Smart, Low-Cost Sensors Are Making Their Way into Homes

Diabetes is very treatable, but managing it often feels like a full-time job. So many factors affect blood glucose levels that it can seem impossible to track them all. Strict diet and exercise regimens are key to keeping glycemic levels in check, in addition to regular insulin doses for some patients. Some patients have to test their blood glucose multiple times daily. But without a regimen in place, patients are at risk for a whole host of comorbidities. So how do diabetic patients contend with all this?
 
Livongo Health, which offers a blood glucose monitor with a companion service designed to coach diabetic patients, is one tool that provides guidance. The company’s cellular-connected monitor takes blood sugar readings and transmits the information to its health monitoring services. If the reading is outside of normal range, that person is flagged and recommended to drink some fruit juice or take a walk to bring blood glucose back to normal levels. If too far from the normal range, it alerts a specialist.
 
Issuing patients “reminders” about managing their disease and alerting a specialist when necessary are just a few of the benefits of using a mobile health (mHealth) tool to proactively manage a chronic illness. mHealth technologies can also save healthcare costs by preventing an unnecessary trip to the emergency room by detecting an inconsistency early. They could also replace a lengthy clinic visit to adjust a treatment, because mHealth platforms typically collect—and sometimes analyze—patient data to determine if a treatment is working.
 

MPO’s October feature story “I’ll Take My Healthcare ‘To Go,’ Please” explored the trends and technological advances driving the mobile health market as well as some of the latest mHealth platforms available to patients. Avner Halperin, CEO of EarlySense, a developer of sensors and analytics for continuous patient monitoring in hospitals, alternate care, and homes, was among the industry experts interviewed for the article. His complete input is included in the Q&A below.

Sam Brusco: What trends are you noticing in mobile health (mHealth) management?

Avner Halperin: In the early days of mobile digital health, it was all about collecting data. Companies and consumers were excited about the various, creative ways data could be collected, reported, and shared. Now the trend is “so what?” Data in itself is not interesting, only actionable analysis that is clear, concise, and can be proven to bring value by improving some outcome the patient or consumer is interested in. Examples include alerts that indicate when a physician must be called, or a clear lifestyle change that will prevent the risk of driving with not enough sleep.
 
Brusco: In what ways does the use of a mobile digital health tool ease the burden on patients managing their illness? How does it ease the burden on healthcare professionals/caregivers?

Halperin: Using a mobile health tool lowers stress, empowers self-management, and empowers family caregivers.
 
Brusco: Which illnesses in particular are made less burdensome to manage using an mHealth tool, and in what ways does the tool lessen that burden?

Halperin: Historically, the best improvement in managing chronic disease through mHealth has been in diabetes. The ability to closely track dynamic glucose levels at home empowers patients to live longer, healthier lives with significantly fewer complications. Diabetes is relatively ‘easy’ to control with a mobile health tool because a single parameter drives a simple, effective self-intervention.

Congestive heart failure, COPD, and asthma are more challenging to measure and control. But recent developments that allow continuous, accurate, and burden-free measurement of cardiac and respiratory parameters at home will empower patients and caregivers to provide proactive care that will significantly improve outcomes. This is crucial with the growing rates of chronic disease.
 
Brusco: What innovative technologies are making mobile health solutions possible?

Halperin: Smart, low-cost sensors are now making their way to the homes as part of the mobile health revolution. Historically, accurate medical data was only collected in hospitals with complex and expensive tools operated by highly trained technicians or nurses. Today, many of these sensors measure vital signs at home or on the move. The abundance of information is only part of the value creation, but it’s a critical first step.
 
The technologies are also enabling personalized medicine. Some examples are EarlySense sensors that very accurately track breathing, cardiac function, sleep, and stress parameters at home at hospital level accuracy. Personalized medicine is enabled further by the ability of companies like 23andMe that sequence personal genomes and DayTwo that analyze personal gut microbiomes—these companies provide consumers with medical grade analysis for a few hundred dollars, which previously cost many thousands (if not tens of thousands) of dollars.
 
Deep learning analytics is also advancing mHealth solutions. The collection of large amounts of data, as mentioned before, is not valuable and interesting in and of its own. In order to drive meaning and outcome, strong, deep learning algorithms are required. Artificial intelligence (AI) is key to delivering such analyses and delivering on the promise of clinically proven outcome improvement. Companies like Zebra Medical Technologies use AI to deliver automatic, accurate medical imaging analysis; AliveCor uses deep learning algorithms to diagnose arrhythmia at home, and EarlySense uses AI to determine when a significant change in heart or breathing parameters may indicate a health risk condition. Data collection and learning from large data sets is done in many dimensions: learning from a person’s own history and patterns on his/her future risks and health changes, learning the data patterns of many people, what a current picture of a person may indicate on his health and what’s his statistical risk of events such as cardiac arrest, and learning from the data of many what largescale population health trends are developing in real time (e.g., are we seeing an outbreak of SARS in a specific city in the US?)
 
Brusco: What are some of the software advances fueling innovative mHealth technologies?

Halperin: Cloud-based distributed computing allows many large data collection and analytics tools to operate effectively in cost effective ways. New AI software platforms are also key for the development of mHealth. As mHealth evolves and becomes even more widely used, cyber protection technologies will become a critical element in every mHealth solution as well.
 
Brusco: What’s the future of mHealth tools?

Halperin: By 2030, big data, wearable sensors, and personalized medicine will all be old news. Developments like robotic surgery and genomically optimized medicine will allow us to live longer and healthier lives. With over 100 million seniors and more than a million centenarians in the U.S., the key challenge will be to improve mental well-being as effectively as we have improved physical health. The most precious resource will be the time of caregivers—professionals and family that will almost be ‘outnumbered’ by seniors. Patients in hospitals and residents at home will be hungry for the human connection. A key objective for medtech in 2030 will be to optimize human interaction. Sensors and analytics will communicate the need for interaction and provide caregivers with the best time to connect/intervene most efficiently, with the right data and advanced analytics. All of this will be focused on optimizing the ‘ROM’ (Return On every Minute) of caregiver time.