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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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Report: At-Risk Medicare Seniors the Next Apple Watch Target?

January 18, 2019
by Rajiv Leventhal, Managing Editor
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Apple is engaging in discussions with multiple private Medicare insurers about subsidizing the cost of the Apple Watch for at-risk people over 65 years old, according to a CNBC report.

This week’s report, from health tech reporter Christina Farr, noted, “The insurers are exploring ways to subsidize the cost of the device for those who can't afford the $279 price tag, which is the starting cost of an older model.”

The latest version of the Apple Watch—the Series 4—was launched in September, and as officials of the tech giant stated at the time, brings “advanced activity and communications features, along with revolutionary health capabilities, including a new accelerometer and gyroscope, which are able to detect hard falls, and an electrical heart rate sensor that can take an electrocardiogram (ECG) using the new ECG app.” This version retails for at least $399.

According to the CNBC report, “Apple has paid a visit to several of the largest insurers in the market, as well as some smaller, venture-backed Medicare Advantage plans. The people declined to be named as the discussions are still private.”

The watch’s electrocardiogram function and fall detection capabilities particularly make it appealing and valuable for seniors; about 19 million seniors, and growing, are enrolled in a Medicare Advantage plan, which are private health plans that receive government payouts for providing services to seniors—about $10,000 per member, on average, Farr’s report noted.

According to Apple, the new Series 4 Apple Watch intermittently analyzes heart rhythms in the background and sends a notification if an irregular heart rhythm such as AFib is detected.  It can also alert the user if the heart rate exceeds or falls below a specified threshold.

And, the Apple Watch’s fall detection function utilizes a next-generation accelerometer and gyroscope, which measures up to 32 g-forces, along with custom algorithms to identify when hard falls occur, the company has stated.

To this end, data from health technology company HealthMine shows that 21 percent of Medicare Advantage beneficiaries use a fitness/activity/steps tracker; and 70 percent currently use digital tools, with a blood pressure monitor being used by 50 percent of beneficiaries.

In this realm, Apple is working with other major insurers as well. It was recently announced that UnitedHealthcare Motion, an employer-sponsored wellness program, is telling its participants they can get a free Apple Watch if they meet the insurer’s daily walking goals over a six-month period. For this initiative, program participants can use the Apple Watch to see how they are tracking against the program’s three daily goals—frequency, intensity, and tenacity—helping integrate physical activity and engagement with their health plan.

Industry observers have already begun to offer some reaction to CNBC’s story. In Farr’s report, Bob Sheehy, the CEO of Bright Health, an insurance start-up with a Medicare Advantage plan, and the former CEO of United Healthcare, spoke to the idea of seniors potentially avoiding expensive care visits by leveraging the device. “Avoiding one emergency room visit would more than pay for the device," said Sheehy.

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PerfectServe Acquisition of Telmediq Consolidates Secure Communication Platforms

January 17, 2019
by David Raths, Contributing Editor
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Combined companies have more than 500,000 clinical users across 250 hospitals, 27,000 physician practices and post-acute care organizations

PerfectServe, a provider of cloud-based clinical communication and collaboration solutions, has acquired competitor Telmediq, a secure communications platform provider for health settings.

Knoxville, Tenn.-based PerfectServe said it plans to support both solutions going forward, taking advantage of each platform’s cloud-based, service-oriented architecture to integrate complementary features.

Telmediq is deployed across 300 healthcare organizations and 80,000 users. It offers a call center solution, nurse mobility, advanced alert and alarm management capabilities, and mass notification functionality. Combined, PerfectServe and Telmediq have more than 500,000 clinical users across 250 hospital sites and 27,000 physician practices and post-acute care organizations. 

PerfectServe said the acquisition is an important step in its effort to build a care team collaboration platform that unifies the entire care team across the continuum, from inpatient, to outpatient, to patients at home. “Our vision is to build a platform that is separate from, transcends, and is fully integrated with the EHR and all other point-of-care technologies,” said Terry Edwards, president and CEO of PerfectServe, in a prepared statement. “The goal is to make it easy for clinicians to overcome persistent care coordination challenges that have existed in the industry for years.”

The Telmediq acquisition follows an investment last year in PerfectServe by private equity firm K1 Investment Management.

 

 

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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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