Machine learning is critical to effective and scalable virtual care; allowing clinicians to simultaneously improve outcomes and reduce the cost of care.
With the proliferation of sensors and wearables in the home setting, a new host of data is now available for clinicians. And yet, no human can feasibly and economically make sense of this “deluge” of data. Enter machine learning, which according to Nature, is already showing significant promise augmenting clinicians’ ability to treat Type II Diabetes, analyze skin lesions, and electrocardiograms. According to Accenture, machine learning will save $150 Billion a year in healthcare costs by 2026.
And yet, today’s care model, as is perpetuated by telehealth providers, struggles to adapt and learn from each patient interaction as any learned knowledge that can benefit a population, is effectively lost when clinicians press “end call” after each session.
Machine learning will unlock clinicians' ability to deliver personalized care at population scale.
Effectively “bridging” a capacity gap, machine learning is critical to understanding the “deluge” of data coming from multivariate sensors in the patient’s home.
Enabling clinicians to scale personalized care to thousands of patients, machine learning will not only allow clinicians to practice “top of license” it will also foster a “learning system” that gets smarter with each patient interaction.
Underpinning this opportunity must be an emphasis on transparency and accountability into the drivers of recommendations coming from any machine learning system.