Today’s podcast is produced in partnership with Business of Healthcare. BOH was founded as a forum and information exchange for the decision makers leading hospitals & health systems, physician practices, pharma, device, and senior living as well as government and commercial payers. These leaders, and innovators serving them, join Business of Healthcare interviews to solve the complex issues they face together.
In this episode, host and BOH Executive Producer Matt Hanis speaks with David Young. Young is Chairman of Raiven Health, and he envisions a world where machine learning and automated patient self-reporting lead to better outcomes. In his work, he has articulated a seamless and automated patient experience from reporting history and concerns through diagnosis, treatment, and recovery. He believes the challenges of provider shortages, access, and clinical workflow can be overcome with these innovations.
Raiven Health uses the behavioral health diagnostic and treatment archives of a large behavioral health provider, Centerstone of America, to develop machine learning capabilities.
Young recognizes the need to address patient privacy risks, to establish ethical policies for reporting back to the patient, and the need reward providers for value rather than volume in order for his vision to be realized.
Let us know what you think of this conversation by leaving a comment below!
Machine Learning, a form of Artificial Intelligence, can organize and make meaning from scattered patient clinical data
Centerstone Research Institute, part of not-for-profit behavioral health provider Centerstone of America, created Raiven Health to apply machine learning to its large troves of patient data
Automated patient-reported data collection + applied machine learning make scarce behavioral providers much more efficient
Automating data collection and diagnosis poses privacy concerns; programs deployed by payers or employers must be non-punitive as well as trusted & valued by consumers
Alternative payment models needed to reward behavioral health providers; fee-for-service does not compensate if capital-intensive technology is needed to automate care