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The Internet of Medical Things: Better Patient Care and Improved Clinical Management

December 27, 2018
by Mark Wolff, Ph.D., Industry Voice, Chief Health Analytics Strategist, SAS Global IoT Division
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Driven by fundamental business changes and seeking innovation to control rising costs, the U.S. healthcare market sees a big upside in deploying Internet of Things networks. The goal is to boost patient-centric care, provide more value-based services and offer more personalized medicine.

The potential for a healthcare IoMT—also referred to as the Internet of Medical Things (IoMT)—is huge. IoMT paves the way for a huge leap forward in patient care. What does IoMT in healthcare look like today and in the future?

IoMT smart sensors such as patient wearables and medical devices are connected to resources that detect issues in real time. The sensors transmit data over an IoMT network that is analyzed with machine learning and artificial intelligence (AI) software that initiate actions such as notifying caregivers or patients of impending issues. Consider these potential IoMT uses in a clinical or home-based care setting:

  • Cutting emergency room wait times. Who hasn’t endlessly sat in the ER waiting room? Comprehensive, IoMT-based ER resource tracking systems offer dramatic reductions on a real-time basis.
  • Remote health monitoring. Caregivers are being pushed to cut the length of hospital stays. Connected patient wearables provide constant monitoring to greatly improve in-home healing. Alerts can trigger caregiver queries or actions.
  • Ensuring critical equipment availability.  Like all machines, life-saving equipment can suffer power failures and breakdowns. An IoMT can sense if a piece of gear is near failure or in need of maintenance, proactively heading off the problem before it becomes critical.
  • Patient, staff, and supply management. All assets must be tracked. If the ER needs a defibrillator, an IoMT can tell if it’s been left in a nearby ER suite. Is a certain drug not available on a certain ward? Consult the IoMT-based drug tracking software. A patient comes into the ER but only speaks Italian. Is there an Italian speaker on duty? Guess where the answer can be found.
  • Improved drug management. Through small sensors in a pill transmitting to a patch worn by the patient, prescribers can determine if the medication has been taken and in what dose. Patients can also track their drug regimens through a smartphone app.

The impetus for such healthcare advances is nothing new, but IoMT now puts them within reach of health service providers, and even insurers. Technology has advanced, mostly through greatly improved computing power and connectivity, to provide a foundation for what might be called next gen healthcare.

None of this is some futuristic vision. IoMT infrastructure is already reshaping the healthcare industry, just as it has for manufacturing, energy, smart cities, retail, transportation and more. Most observers expect IoMT to quickly spread as wireless connectivity becomes even faster and more pervasive. IoMT networks, linking a wide variety of sensors, computing devices and analytic software applied to fast-streaming data, can be deployed today to connect patients with personalized medicine.  

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Once the technology has been implemented, the above changes can happen effortlessly, without human-to-human or human-to-computer interaction.

Optimized staffing and workflow

When an IoMT is implemented, significant value can be gained through operational improvements, which boost efficiencies that enhance quality of care and simultaneously reduce costs. Additional benefits can be gained through clinical improvements, which enable faster and more accurate diagnoses and a more patient-centric, scientific determination of the best therapeutic approach for better health outcomes.

IoMT in healthcare dramatically optimizes workflow and staffing. Even a basic IoMT solution can collect and bring together such data as staff location and expertise, patient acuity and location, and availability and location of critical diagnostic and therapeutic equipment.

With analytics, this data can help managers improve workflow and make better staffing and scheduling decisions. The data can also be used to understand the movement of people and assets, and to predict where staffing and equipment will be most needed the next day, or in the weeks ahead. Ideally, healthcare facilities will be able to move to appropriate dynamic, on-demand scheduling and resource allocation schemes. This ensures that the right people are assigned to the right places to efficiently deliver quality care while improving staff morale and patient satisfaction.

Fewer false alarms

The critical problem of alert fatigue in clinical care delivery is another pain point that can be addressed with IoMT. This occurs when care providers receive too many clinical alerts and become desensitized over time. With up to 99 percent of alerts being false alarms, alert fatigue is a life-threatening epidemic in healthcare settings, directly responsible for growing numbers of patient injuries and deaths.

With IoMT in healthcare, there are many ways to improve patient care and safety. For example, hospitals can use smart, connected monitoring devices that are linked to patient records, pharmacy systems, room location, nursing staff schedules and more. The sensors in these smart devices collect data, which is integrated with other medical device and system data and then analyzed to determine whether to trigger a silent alarm for a noncritical event or an audible alarm for a life-critical event.  In this way, IoMT will increase confidence in alarms, reduce work load and drive timely action—keeping patients safer.

Better diagnoses, better outcomes

The greatest opportunities for IoMT in healthcare may lie in helping clinicians make faster, more accurate diagnoses and more precise, personalized treatment plans. These capabilities can improve outcomes, reduce costs and ultimately provide greater access to high-quality care for more people across the globe.

Healthcare IoMT technology can integrate and analyze diverse types of diagnostically relevant data and move it to clinical decision support systems. Providers using these systems will have a more complete picture of each patient’s health, as well as the tools to make faster and more precise treatment recommendations. Such opportunities are already being realized in the diagnosis and treatment of sepsis, where speed and accuracy are critical to saving patients’ lives.

Such examples of how IoMT in healthcare shows that collecting granular patient data at frequencies previously unimaginable is within reach—not just when people are sick or in a hospital, but where people live and work. Think of the potential in clinical trials, as well. This data can be combined with behavioral, physiological, biochemical, genetic, genomic and epigenetic data and more.

Analytics will be able to detect new, previously hidden or unknown patterns and relationships between data, diagnoses, treatments and patient outcomes. The result will be next-generation expert systems that will eventually develop a level of autonomy in diagnosis and treatment. We’ll soon see them routinely assisting physicians and nurse practitioners, helping them provide high-quality care and achieve better patient outcomes at a lower cost.

The high volume and broad scope of healthcare IoMT data makes it possible to develop powerful learning and adaptive diagnostic and therapeutic models. The greatest opportunities for healthcare IoMT potentially lie in helping clinicians make faster, more accurate diagnoses and more precise, personalized treatment plans. This can improve outcomes, reduce costs and ultimately provide greater access to high-quality care globally.

 

Mark Wolff, Ph.D., has more than 25 years of experience in the health and life science industries as a scientist and analyst. His areas of expertise include the development and application of advanced and predictive analytics in health care and life sciences, with an interest in outcomes and safety. His current work focuses on methods and application of machine learning to real-time sensor/IoMT data, supporting safety research, visualization and development of intelligent decision support systems.


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Research: By 2023, 5M Consumers will be Remotely Monitored by Providers

January 15, 2019
by Rajiv Leventhal, Managing Editor
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Wearables, including health trackers and remote patient monitoring devices, are set to become “must-haves” in the delivery of healthcare, with an annual spend on these devices reaching an estimated $20 billion by 2023, according to new research.

The exploration into mobile devices, and more broadly digital health, conducted by Juniper Research, also noted that assistive “hearables,” or connected hearing aids made available via healthcare providers, as well as directly to customers at varying price models, will mean this sector generates revenues of over $40 billion by 2022. Together, healthcare spending in the wearables market is projected to reach $60 billion by 2023, according to the report.

The research found that adoption of healthcare wearables will be driven by improvements in remote patient monitoring technology, in addition to increased adoption by medical institutions. Juniper forecasts that 5 million individuals will be remotely monitored by healthcare providers by 2023.

The research also forecasts that the advanced ability of AI (artificial intelligence)-enabled software analytics to proactively identify individuals at risk of their condition worsening will witness increased confidence among medical practitioners and regulators with regard to sensor accuracy.

As wearables become part of patients’ treatment plans, manufacturers will seek to adjust their business models and generate revenues from devices being monitored. An example of this would be selling data produced by the devices to insurance providers, the researchers noted. Juniper forecasts that service revenues of this nature will reach $855 million by 2023.

However, data privacy and consent will continue to be a significant barrier. Improving healthcare systems, such as using AI-enabled software analytics, is contingent on patient data being anonymized. Some insurance providers are changing the dynamics; in order to be covered, they require a data feed from the policyholder’s device, the researchers explained.

Research author Michael Larner stated, “It is vital that patients are made aware of how their personal data will be used. If not, making wearables a ‘must-have’ to provide personalized care or receive medical insurance risks a backlash from patients and heightened regulatory scrutiny; stalling the effectiveness of remote monitoring.”

To this end, in a recent study published in Health Affairs, researchers looked at new digital health solutions—both consumer-facing and provider-facing—and concluded that while there is real collective potential in these solutions to address significant healthcare challenges, to date, there simply have not been studies that evaluated effectiveness in terms of reducing cost or improving access to care. Furthermore, clinical effectiveness studies with a high level of evidence were uncommon, according to this research.

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UC San Diego Health Publishes Early Research on Apple Health Records Feature

January 14, 2019
by Heather Landi, Associate Editor
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The first research on Apple’s Health Records initiative has been released and the research findings indicate that patients are generally satisfied with the app’s ease-of-use and feel that it improves their understanding of their health and has facilitated conversations with clinicians.

The University of California San Diego Health was an early adopter of the Apple Health Records feature, as one of the first 12 health systems to test the app, and researchers set out to gauge the initial reactions of patients to the new platform. The research, led by Christian Dameff, M.D., department of emergency medicine; Brian Clay, M.D., department of medicine, and Christopher Longhurst, M.D., department of pediatrics as well as CIO and associate CMO at UCSD Health, was recently published in the Journal of the American Medical Association.

In January 2018, Apple announced that it would be testing the Health Records feature out with 12 hospitals, inclusive of some of the most prominent healthcare institutions in the U.S. Then in March, Apple tripled the number of health systems participating, from 12 to 39, and announced that the new capability was available to all iPhone users with the latest iOS 11.3 update. As of a January 10 update from Apple, about 160 provider institutions are on board with the project.

The gauge patients’ responses, UCSD Health researchers sent a brief, three-question, anonymous online survey to 425 patients who activated the personal health record feature in 2018. Of the 132 patients responded, 96 percent indicated that they could easily connect their mobile devices to the platform and 78 percent said they were satisfied with using the feature. What’s more, 90 percent of survey respondents said the smartphone solution improved their understanding of their own health, facilitated conversations with their clinicians, or improved sharing of personal health information with friends and family. However, less than half (48 percent) reported improvement with all three of these outcomes, according to the research.

As of fall 2018, UC San Diego Health has hundreds of personal health record users who have downloaded thousands of clinical results and other pieces of medical information though the platform.

“As with many other new products and solutions, such enthusiasm is common from early adopters,” the researchers note. “The platform will need to prove that it is useful, sustainable, scalable, and actually improves health outcomes.”

The key questions are whether this personal health record will improve patient outcomes and lower costs while also increasing quality, the researchers wrote. “Three key developments may contribute to success: the ubiquity of mobile technology, the maturation of health data communications standards, and the widespread use of mobile software distribution platforms,” Drs. Dameff, Clay and Longhurst wrote.

The researchers note that interoperable personal health records are not a novel concept; unsuccessful attempts to collect digital patient records have been pursued by several major technology companies.

When Microsoft introduced HealthVault (2007) and Google launched Google Health (2008) personal health records, the first iPhone and Android devices had just been released, according to the UCSD Health researchers. “The newly launched Android Market and iOS App Store offered just hundreds of apps to download compared with the millions of apps available today. Thousands of apps on these devices are related to health or fitness. Since that time, smartphones have become the de facto standard for communication for consumers,” the researchers wrote.

The researchers also point out that if device manufacturers incorporate these features into the core smartphone operating system, this would lead to wide dissemination of these applications with a simple software update, and could possibly seamlessly push features to millions of patients.

“Although it remains too soon to draw firm conclusions, the continued development of patient-facing health care technologies by well-established technology companies suggests that the digital health care landscape may now be sufficiently mature to foster the broad adoption of personal health records. Whether these technological advances ultimately improve patient outcomes, lower costs, and improve quality remain the most important unanswered questions,” the UCSD Health researchers wrote.

The UCSD research is the first to gauge patients’ responses to the Apple Health Records feature, however, back in May, Orem, Utah-based KLAS Research published a report that gathered feedback from executives at all 12 of the early adopter health systems that partnered with Apple through the beta process. The report sought to validate the significance of Apple’s move. KLAS researchers spoke with healthcare executives at participating health systems to gauge their experience.

KLAS researchers note in the report that Apple’s revelation created a stir for at least a few reasons: Apple is a consumer-oriented healthcare outsider; Apple is attempting to make inroads where peers Google and Microsoft have failed; and the feature has the potential to impact millions of patients given the iPhone’s broad customer base.

According to the report, early participants say that Apple’s move is not just a marketing ploy and that it has both short-term benefits and long-term potential to impact how provider organizations interact with patients and how patients manage their health.

Close to 60 percent of respondents say they expect Apple’s “ready-to-go” patient-record portability to have an immediate positive impact, within zero to six months. Another 33 percent expect to see benefits from the Apple health records feature within six to 12 months. According to the report, one CIO said, “Honestly, there are no hurdles for us. The work effort to turn the app on is measured in days. There is no IT team that needs to do anything fancy or complicated. The wonder of this app is that the lift is tiny and the benefit is huge.”

KLAS researchers also looked at next steps and participants indicated that Health Records’ impact will depend on Apple’s ability to scale education, adoption, and data complexity. The KLAS report notes that there are over 2,000 hospital-based health systems in the U.S., and, in order to reach more than one-third of these, Apple will need to expand to EMR vendors beyond their current partners (athenahealth, Cerner, and Epic). According to KLAS data, back in March, of all the acute care hospitals in the U.S., only 5 percent are among the health systems that are participating. What’s more, 48 percent of hospitals of non-participating health systems use Apple partner EMRs (athenahealth, Cerner and Epic) and 47 percent of hospitals are currently using other EMRs (Allscripts, CPSI, Meditech).

“In terms of capabilities, participants say that being able to upload data back into the EMR will be vital and that eventually Health Records’ data model will need to support more detailed data than the C-CDA data elements handled today,” according to the KLAS report.

The KLAS report notes that, eventually, if this general capability is going to benefit all patients in the U.S., it will need to expand beyond Apple to other smartphone vendors. One CIO stated, “If an Android version becomes available that will be a home run for a lot of people.”

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New Health Affairs Study: Effectiveness of Digital Health Solutions Remains Unproven

January 7, 2019
by Mark Hagland, Editor-in-Chief
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A review of the research around the new digital health solutions by a team of healthcare policy researchers finds those solutions’ effectiveness remains largely unevaluated

How effective are new digital health solutions—both consumer-facing and provider-facing—including “health care, health diagnostics, hospital, personal health, health insurance, analytics, big data, mobile apps, wearables, cloud data services, wellness, predictive analytics, mobile devices, data mining, biometrics, [and] home healthcare”—proving to be, in impacting health status? The reality is that even the serious study of such solutions remains in its infancy.

In that context, a group of healthcare policy researchers has just published an article in the January issue of Health Affairs. The researchers—Kyan Safavi, Simon C. Mathews, David W. Bates, E. Ray Dorsey, and Adam B. Cohen, have authored an article entitled “Top-Funded Digital health Companies And Their Impact On High-Burden, High-Cost Conditions,” in which they look at the extent to which other researchers have studied the effectiveness of all these solutions.

There is real collective potential in these solutions. As the authors point out, “Digital health companies hold promise to address major health care challenges, though little has been published on their impact. We identified the twenty top-funded private US-based digital health companies to analyze their products and services, related peer-reviewed evidence, and the potential for impact on patients with high-burden conditions. Data analytics (including artificial intelligence and big data) was the most common company type. Companies producing biosensors had the greatest funding. Publications were concentrated among a small number of companies. Healthy volunteers were most commonly studied. Few studies enrolled high-burden populations, and few measured their impact in terms of outcomes, cost, or access to care. These data suggest that leading digital health companies have not yet demonstrated substantial impact on disease burden or cost in the US health care system. Our findings indicate the importance of fostering an environment, with regard to policy and the consumer market, that encourages the development of evidence-based, high-impact products.”

What’s more, the authors note, “The necessary ingredients for the meaningful and widespread adoption of digital health technologies by patients and providers are falling into place: 70 percent of physicians report using mobile or smart devices as a part of their practice, and 80 percent of the US population has used at least one digital health application or technology.”

On the other hand, the task of analyzing the effectiveness of these digital solutions is a gargantuan one. As the authors point out, “[T]he digital health industry has become more crowded and diverse, as well as better funded. A 2016 study reported that 259,000 digital health apps were available for consumers. Global consumers are expected to spend $49 billion by 2020 on digital health solutions. A record number (296) of private digital health companies received venture funding in 2016, funding that totaled more than $4.2 billion that year and approached $6 billion the following year. As digital health companies have grown, digital technologies have attracted interest from US governmental bodies—as demonstrated by the Precision Medicine Initiative of the administration of President Barack Obama and the Digital Health Program of the Food and Drug Administration (FDA).”

So what have these researchers learned? While “Digital health products have the potential to help manage high-burden populations that account for the most morbidity, mortality, and cost in the US health care system,” they state bluntly that, “[A]mong a subset of leading private digital health companies in the U.S., we found relatively few studies published in the peer-reviewed literature. Most of those we found evaluated the product or service in healthy patients; high-burden populations were less commonly targeted. These results give reason to assume that digital health products and services from leading companies have had limited impact on disease burden and cost in the U.S. health care system in their current form and implementation.”

Indeed, the authors write, “We found no studies that evaluated effectiveness in terms of reducing cost or improving access to care. Furthermore, clinical effectiveness studies with a high level of evidence were uncommon (that is, there were few randomized controlled trials demonstrating clinical effectiveness). This may explain why the studies were largely published in lower-impact journals. Health care organizations attempting to identify which digital products to purchase might find such data essential,” they add.

As a result, they write, “We believe that the findings of this study indicate the importance of building an environment to encourage the digital health industry to build products and services that are impact focused and evidence based and that provide high value for patients and the health care system. Two areas that policy makers may address to foster such an environment are clarifying the regulatory requirements for such technologies and developing incentives that lead to a stronger customer market.”

Looking forward, the researchers note that, “To encourage innovation among companies aiming to solve critical challenges in the health care system, the FDA has created several guidance documents for app developers intended to clarify the scope of products requiring approval and the standards that these products must meet. FDA Commissioner Scott Gottlieb emphasized his hope that such clarity would help support the creation of digital technologies that, for example, empower patients to monitor and manage their chronic health conditions; enable better clinical decision making, diagnosis, and treatment; and help address public health crises.”

In addition, they note, “Another factor that may explain why the digital health companies in our study demonstrated little impact on key health care metrics or in high-burden patient populations might be the complex, challenging nature of the customer market for products with such characteristics. In contrast, wellness products that use direct-to-consumer approaches may present larger market opportunities.”

Ultimately, the authors write, “The value-based purchasing trend could encourage the market to produce more evidence-based, high-value digital health products. Value-based purchasing should incentivize providers to seek methods that improve outcomes and reduce costs. The Medicare Access and CHIP Reauthorization Act (MACRA) of 2015 represents the strongest recent federal legislation to encourage a shift to value-based care.32 If value-based purchasing becomes the dominant model, digital health tools that have evidence-based value will be in demand. However, to expect value-based purchasing to drive digital health companies might not yet be realistic, because fee-for-service models continue to dominate patient care reimbursement.”  As a result, they state, “To incentivize the adoption of impact-focused products in the health care system, several approaches could serve as a bridge while value-based purchasing expands.”

Ultimately, the researchers conclude, “Digital health represents a new and expanding field with substantial promise to address major health care challenges in high-cost, high-burden patient populations. In this cross-sectional observational study of top-funded digital health companies, we found that few companies studied their products and services in high-cost, high-burden populations or measured their impact in terms of key health metrics such as outcomes, costs, or access. Most studies were of healthy patients, congruous with the direct-to-consumer approach that many digital health companies have taken to bring their products to market. These findings indicate the importance of building an environment to encourage the digital health industry to build products and services that are impact focused and evidence based and that provide value for high-cost, high-burden patients. Two areas in which policy makers could help foster such an environment are in clarifying the regulatory landscape around these products and incentivizing an impact-focused customer market.”

 

 

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