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

Dec. 27, 2018
The potential for a healthcare 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?

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.  

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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