Real-World Telehealth Data Enables Personalized Medical Decision Making

| URAC Staff
Doctor and nurse looking at electronic medical record

To explain the promise of personalization enabled by telehealth, Martin Kohn, M.D., points to the current burden of multiple comorbidities.

Half of Medicare patients are treated for five or more chronic conditions each year, and they account for three-fourths of Medicare spending, according to an Emory University official quoted in a recent New York Times article.

Despite this prevalence, “we have no guidelines for managing patients with multiple chronic diseases,” Kohn said. Instead, medicine relies on randomized clinical trials that are disease oriented rather than patient centered. “You can’t optimally manage the patient by managing the individual diagnoses,” he stated.

In the years ahead, telehealth, specifically remote monitoring, will provide the knowledge needed to move medical practice away from this conventional model toward personalization, Kohn said. With real world data collected by remote monitoring, millions of patients can be combined into cohorts that provide longitudinal information to inform medical decisions on a personal basis.

When a patient has multiple problems, the goal is to optimize the overall outcome for the patient.  Often, especially when routine management of each diagnosis conflicts with each other (such as a patient with symptomatic coronary artery disease and severe asthma), you cannot safely employ the guidelines designed for the individual diseases.  It gets even more complicated when the patient has multiple problems, such as congestive heart failure, chronic obstructive pulmonary disease and diabetes mellitus.

“My argument is that we need a better way to manage conflicting treatments,” said Kohn, keynote speaker at the Telemed Leadership Forum 2019: Transforming Healthcare Delivery in Washington, D.C., April 3-5, 2019. “The only way to learn about that is lots of data over a long period, then mine that data to see which patients do better.”

Helping to design Watson at IBM

As a former chief medical scientist of IBM research, Kohn used big data to help design the Watson supercomputer so it would better support the work of practicing physicians.

More recently, he served as chief medical scientist at Sentrian, creating predictive analytic systems integrating home monitoring with longitudinal health data for patients with complex chronic diseases.

Knowing how patients are doing day to day is essential when building new models of care based on clinical outcomes rather than fee-for-service, Kohn says. “Doctors need to be rewarded for keeping patients safely out of the hospital,” he said. Remote monitoring ensures that patients remain both supported and independent. As part of a larger telehealth program, remote monitoring can reduce costly waste and improve quality outcomes.

Remote monitoring plays an important role in the effort to move from volume to value in healthcare because it shifts the focus from treating individual diseases to treating individuals.

“Inevitable momentum” toward value in healthcare delivery

Continuing to push this transition toward value, Kohn recently became certified in the subspecialty of clinical informatics. Offered since 2011 through the American Board of Medical Specialties, this credential demonstrates medical commitment to using modern data techniques such as machine learning and artificial intelligence.

The performance and adoption of clinical informatics have accelerated in recent years. And Kohn says he sees an “inevitable momentum” in how today’s telehealth and associated technologies are transforming healthcare to a personalized system that improves clinical outcomes while simultaneously reducing costs.

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