June 22, 2021
By Lalit Panda
Read the original article here: IoT For All
The value of medical IoT (IoMT) is being amplified by the symbiotic growth of machine learning (ML) and artificial intelligence (AI). In processing large amounts of continuously streaming data from connected medical devices, doctors are able to reach actionable conclusions more quickly and reliably.
What is the internet of medical things, or “IoMT” as it’s sometimes called today? With the explosion of IoT Applications across industries, the medical space is no exception. Given the transformation of US healthcare to evidence-based outcomes with incentives that are beginning to align, metrics and patient feedback have become essential for care providers. Payers are increasingly interested in optimizing costs with treatments that are more effective than others.
My personal experience with orthopedic sensors and the analytics possible with these sensors make me feel confident of a couple of things. Data-based care will continue to grow and will have a beneficial impact on both costs and quality of treatment. Having worked in other industries, such as consumer electronics, data-focused customer care and operations have been ingrained in the DNA for a very long time. Over the last decade, healthcare has become more adept at moving the ship in that direction.
The key value that sensors are bringing to healthcare is reducing the time between measurement, detection and treatment. Insulin pumps measure and deliver doses at the appropriate time, albeit with patient action, using blood glucose monitors that have sensors under the skin communicating blood glucose levels to external receivers. In addition, the data analytics capabilities available now add context and meaning to the measurements at a rate much faster than was possible earlier.
How can medical IoT devices improve diagnostics? Devices that track bodily metrics that could indicate medical conditions like diabetes and atrial fibrillation are becoming increasingly available. Key medical parameters like blood chemistry, blood pressure, brain activity and pain levels can be gathered on a continuous basis.
This can help detect early signs of disease onset or activity, improving responses. Causal indicators can be closely tracked with the right targeted sensors, once disease proclivity or risk factor have been identified. Even the most recent version of Apple Watch 4 has been declared as a class 2 medical device, because of features like heart rhythm monitoring and fall detection.
It must be said that most consumer-oriented devices have not gone through the FDA regulatory process to be qualified as a medical device.
Postoperative recovery time for patients is a significant part of the procedure cost, and minimizing that is an essential element of cost reduction. For instance, for a total knee replacement, hospitalization is about two days in the US compared to four to five days in National Health Services (NHS) in the U.K. Beyond the hospital, there is a need to reduce time at an SNF (Skilled Nursing Facility) and Physiotherapy. This can be accomplished by using wearable sensors that assist with exercise, compliance and remote monitoring for issues that might result in revisions if not dealt with timely.
Sensors can track various critical metrics and alert caregivers to respond in time. Sensors combined with telemedicine make it even easier to help speed up recovery. Knowing what patients are doing in between visits can help speed up the recovery time for post-surgical procedures. In fact, a collaboration between Geisinger System and Force Therapeutics over three years has resulted in significantly improved outcomes. That includes a 30 percent reduction in hospital length of stay, a 56 percent reduction in skilled nursing facility utilization, as well as an 18 percent reduction in readmissions, reports Greg Slabodkin in Health Data Management.
Sensors that track bodily parameters are getting increasingly sophisticated with blood pressure, glucose levels, sweat and even tear analysis. The benefit is not only in terms of logistics but also in terms of the frequency of data capture as compared to standardized tests. Mobility sensors can help improve gait and form in case of chronic degenerative diseases like rheumatoid arthritis. Another category of IoMT device application is in the monitoring and response of patients to treatment compliance. In chronic care specifically, poor outcomes and extended recovery can be avoided by measurement and monitoring ideally suited to IoT devices.
Devices that actively engage patients with guided exercise can help prevent injury that requires medical care and associated costs. For instance range of motion of joints in the orthopedic space, or the alignment of posture to prevent cervical spondylosis are examples of how devices can help in prevention. An example device is Upright.
Wearables, for instance, may prevent catastrophic falls for the elderly by checking their activity and noticing anything unusual that might cause loss of balance and a fall. Apple’s watch uses the inbuilt IMU (Inertial Measurement Unit) to identify a fall or likelihood of one. It can even be used for measuring tremors related to nervous system disorders like Parkinson’s disease.
The value of medical IoT is being augmented because of the symbiotic growth of machine learning (ML) and artificial intelligence (AI). In processing large amounts of continuously streaming information from sensor assisted medical devices, data analytics and ML, provide actionable conclusions quicker aiding the therapeutic process.
With streaming information, preventive care can reduce hospitalization and reduce the cost of acute care significantly. That would increase efficiencies and improve patient satisfaction and outcomes. However, some risks of data security in motion and at rest must be carefully evaluated. Moreover, risks of false positive readings can cause undue stress to the patients and the care system. Accuracy, repeatability and reliability are the three essential elements of IoMT that must be always prioritized.