot (as shown in Fig. 8 ). This allows easy comparison of tremors between healthy subjects and PD patients while identifying the difference in patient tremors when they are on vs off their medication. As more data becomes available, both for the individual, and across individuals, this figure can be further enhanced by adding in bounds for the “normal” range for tremors, or by customizing the presentation for each individual. Compared with the raw sensor data which for a single sensor is 8.2 MB over an hour and 196.1 MB over a 24 hour period (total across 5 sensors is approximately 1GB per day), the summarized presentation in the visualization is much easier for physicians to absorb and interpret.

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Visualization example from our PD/HD case study. A clock based visualization shows the variation in at-rest tremor magnitude in individuals with PD and compares it against controls and between on and off medication states. The tremor magnitude is quantified as the fraction of power in the 4 to 6.5 Hz frequency band (radial axis) for the principal axis acceleration recorded from MC 10 sensors affixed to the forearm over an hour duration (angular axis): (a) PD vs control (left), and b) PD ON vs OFF medication (right).

Studies such as [ 189 ]–[ 191 ] focus on providing feedback on personal health status through visualization. This information may be presented in different forms, such as an abstract art display of physical activity [ 192 ], charts and graphs [ 190 ], or even through a physical form with data sculptures [ 193 ].

B. Interactive Visualization

Many applications focus on providing an interactive environment in their visualization scheme. For example, Care-Cruiser is an interactive system that visualizes the effects of applying patient treatment plans [ 194 ]. The system shows the treatment plan progress and depicts the patient’s condition at any given point during treatment. The system also shows hierarchical data (nesting of treatment plans and sub-plans), temporal data, and qualitative data, and provides a means to compare multiple patients. Another interactive system is Medical Information Visualization Assistant [ 195 ] that allows medical experts to obtain relevant information in a context-related environment. A more specific visualization called Interactive Parallel Bar Chart (IPBC) is proposed in [ 196 ], which depicts data gathered from multiple hemodialysis sessions in a 3D bar chart. Numerical data over time of each hemodialysis session is shown in one row and different sessions are bundled together to create the final plot.

IX. SECURITY AND PRIVACY CONSIDERATIONS

System security and data privacy are the highest priorities in a medical system and should be considered during every phase of system design. Additionally, designers must comply with the Health Insurance Portability and Accountability Act (HIPAA) [ 197 ], which mandates that all service providers ensure the privacy of their clients. In this section, we discuss security and privacy considerations for each layer of our proposed HIoT system.

Data Acquisition and Sensing

Strict energy-budget, on-field deployment, and limited computational resources leave data acquisition level vulnerable to a wide spectrum of cyber threats such as eavesdropping and man-in-the-middle attacks, making it an easy target for adversaries (particularly insiders [ 198 ]). Lightweight cryptography is the main countermeasure against attacks. It is very effective in protecting both the security and privacy of smart healthcare sensing; simple but effective algorithms such as Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) can be tailored for resource-constrained devices. These techniques, however, leave the system vulnerable to side channel and hardware attacks. Hence, in addition to cryptography-based solutions, HIoT devices must be equipped with platform integrity attestation mechanisms for protection against hardware/software tampering attacks [ 199 ].

Communications

Adversaries primarily target smart healthcare communication because (i) its properties are better-known (in comparison to other components), (ii) it can be addressed remotely, (iii) due to its interconnection with other networks (e.g., home network) it can provide a launchpad for targeting non-HIoT applications, and (iv) well-known attacks such as Denial-of-Service (DoS) can result in grave consequences.

Cryptography is the premise of many existing communication security measures. Particularly, AES and ECC are suitable for the resource-constrained nodes in smart healthcare systems [ 200 ]. Both BLE and ZigBee utilize AES in their link layers. The network layer can be effectively protected using IPv6 over low power wireless personal area networks (6LoWPAN), since it can incorporate the Internet protocol security (IPSec) protocol. IPSec improves communication security, regardless of the protective mechanisms implemented in the application layer [ 201 ]. Finally,to achieve end-to-end encryption, the application layer must use the constrained Application Protocol (CoAP), as it utilizes Datagram Transport Layer Security (DTLS) and IPSec. DTLS can be considered as the User Datagram Protocol (UDP)-compliant implementation of the transport layer security (TLS) protocol.

Cloud Storage and Processing

Modern authentication mechanisms aim to replace conventional password-based solutions, as passwords are susceptible to many attacks (e.g., brute force and dictionary attack) and can become an inconvenience (especially, in HIoT, where a portion of users are elderly and disabled people). Two-factor authentication (TFA) is a common methodology today for healthcare systems to avoid a breach in case one key is compromised. TFA requires the user to enter a password and a secondary piece of information (e.g., a code obtained through a cell phone call or a dedicated device) for successful login. Due to the sensitivity of collected data and EHRs, HIoT implementations must ensure secure access to data. The conventional access control techniques, however, are proven inefficient in HIoT as it involves a large number of stakeholders with many dynamic roles. Attribute-based access control can circumvent many of these limitations but it fails to dynamically adjust privileges (say) in an emergency situation [ 202 ].

To protect data during processing [ 203 ], Advanced encryption schemes, such as Fully Homomorphic Encryption (FHE) have been proposed. While there is research showing the feasibility of such schemes, their computational intensity renders them impractical. Additionally, applying multi-layer isolation (Operating System (OS), Virtual Machine (VM), and hardware) can reduce the odds of data leakage in public servers both in the data processing and data storage layers [ 169 ]. Finally, it is equally important to stress the non-technical aspects of HIoT security. HCOs must form security teams to oversee the security of the system and provide timely responses to threats and attacks. Stakeholders must be trained, and HCOs must be ready to negotiate with attackers to protect the data [ 204 ].

In the PD/HD case study, participant privacy is carefully managed. Within the MC10 portal where the sensor data for the participants is stored and made available for analytics, participants are identified only by assigned ID numbers. The portal provides basic demographic information such as sex and age (see Fig. 5 ) but does not provide any other personally identifying information. EHRs with personally identifying information used in the clinic are maintained in a separate Red-Cap database [ 205 ] that is HIPPA compliant. This partitioning of data effectively ensures the availability of the sensor data for analytics without compromising the privacy of the participants. Of course, the partitioning of the data notwithstanding, the study is performed in compliance with the Institutional Review Board (IRB) requirements: informed consent is required from all participants and everyone accessing the data is required to undergo human subjects training certification.

X. CONCLUSION AND VISION FOR THE FUTURE

This paper reviewed the state-of-the-art in Healthcare IoT (HIoT) technologies, particularly focusing on clinical applications of HIoT. It presented HIoT through the lens of three of its primary components: (i) sensing and data acquisition, (ii) communication, and (iii) data analytics and inference. As the underlying IoT technologies in HIoT become more mature, each one of these three components will independently witness rapid progress within their own domain. Data acquisition and sensing technologies will benefit from future VLSI technologies that require lower battery power for their operation, while communication standards will continuously advance to provide higher communication throughput with decreasing power consumption demands from the sensing networks. Intelligent energy-aware operating systems will also be critical to harnessing the energy demand of end-devices [ 206 ]. Future cloud platforms will take advantage of the ever-increasing computing power of the CPU and GPUs to enable more sophisticated machine learning algorithms to be run in the cloud with higher accuracy [ 207 ], [ 208 ]. As a consequence of this dizzying pace of progress, we envision significant opportunities that will eventually make HIoT an indispensable part of clinical practice in the coming decade. However, we also expect several barriers to entry that can slow the pace of adoption. In this section, we first review the challenges and then discuss opportunities.

Challenges that may prevent rapid adoption of HIoT can be broadly categorized as legal, regulatory, and ethnographic [ 209 ]. From a legal point of view, we particularly expect legal accountability to pose a challenge. Suppose a highly-trusted machine learning-based decision support system were to fail, causing bodily harm or fatality among patients. Can the machine be held responsible? If so, specifically, does the responsibility lie with the programmer, adopting institution, or the business entity that sells the program [ 210 ]? Questions of this type that were previously irrelevant, must be addressed to eliminate uncertainties and allow organizations adopting HIoT to better understand their legal exposure and risks.

The second set of challenges we envision are in the regulatory domain. HIoT devices are likely to span a wide range of applications and sensing/actuation modalities that vary in invasiveness. Accordingly, there are likely to be different classes of devices with different regulatory requirements [ 211 ]. While a majority of the non-invasive sensors may be made available for purchase over-the-counter (for instance, for gathering data prior to a routine check-up with a physician), other devices that are more invasive —and/or have potentially adverse side effects— will likely be available only upon prescription and will likely be subjected to regulatory approval after clinical trials [ 212 ]. As such, approval and adoption rates, as well as pricing of devices, will exhibit significant heterogeneity and the synergistic benefit from multiple HIoT sensors working in unison may take longer than anticipated [ 177 ], [ 213 ].

Finally, ethnographic challenges involve the reluctance of the medical community to adopt HIoT, due to its perceived risks vs. marginal added-utility in day-to-day clinical practice [ 214 ]. We anticipate the adoption may be slow until some avant-garde healthcare organizations start gaining significant advantage due to HIoT adoption and build a history of operation without glitches. If early adopters are able to exhibit significant operating advantages quickly, there is also the possibility that HIoT penetration may accelerate quickly and virtually become the norm within a short period of time.

Despite the challenges, the HIoT also presents significant opportunities. One of the biggest opportunities is the potential for much higher diagnostic accuracy that can be achieved by using statistical inference and data analytics algorithms with the increasing availability of clinical data [ 215 ]. Currently, limited public datasets are available for training ML algorithms and HCOs rely on their individual databases [ 216 ], [ 217 ]. Large datasets that are required to train sophisticated algorithms (such as ones that use Deep Learning) are not freely available. Widespread adoption of HIoT in clinical settings and the aggregation of the resulting data, with appropriate anonymization, can create shared large-scale datasets from which all organizations can benefit and improve diagnostics and health management [ 218 ]. We envision that large-scale anonymous medical data sharing networks, enabled by HIoT, will vastly improve ML accuracy, much like data availability is currently revolutionizing computer and machine vision applications. Once established, the trend is likely to be self-reinforcing with continued acceleration in data accumulation driving improved inference and finer-grained/personalized analyses. Such a positive feedback cycle is also likely to speed up the mainstreaming of HIoT.

Another opportunity lies in the potential future use of new sensing modalities and actuators. For example, while today’s sensor technology requires a blood draw for accurate measurement of blood glucose levels, future sensor technology promises to perform the same measurements far less invasively, without a blood draw [ 219 ]. Availability of such conveniently-deployable technologies (e.g., non-invasive blood pressure, blood glucose, and oxygenation sensors) can drastically increase their use in clinical settings. Future actuator technologies can also improve the automation of routine medical tasks, such as drug delivery. Advances in Micro-Electro-Mechanical Systems (MEMS) technologies promise to create actuators that can deliver drugs (e.g., insulin) into the bloodstream directly, eliminating the need for the patient to manually do so [ 220 ]. This can result in substantial improvements in patient health, as the need for the patient to perform routine measurements, followed by drug intake, are eliminated and the drug dosage and administration regimen can be customized based on both the specific medication involved and the individual [ 221 ]. Benefits of MEMS-based technologies are even more pronounced for patients that cannot administer the drugs themselves due to motor deficiencies.

In summary, although the HIoT roadmap into the future is not free from bumps, we expect the pace to quicken once the ecosystem is in place and early adopters demonstrate the benefits it can provide.

ACKNOWLEDGMENT

The authors thank the anonymous reviewers for several constructive suggestions that have significantly improved the presentation in the paper. This work was supported in part by the U.S. National Science Foundation grants CNS-1239423 and CNS-1464273 and by the U.S. National Institute of Health grant NINDS-1P50NS108676-01.

Hadi Habibzadeh (S’17) received his B.S. in CE from Isfahan University of Technology in Iran in 2015 and his M.S. degree from University of Rochester, USA in 2016. He is currently pursuing his PhD degree in the ECE department at SUNY Albany, under the supervision of Dr. Tolga Soyata in the field of Cyber Physical systems and embedded systems with applications in Internet of Things and Smart Cities.

Karthik Dinesh received B.E in Electronics and Communication from National Institute of Engineering, Mysore, India in 2010 and M.Tech from Indian Institute of Technology, Kanpur, India in 2013. He is currently pursuing PhD in ECE department, University of Rochester under the supervision of Dr. Gaurav Sharma.

Omid Rajabi Shishvan (S’16) received his B.Sc. in EE from Sharif University of Technology in 2012 and his M.Sc. degree in ECE from University of Rochester in 2015. He is currently pursuing his PhD degree at the ECE department of SUNY Albany.

Andrew Boggio-Dandry (S’17) graduated summa cum laude from The University at Albany, State University of New York (SUNY Albany) with his B.S. in Computer Engineering in 2018. He is currently pursuing his PhD degree at the ECE department of SUNY Albany.

Gaurav Sharma (S’88-M’96-SM’00-F’13) is a professor at the University of Rochester in the Department of Electrical and Computer Engineering, in the Department of Computer Science, and in the Department of Biostatistics and Computational Biology. He received the BE degree in Electronics and Communication Engineering from Indian Institute of Technology Roorkee (formerly Univ. of Roorkee), India in 1990; the ME degree in Electrical Communication Engineering from the Indian Institute of Science, Bangalore, India in 1992; and the MS degree in Applied Mathematics and PhD degree in Electrical and Computer Engineering from North Carolina State University, Raleigh in 1995 and 1996, respectively. Dr. Sharma’s research interests include data analytics, cyberphysical systems, signal and image processing, computer vision, media security, and communications. He is a fellow of the IEEE, of SPIE, and of the Society of Imaging Science and Technology (IS&T) and a member of Sigma Xi. He is the Editor-in-Chief (EIC) for the IEEE Transactions on Image Processing and previously served as the EIC for the Journal of Electronic Imaging from 2011 through 2015. He is a 2020 Distinguished Lecturer for the IEEE Signal Processing Society.

Tolga Soyata (M’08 SM’16) received his B.S. degree in ECE from Istanbul Technical University in 1988, M.S. degree in ECE from Johns Hopkins University in 1992 and Ph.D. in ECE from University of Rochester in 2000. He is an Associate Professor in the ECE Department of SUNY Albany. His teaching interests include CMOS VLSI ASIC Design, FPGA-and GPU-based Parallel Computation. His research interests include Cyber Physical Systems and Digital Health. He is a senior member of both IEEE and ACM.

Contributor Information

Hadi Habibzadeh, Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203.

Karthik Dinesh, Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627.

Omid Rajabi Shishvan, Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203.

Andrew Boggio-Dandry, Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203.

Gaurav Sharma, Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627.

Tolga Soyata, Department of Electrical and Computer Engineering, SUNY Albany, Albany NY, 12203.

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  25. Microsoft makes the promise of AI in healthcare real through new

    Just over a year ago, the healthcare industry was energized by the debut of generative AI and the promise this new technology held for delivering real-world outcomes that positively impact clinicians, patients, health systems, and the broader health and life sciences ecosystem. Since then, it has been a catalyst for the development of new use...

  26. Expanding Access to Surgical Care Through Technological ...

    3/16/2024. Expanding Access to Surgical Care Through Technological Empowerment. Recommendations from Kaliber. Photos provided by Advantech and Kaliber. Interview with VP of Engineering, Mark Ruiz. Taipei, Taiwan, March 16, 2024 - As the leading provider of cutting-edge technology solutions, Advantech is excited to collaborate with Kaliber AI ...

  27. New Graduate Nurse Transition into Rural Home: A Case Study

    A case study supports mentorship facilitation as a readily available, effective strategy that can overcome the challenges of rural home care settings. A comparative analysis will link Duchscher's (2008) transition shock theory to mentorship as an effective strategy for supporting NGNs' transition in home care nursing.

  28. Caregivers face not only burden, but reward and benefits in their ...

    Iklan. A total of 278 caregivers took part in the Duke-NUS study, which also identifies the different types of caregivers. Dr Malhotra said the research is "relatively new, compared to the ...

  29. Trends in IoT based solutions for health care: Moving AI to the edge

    In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health. Keywords: Atrificial intelligence, Internet of things, Healthcare, Edge computing, Fog computing.

  30. A Survey of Healthcare Internet-of-Things (HIoT): A Clinical

    Abstract. In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments.