Can IoT Data Analytics Fix the Future of the Healthcare System?

The 2019 edition of the United Nations (UN) World Populations Prospects report states that there are approximately 7.7 billion people on earth in 2019. The population is expected to grow to up to 8.5 billion by 2030 and 9.7 and 10.9 billion in 2050 and 2100, respectively. However, the most astonishing statistics were revealed in 2018, when the UN acknowledged that, for the first time in history, people aged 65 and over had outnumbered children under age 5 worldwide. ABI Research is interested in how a continuously growing and aging population is impacting the healthcare system. Can Intenet of Things (IoT) technology fix the overwhelming financial burden of healthcare on tax payers in general? How can IoT-native medical data help?

The IoT in healthcare is not a ground-breaking concept, especially since the recent boom in wearable medical devices; ABI Research’s Wearable Device Market Share and Forecasts (MD-WADT-115) Market Data projects a total of US$44.5 Million wearable healthcare devices by 2023. Nevertheless, the emerging IoT technologies and growing competencies over data manipulation, prediction, and management are exemplifying some new and groundbreaking capabilities:

• Since 2018, the National Health Service (NHS) in the United Kingdom, widely famous for its “free-of-charge” medical services and treatments, announced that remote treatment and various medical IoT devices would be part of its coverage. The Continuous Glucose Monitor (CGM) is one example of IoT-enabled remote monitoring of patients' sugar levels, having been commercialized for the mass market by Eversense.
• While French software corporation Dassault Systèmes has developed “3-D Digital Twin” for predominantly manufacturing companies, its recent project was covering digital twinning in the medical domain by creating software that can turn a 2-D Heart scan from a patient into a complete, fully digitized model of the human heart.
• Meanwhile, some companies are adopting a more holistic approach when it comes to healthcare and providing services for IoT solutions. For example, Nexleaf Analytics provides analytics and predictive maintenance for vaccine fridges in Central and South African countries.

Finally, the other way in that which IoT technology can assist in digitalization transformation for the healthcare system is through real-time duplication of medical data records through digital twin technologies; such a data centric-approach also enables more specialized, patient-centric care.

Despite the tremendous progress and transformation of the healthcare system because of the IoT, the Internet of Medical Things (IoMT) is still facing significant challenges that go beyond financial investment and the democratization of the technology. The year 2020 is expected to highlight much greater connectivity and integration of the IoT tech. However, there are technical, adaptation, personalization, and privacy challenges that still have yet to be overcome:

• Understanding Data in Real Time: The key to re-loading the burden on the healthcare system is to make incoming data readable and organized and ensure its seamless transition across the system. Taking into consideration the variety of equipment types, machine software updates, various devices, and variety of incoming information from those devices, including heartbeat, environmental indicators and many more, the challenge of data readability is very real. Firstly, the well-thought-out connectivity would require a diversity of protocols such as Hypertext Transfer Protocol (HTTP), Constrained Application Protocol (CoAP), Extensible Messaging and Presence Protocol (XMPP), Data Distribution Service (DDS), and many others. The full range of protocols would constitute a challenge itself since it would require more exceptional selections and engineering ability to work with different toolkits.

Assuming that streaming and data analytics tech in the IoT can overcome the challenge of data management and readability, it still has to be done in real or near-real-time in order to have a practical effect on the healthcare system overall. One of the newly emerged partnerships addressing the challenge of data in medical applications was established between the National Football League (NFL) and Amazon Web Services (AWS) Machine Learning (ML). The companies intend to create digital twins of players that would be virtual replicas of them and hence enable the utilization of real-time environmental data alongside historical data. Such data application would enable the NFL to use ML and predictive analytics to construct real-time virtual scenarios, allowing engineers and data scientists to not only analyze the outcomes of various changes in the game but also monitor players and attempt to avoid injuries.

• Data Protection and the Security of Personal Data: The personal nature of the incoming information from wearable devices specifically, as well as digital copies of the healthcare record, alongside the possibility of personal digital twinning for medical service opens up the challenge of data security. A lot of hospitals and medical and pharmaceutical corporations lagged in moving to the cloud predominantly because of the sensitive nature of the data. The IoMT is still facing challenges of online systems getting hacked and breached, as well as the threat of hacking private wearable and medical devices, which can jeopardize people’s lives and health.

• Healthcare Regulations and Provisions: The IoMT still has yet to be approved by global healthcare regulatory bodies worldwide. From a business perspective this hinders progress, as it would require long-term investment with little knowledge about Return on Investment (ROI) alongside the legal and social perception formalities.

• Challenges of Legacy Infrastructure Regular Updates: The old, outdated and legacy infrastructure is a known issue in the healthcare domain. The facts are that legacy infrastructure hinders cyber-readiness, challenges new IoT technologies’ interoperability, and precludes infrastructure from regular updates.

Although the newly established partnerships and IoMT technology potential are unquestionable, the high risk and challenging side of IoT is still dominating the discussion of IoT in healthcare. It is essential to acknowledge that, despite the challenging predisposition of the industry toward IoT, the IoMT is expected to have tremendous growth in the future. The examples of current and successful IoMT experiments already cover data insights, hospitals’ logistic and optimizations tools, personalized patient care, innovative drug management systems, tracking and medical wearables devices, medical sensors etc. Having a closer look at the industry, it is possible to address previously mentioned challenges and their potential solutions for the future of IoMT:

• Understanding Data in Real Data: The continuous transmission of data would require continuous connectivity. Subsequently, the expansion of Wi-Fi 6, LTE, and 5G connectivity could not only assist in overcoming the traditional restrictictions of connectivity such as Ethernet, but also provide cost saving opportunities. For example, with stable and secure LTE, there is the possibility for various channels’ connectivity alongside the establishment of the priority and processes, which is vital in the healthcare industry. Hence, by deploying LTE, medical personels are in a position to receive real-time data, with an application of real-time analytics (i.e., streaming analytics toolset) by being in a far proximity from the patient or medical facility.

• Data Protection and Security: There is greter momentum toward Central Processing Unit (CPU)-based security options; for example, Arm TrustZone is providing system-wide embedded security options for the ARM and cortex-base processor system, enabling security in everything from Microcontrollers (MCUs) to high-performance processors.  

• Healthcare Regulations and Provisions: Despite long processes of IoMT device approval and potential hesitation to invest due to quick ROI, IoT devices have to go through standard medical evalution in order to be classesd as medical devices. The Food  and Drug Administration (FDA) Class II and III have a potentially lengthy procedure of approval, predominantly due to the imbedded nature of the solutions, while ISO 13485:2016 and  IEC 60601-1/2/6 are relatively straightforward and affordable standartizations for medical and electric-medical devices, respectively, which would be suitable for IoMT prototyping.   

In conclusion, the identified IoMT challenges are the next step of tackling overload and growing burdens on the global healthcare system. The sooner the social and governmental consensus over regulations and security can be achieved, the sooner innovation and new technologies will be on the way to develop, improve, and elevate healthcare solutions, care, and the system as a whole.