Tag: Internet of Medical Things

One Tech Company Steadily Expanding Their Business in Health Care That Deserves More Attention

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“We are essentially nurturing a start-up within a large-scale organization and leveraging Best Buy’s core assets, including the Geek Squad, to incubate a new business.”

Corie Barry, CEO, Best Buy
Image Credit: Shutterstock.com

Best Buy isn’t the first name that comes to mind when people think of health care. For decades, Best Buy has been one of the leading consumer electronics retailers in North America. With more than 1,000 stores across the US and Canada, the company brought in over $50B in revenue in 2021, mainly driven by sales of consumer electronics such as laptops, desktop computers, and smartphones. However, in recent years the company has been heavily focused on expanding into healthcare, building out a dedicated tech-enabled offering for at-home care as the aging population increasingly looks to age in place. Nearly 90% of adults 65 and older want to live in their homes, which presents opportunities to introduce tech-enabled monitoring and engagement solutions.

Best Buy tapped into healthcare in 2018 with its $800 million acquisition of GreatCall (now Lively), which provides emergency response services to seniors. Their Lively Health & Safety Packages were recently updated to empower better those who are aging at home, and two new services were added: Nurse On-Call and Care Advocate. In addition, Lively Urgent Response was made compatible with Amazon Alexa-enabled devices. Now, users can say, “Alexa, call for help,” and will be immediately connected with someone who will assess the situation and get them the help they need in various situations.

The following year, Best Buy bought the remote monitoring company Critical Signal Technologies. Its 2021 deal with Current Health was a big bet for at-home services, building off past investments and further solidifying the retailer’s presence in healthcare. Current Health is a remote patient monitoring platform that major hospital systems nationwide rely upon to communicate with patients and track patients’ vital signs. The combination of Current Health’s technology and Best Buy’s size and ability to help customers with technology in their homes helps close the gap in enabling care at home.

And they’re delivering tangible results. Current Health customer, the Defense Health Agency’s (DHA) virtual care program was the focus of a study published in the Journal of Medical Internet Research (JMIR). In the study, researchers compared the financial impact and clinical outcomes at hospitals that participated in the care-at-home program versus those that weren’t. The researchers at Current Health and DHA found a 12% lower length of stay averaged across all COVID-19 patients, saving $2,047 per patient and a total net savings of an estimated $2.3 million in the first year of the program, with no increase in 30-day readmissions or emergency department visits. This vital research demonstrates that care-at-home programs can improve the operational efficiency of care delivery without harming clinical outcomes, which is essential to making healthcare better and more sustainable.

The company is partnering with several health systems, including Geisinger Health, and Mount Sinai Health System, to expand its at-home care technology platform. New York-based Mount Sinai Health System partnered with Current Health on remote patient monitoring starting in 2020 and already monitors cancer patients at home. Geisinger is working with Best Buy to manage at-home care for patients with high-risk hypertension, diabetes, and those recovering from sepsis.

And this week, Best Buy announced that it would offer technology support to Charlotte-based Atrium Health, part of the newly formed Advocate Health, for its hospital-at-home program launched in early 2020 in response to the COVID-19 pandemic. Best Buy’s Geek Squad will go to patients’ homes, set up technology that remotely monitors their heart rate, blood oxygen level, or other vitals, and train the patient or others in the home how to use the devices. The data would then be shared securely with doctors and nurses through the telemedicine hub from Current Health. The tech needs previously were handled within Atrium. The goal is to eventually scale these services nationwide, including across Advocate’s Southeastern and Midwestern footprints. Best Buy began setting up virtual-care systems in mid-February for ten hospitals in and around Charlotte, North Carolina. The company said it aims to have about 100 patients in the program daily — roughly equivalent to a midsized hospital but without a building.

“The reason we’re betting on this partnership is because we truly believe that we can bring so much to the table that is actually distinctly different, from not just what others are doing in the hospital-at-home space or the health-at-home space, but also really unique and what we actually need in service of the communities that we’re a part of.”

Rasu Shrestha, Chief Innovation and Commercialization Officer, Advocate Health

Best Buy is leaning into core capabilities of supply chain and logistics, data analytics and consumer wellness products. Entering the healthcare space is also a defensive move for Best Buy’s retail business, allowing the company to hedge against supply chain disruptions and increasing competition from Amazon, both of which have threatened its main consumer electronics business. The retailer expects a same-store sales decline of between 3% and 6% in the fiscal year, with most of that drop coming in the first six months. On an earnings call last week, CEO Corie Barry said Best Buy expects sales in its health division to grow faster than the rest of its business this fiscal year.

“We want to do this well. We want to create pathways that enable care at home in a more seamless manner. We want to tie technology and empathy together and really help change how health care is delivered to people in their homes.”

Deborah DiSanzo, President, Best Buy Health

Best Buy’s existing Geek Squad customer service workforce — comprised of more than 20K agents already making approximately 9M home visits a year to help customers with tech setup and use — makes the company well-positioned to take on the opportunity in at-home care. Best Buy can also leverage existing relationships with healthcare device buyers.

Best Buy is one of many retailers seizing opportunities in the healthcare space. In January, Dollar General launched three mobile healthcare clinics in Tennessee. The following month, CVS Health announced its $10.6 billion acquisition of primary care provider Oak Street Health, outlining plans to add 130 Oak Street sites by 2026. And last week, Walmart Health detailed its plans to add 28 new centers and expand into two new states.

Remote patient monitoring and efficient delivery of medical devices are important components of home health care — but hospitals are struggling in all of these areas as they scale their program. Partnering with a well-resourced tech company is a common solution.

“The vast majority of home hospital programs partner with some sort of technology solution to do the last mile monitoring of the patients,”

Constantinos Michaelidis, the director of UMass Memorial Health’s Hospital at Home program

Watch for more partnerships like the Atrium deal announced this week in the future. These leverage Best Buy’s strengths and takes the tech deployment challenges away from already overwhelmed health system tech staff. It’s a win-win for both partners and the patients they serve.

How Leveraging Exponential Technologies Will Virtualize Clinical Trials

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“Clinical trials are the most expensive part of developing a drug. And, it’s very hard to do a clinical trial testing the new drug’s interaction with every other drug that might be out there.”

William F Feehery, CEO of Certara
Image Credit: Shutterstock.com

Traditional clinical trials are equivalent to billions of dollars and years of hard work with no guarantee for the new drug to be approved by regulatory bodies, not to speak about the dangers of testing medication on animals or humans. According to CB Insights, on average, it costs $2.6B to research and develop a successful drug and takes 10+ years to come to market. It’s estimated that in-vivo testing (testing on animals and humans) accounts for more than 75% of the total cost, with recruitment alone being one of the most significant barriers to drug development — only 6% of clinical trials are ultimately completed on time.

Then there’s the issue of clinical effectiveness. According to the US Food and Drug Administration (FDA), medication ineffectiveness ranges from 38 to 75% for various illnesses ranging from depression to osteoporosis. The primary cause is each individual’s genetic makeup. It is so diverse and their interaction so unique that medicines designed for the “ideal patient” may not be appropriate for the “actual patient.”

Amid a global health crisis, the challenges only multiply: Covid-19 interrupted an estimated 80% of non-Covid-related clinical trials. One way to modernize the drug testing process is by applying technologies to the traditional framework, such as online platforms to seek out participants. An alternative method is to build an entirely new setting. That new setting leverages the multiplier effect of several exponential technologies to virtualize the clinical trial process.

If you’ve been following this blog, you know that I’ve written on several of the critical topics before in my “Straight Talk” series of posts. Here are the major ones:

For this post, I want to focus on two additional areas recently getting attention: digital twins and in-silico trials. These are generally lumped together. But for this review, I’ve chosen to keep them separate.

As part of the move to personalized medicine, researchers are interested in developing digital twins that could integrate known human physiology and immunology with an individual patient’s clinical data in real-time, then produce predictions of what would happen during various medical events. A digital twin is a virtual representation of a single person where every known medicine for that person’s illness can be tested. This will allow the best treatment to be determined. It can even monitor the virtual “person” and notify you if a medical condition develops as a side-effect enabling preventive actions. As a result, the digital twin has numerous applications across multiple therapeutic areas in healthcare.

It’s been reported that 66% of healthcare executives expect increasing investment in digital twins over the next three years. This is because digital twins improve healthcare organization performance, discover areas for improvements, provide customization and personalization of medicine and diagnosis, and enable the development of new medications and devices.

If we return to the list of exponential technologies above, digital twins use all of them to create a complete picture of an individual’s vitals, medical state, response to drugs, therapy, and the surrounding environment. Companies are creating digital twins to specifically look at chronic diseases like diabetes, where a chronic diabetes patient’s lifestyle, daily food habits, and blood sugar data are analyzed. The model notifies the patient about prescriptions, dietary habit modifications, medical consultations, and so on.

YouTube video credit: TEDx, Perth, Jacqueline Alderson

Another excellent example of the advancement of the field is the Oncosimulator project. The In Silico Oncology Group is developing an in silico experimental platform, as well as an advanced medical decision support tool called Oncosimulator, in collaboration with several research centers in Europe and Japan to optimize cancer treatment. The oncosimulator is an integrated software system simulating in vivo tumor response to therapeutics within a clinical trial environment. It aims to support clinical decision-making for individual patients.

YouTube video credit: VPH Institute

Several companies have created digital twin representations of human organs. For instance, Hewlett Packard Enterprise collaborated with Ecole Polytechnique Fédérale de Lausannes (EPFL) on the Blue Brain Project, using its supercomputer to develop digital models of the brain for scientific purposes. Siemens Healthineers offers a Digital Twin model, and Philips offers their own virtual heart. Dassault Systèmes launched its Living Heart Project in 2014 to crowdsource a virtual twin of the human heart. The project has evolved as an open-source collaboration among medical researchers, surgeons, medical device manufacturers, and drug companies. Meanwhile, the company’s Living Brain project is guiding epilepsy treatment and tracking the progression of neurodegenerative diseases. The company has organized similar efforts for lungs, knees, eyes, and other systems.

YouTube video credit: Dassault Systèmes

Although digital twins have a promising future in health care, the full impact of the technology will be determined by its capacity to integrate knowledge into accurate medical advice at scale. Better data, new interactions between patients and providers, and a regulatory framework to confirm these promises will be required to support this transformation.

In-silico trials simulate the effects of a new treatment using virtual populations to supplement or even partially replace in vivo testing. Researchers can use modeling and simulation to predict trial outcomes before advancing to real-world clinical trials and ultimately design studies that are more likely to succeed. Virtual populations can diversify the biological variability of traditional trials and enable the exploration of irregular phenotypes that would be difficult to recruit for. Control or placebo arms in trials can be simulated so that real-life patients who need treatment are guaranteed to receive it. This helps encourage potential subjects to enroll in the first place. Finally, in silico methods can lead to more exploratory research outcomes that might not be feasible with conventional trials. For instance, a recent in-silico trial looked at the same virtual population twice to see how the presence or absence of a secondary risk factor affected treatment.

“You can run an in-silico Phase II trial on 10,000 virtual subjects, rather than being limited to let’s say 10 or 20 or 50 real human subjects.”

FRANÇOIS-HENRI BOISSEL, NOVADISCOVERY CEO
YouTube video credit: WTF Health, Berlin, November, 2018

The technology is mainly early-stage, but it has recently seen increasing adoption from medical device and pharma players. Using statistical models of disease progression, researchers can better simulate clinical outcomes for a given cohort of patients, down to the level of how specific traits impact treatment. This could result in a hyper-personalized approach to assessing a patient’s fit for a given intervention.

In-silico technology, though, is not without its drawbacks. For one, using computer-generated patient populations relies on real-life, historical data for modeling, making it tricky to test for unexpected or novel side effects to treatments. Instead, in-silico trials might be more suited to test a treatment’s efficacy (i.e., to validate “expected” results). Soon, in-silico testing will primarily be used to augment or optimize traditional in-vivo testing rather than replace it altogether. Watch the regulators too. The U.S. Food and Drug Administration also picked up on the potential of in-silico trials, and it’s actively supporting the development of virtual models – for the testing of new medical devices. The FDA and the EUA in Europe are creating frameworks outlining best practices for collecting and analyzing data like digital evidence. And the FDA is already planning for a future in which more than half of all clinical trial data will come from computer simulations.

YouTube video credit: Novadiscovery, July, 2021

Big tech has a vital role to play here. In 2019, Verily, the health and life sciences company under Google parent company Alphabet, announced it was moving into the clinical trials space. Last month, Amazon Web Services announced a collaboration with Thread Research intended to decrease clinical trial costs while improving research access and data quality. And the Apple Watch has been used in a variety of studies, recent and current, both to investigate the efficacy of treatments (in areas where the watch’s efficacy has already been satisfactorily demonstrated), as well to further investigate the watch in other use cases. Watch for new partnerships with Google, Amazon, Apple, and others to gather and collect data to support in silico clinical trials.

After reviewing the current literature, I would sum up the benefits and drawbacks of virtual clinical trials like this:

  • Benefits
    • Larger number of trial subjects
    • Decreased costs
    • No consequences for either animals or humans
    • Better patient engagement
    • Can lead to more exploratory research outcomes
  • Drawbacks
    • Not compatible with all types of clinical trials
    • Access issues
    • Difficult to test for unexpected side effects
    • Credibility factor
    • EHR interoperability challenges

While completely simulated clinical trials are not feasible with current technology and understanding of biology, their development would be expected to have significant benefits over current in-vivo clinical trials. Under the right conditions, they could rapidly supplant traditional in-person approaches and dramatically enhance the scale, data collection, geographic range, cost-effectiveness, and speed of clinical trials. Certainly, decentralized clinical trials are here to stay. As we’ve seen in other areas of health care, the last 24 months have crystalized the potential of the virtualized research model, driving a rate of deployment and progress that might otherwise have taken 5-10 years to materialize.

Some Straight Talk on Edge Computing in Health Care

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“5G and edge computing will enable the low-latency, real-time guaranteed conditions necessary to use IoT devices for patient monitoring and at-home care. For rural patients unable to access the care provided in larger metropolitan facilities, this could be a game-changer.”

Greg Chiasson, Principal, Capital Projects & Infrastructure (Technology, Media and Telecommunications), PwC US
Image Credit: Shutterstock.com

Centralization has been a predominant paradigm in healthcare computing for the past several decades. Originally, this meant that provider organizations operated large, centralized data centers that housed mainframes and servers designed to serve as the industry’s computing workhorses. Recent technological advancements are starting to shift that paradigm, however.

Edge computing, through on-site sensors and devices, as well as last-mile edge equipment that connects to those devices, allows data processing and analysis to happen close to the digital interaction. Rather than using centralized cloud or on-premises infrastructure, these distributed tools at the edge offer the same quality of data processing but without latency issues or massive bandwidth use. It seems clear that edge computing will play an essential role in health care. But what is it? And why is it a significant development?

First, some basics – Edge computing is the practice of capturing, storing, processing, and analyzing data near the client, where the data is generated, instead of in a centralized data-processing warehouse. Hence, the data is stored at intermediate points at the ‘edge’ of the network, rather than always at the central server or data center.” The concept dates back to the 1990s when Akamai solved the challenge of Web traffic congestion by introducing Content Delivery Network (CDN) solutions. The technology involved network nodes storing static cached media information at locations closer to end-users. Today, edge computing takes this concept further, introducing computational capabilities into nodes at the network edge to process information and deliver services.

How is edge computing related to cloud computing? – Think of edge as an extension of the cloud rather than a replacement. Cloud computing is the concept of storing, processing, and analyzing large amounts of data on remote servers or “data centers,” usually online. Data centers are often located remotely where data is processed and collected, resulting in a period between collection and processing or “high delays.” While the lag of time is usually only a few hundred milliseconds, it makes a massive difference to time-sensitive applications. In addition, a large amount of data moving up and down the network poses significant difficulties to bandwidth. This can reduce the speed of data processing and transfer. This back-to-back period could mean the difference between life and death in time-sensitive applications, especially in health care. So, edge computing is used to process time-sensitive data, while cloud computing is used to process data that is not time-driven.

Why is edge computing important for health care? – In the healthcare industry, data is growing at an astounding rate. A recent Dell Technologies global survey found that healthcare and life sciences data had grown by 878 percent over the previous two years — with no slowdown in sight. A significant percentage of this data is coming from the 10–15 connected devices that are often found at the typical hospital bedside. At the same time, healthcare providers are also collecting a mountain of patient data generated from connected wearable medical devices, like smartwatches and mobile wellness applications. Whether it’s on a health worker’s tablet, a wearable device, an ingestible sensor, or a mobile app, computing at the “edge” of the network is essential for speed, scale, and performance. The challenge now is putting all of this data to work to improve diagnostic and patient care processes to contribute to better patient outcomes.

What are some key use cases for edge computing in health care? – In connected healthcare, distributed analytics unlocks insights from data collected from IoT devices to help healthcare providers see beyond episodic patient visits. Edge computing broadens the field of vision, creating a continuous real-time patient record that helps providers shift from reactive to proactive care. A clear view of the power of Edge computing emerges in use case examples that illustrate how the healthcare equation changes when the analytics are brought to the data, including:

Rural medicine – Providing quality healthcare to isolated rural areas has been a challenge throughout history. Even today, with innovations in telemedicine and more readily accessible health data, medical providers have struggled to deliver fast, quality care to people who live far from hospitals and have limited internet access. Traditional healthcare databases face significant challenges here due to connectivity issues, but combining IoT medical devices and edge computing applications can make it easier to overcome these difficulties.

Patient-Generated Health Data – A range of IoT medical devices such as wearable sensors, blood glucose monitors, and healthcare apps have become far more common over the last decade, all of them collecting massive amounts of Patient-Generated Health Data (PGHD) that makes it possible for medical professionals to diagnose problems better and monitor patient health over long periods. The enormous amount of data produced by these IoT edge devices may be valuable. Still, it’s also creating a challenge for the healthcare providers tasked with managing it and keeping it secure. Edge computing applications have the potential to solve this data problem. By retaining much of the critical processing tasks on the devices located on the edge of the network, healthcare IT architectures can still benefit from gathering health-related data while also getting the rapid, real-time analytics that can predict and respond to health emergencies.

Improving the patient experience – Going to the hospital doesn’t have to be an unpleasant or frustrating experience. From smart devices that allow people to check-in for appointments whenever they like to notifications that guide them through an unfamiliar facility to find the appropriate office, IoT medical devices are among the key edge computing use cases that can potentially transform the healthcare industry’s customer experience completely.

Improving the supply chain – Sensor-equipped IoT edge devices have the potential to revolutionize the way medical facilities manage their inventories. Devices gathering data on usage patterns can utilize predictive analytics to determine when the hardware will likely fail. Inventory management based on intelligent RFID tags can eliminate time-consuming paperwork and manual ordering. Fleet vehicles equipped with GPS and other sensors can track the location of critical shipments in real-time. For organizations struggling to control rising costs, IoT healthcare supply chain innovations offer an opportunity to gain operational efficiencies on the margins and represent one of the more compelling edge computing use cases.

Improved patient safety and monitoring – Computer vision solutions can monitor acute patient safety and longer-term medical compliance to reduce readmissions. Examples include cameras and sensors that monitor patient and staff compliance with hand sanitization policies to reduce infection rates, devices that ensure discharge instructions are fully followed, telesitters to improve patient safety and reduce fall risk in post-acute care step-down patients, and connected pill bottles that confirm medical adherence.

Enhanced pharmaceutical drug supply chain safety – Edge and IoT devices and sensors can reduce the risks inherent in the healthcare supply chain, including temperature-related and counterfeit risks. Examples include devices that continuously monitor temperature changes in vaccines during transportation to ensure that safe temperature range is maintained, RFID sensors that track medication from the point of manufacturing to the point of consumption, and GPS-enabled shipping containers that improve inventory and waste management and distinguish between goods in transit and goods stolen.

New opportunities to enhance precision medicine research – Sensor-generated data, combined with medical-grade software applications, make it possible to treat rare medical conditions previously too expensive to address. Examples include wearables and other sensors integrated into the clinical trial process to expedite study completion and improve clinical compliance and reporting, along with digital therapeutic capabilities, such as applications that allow for the automatic collection and use of individual health data.

Where we are today – The sky’s the limit when it comes to the opportunities to use edge computing in health care, says Paul Savill, senior vice president of product management and services at technology company Lumen, especially as health systems work to reduce costs by shifting testing and treatment out of hospitals and into clinics, retail locations, and homes. That is not simply buzzword-driven hype: covid-19, for example, has laid bare the need for health-care options outside the doctor’s office or hospital. There are hundreds of healthcare uses that rely on low-latency, remote, real-time results, from pop-up clinics and cancer-screening centers to patient-monitoring systems, including pacemakers and insulin pumps.

My take – While some technology applications are still in their early stages, edge computing will ultimately help solve problems that cloud computing cannot. Edge computing can act as the “glue” that takes the benefits of exponential technologies like 5G cellular technology, The Internet of Medical Things, digital health sensors, and remote patient monitoring and combines them into a comprehensive tool that can be applied at the point of care to improve clinical outcomes and increase the quality of life for patients everywhere.

But, edge computing in healthcare is domain-specific and needs support from healthcare organizations. It is necessary to work with domain experts to ensure that the system can play in real life. Healthcare organizations will need to bring all stakeholders to the table to discuss their requirements and needs, so they have a better chance of moving forward to applying edge computing to healthcare domains.

The widespread adoption of healthcare IoT devices and edge computing will make people more aware of their health status. It will also make advanced healthcare resources available in remote areas via telemedicine. The ability for caregivers to regularly keep tabs on their patients will reduce the rehospitalization rate significantly. As collaborative edge computing and machine learning can preprocess data and generate meaningful analytics in real-time, caregivers will spend less time collecting and analyzing data and more time caring for their patients.

Some Straight Talk on the Internet of Medical Things (IoMT) in Health Care

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“There’s no doubt that the emerging IoHT has vast implications for the healthcare industry, but it will equally impact those who are not in the business of healthcare. Soon, anything will be able to link up to the IoHT.”

Joseph Kvedar, M.D., Senior Advisor, Virtual Care, Mass General Brigham
Image Credit: Shutterstock.com

In healthcare, the Internet of Things is not a new concept. The healthcare industry has been using telemetry for many years. Telemetry is similar in that it gathers data remotely and transmits it via radio or cellular signal. Still, the healthcare industry has made little progress in incorporating Internet of Medical Things technologies into healthcare in recent years. The technologies we see today may offer a glimpse of what is to come. Hospitals, clinics, diagnostic laboratories, and surgical centers worldwide are already using various implanted, stationary, and wearable medical devices for patients. Web applications also incorporate the technology of the Internet of Medical Things to improve workflow management, patient monitoring, medication management, telemedicine, and more.

If you are interested in a comprehensive discussion on the Internet of Medical Things, the best resource I’ve found is this book by Dr. Joseph Kvedar, Vice President, Connected Health, Partners HealthCare. Connecting to the IoHT presents a huge opportunity for all sectors of business and society, including payers, providers, pharma and biotech companies, technology vendors, and newcomers to the space with fresh, creative ideas. This book shares Dr. Kvedar’s observations as a 20-year veteran in the field.

First, some basics – The Internet of Medical Things (IoMT) is an amalgamation of medical devices and applications that connect to information technology systems using networking technologies. The IoMT market consists of intelligent devices, such as wearables and medical/vital monitors, strictly for health care use on the body, in the home, or in community, clinic, or hospital settings; and associated real-time location, telehealth, and other services.

The best depiction of what the IoMT might be like is shown in this excellent video from Cable Labs. I’ve shown this in conference presentations over the years, and it always strikes an emotional chord with the audience.

How I like to segment the IoMT market – If you review the literature on the topic, you’ll find multiple segmentation models proposed by different stakeholders in the industry. In my previous work, I’ve found that the easiest way to communicate the breadth of applications for the IoMT was to use a segmentation model by location, namely: on the body, in the home, in the community, in the clinic, or in the hospital settings. Let’s look at each in a bit more detail:

On the body applications – The on-body segment can be broadly divided into consumer health wearables and medical and clinical-grade wearables. Consumer health wearables include consumer-grade devices for personal wellness or fitness, such as activity trackers, bands, wristbands, sports watches, and smart garments. Clinical-grade wearables include regulated devices and supporting platforms that are generally certified/approved for use by one or more regulatory or health authorities, such as the U.S. Food and Drug Administration. These devices are used with expert advice or a physician’s prescription (e.g., Kardia ECG device).

In the home applications – The in-home segment includes personal emergency response systems (PERS), remote patient monitoring (RPM), and telehealth virtual visits. A PERS integrates wearable device/relay units and a live medical call center service to increase self-reliance for homebound or limited-mobility seniors (e.g., LifeAlert). RPM comprises all home monitoring devices and sensors used for chronic disease management, which involves continuous monitoring of physiological parameters to support long-term care in a patient’s home to slow disease progression; acute home monitoring, for continuous observation of discharged patients to accelerate recovery time and prevent re-hospitalization; and medication management, to provide users with medication reminders and dosing information to improve adherence and outcomes. (For a more detailed look at Remote Patient Monitoring, check out my previous blog post on the topic here). Telehealth virtual visits include virtual consultations that help patients manage their conditions and obtain prescriptions or recommended care plans.

In the community applications – This is a fairly broad segment that generally consists of five applications. Mobility services allow passenger vehicles to track health parameters during transit. Emergency response intelligence is designed to assist first responders, paramedics, and hospital emergency department care providers. Kiosks are physical structures, often with computer touchscreen displays, that can dispense products or provide services such as connectivity to care providers. Point-of-care devices are medical devices used by an advanced practice provider outside of the home or traditional health care settings, such as at a medical camp. Logistics involves the transport and delivery of health care goods and services, including pharmaceuticals, medical and surgical supplies, medical devices and equipment, and other products needed by care providers.

In the clinic applications – This segment includes IoMT devices that are used for administrative or clinical functions (either in the clinic, in the telehealth model, or at the point of care). Examples include Rijuven’s Clinic in a Bag, a cloud-based examination platform for clinicians to assess patients at any point of care, or the Tytocare platform.

In the hospital applications – This segment is divided into IoMT-enabled devices and a larger group of solutions in several management areas:

  • Asset management monitors and tracks high-value capital equipment and mobile assets, such as infusion pumps and wheelchairs, throughout the facility.
  • Personnel management measures staff efficiency and productivity.
  • Patient flow management improves facility operations by preventing bottlenecks and enhancing patient experience—for example, monitoring of patient arrival times from an operating room to post-care to a patient room.
  • Inventory management streamlines ordering, storage, and use of hospital supplies, consumables, and pharmaceuticals, and medical devices to reduce inventory costs and improve staff efficiency.
  • Environment (e.g., temperature and humidity) and energy monitoring oversees electricity use and ensures optimal conditions in patient areas and storage rooms.

Where we are today – The IoMT brings together the digital and physical worlds to improve the speed and accuracy of diagnosis and treatments and monitor and modify patient behavior and health status in real-time. It also improves health care organizations’ operational productivity and effectiveness by streamlining clinical processes, information, and workflows. The global IoMT market was valued at $44.5 billion in 2018 and is expected to grow to $254.2 billion in 2026, according to AllTheResearch. The smart wearable device segment of IoMT, inclusive of smartwatches and sensor-laden smart shirts, made up for the largest share of the global market in 2018, at roughly 27 percent, the report finds. This area of IoMT is poised for even further growth as artificial intelligence is integrated into connected devices and can prove capable of the real-time, remote measurement and analysis of patient data.

My take – If you’ve been following this blog for any time, you’re probably seeing a pattern developing. Many of the technologies I’ve been reporting on are linked. That’s the beauty of exponential growth technologies. An increase in functionality in one technology has a multiplier effect on others. The potential for the Internet of Medical Things is promising. As more healthcare providers successfully adopt these technologies, approval for more devices will increase. This increase will likely be cautious, but it will change how we give and receive care for the better. Inclusion of IoMT devices into healthcare will enhance efficiency, reduce costs, save time, improve health record storage, foster informed decision making on delivering proper treatment to patients, and improve the operational efficiency of systems and processes, all leading to the creation of an intelligent patient-centric healthcare system.