Steve Griffiths: AI creates a great opportunity for automating our administrative processes across health care. There is great promise in driving a better experience by creating more streamlined operations. AI can also help us identify challenging patient cases earlier than we have been able to historically, not only so that we can support people who already have a condition, but also so we can predict and prevent the onset or progression of other conditions.
Mitch Morris: Many of these emerging technologies that are AI-driven will become more widely deployed when there are payment models that support them. When payers and providers have financial alignment and common goals, they can leverage technology to make meaningful things happen. For example, our teams developed an algorithm through machine learning that helps predict which congestive heart failure patients are most at risk for getting sicker and perhaps getting admitted to the hospital. We’ve used that data to send a nurse practitioner to the home of those who are most at risk, and in doing that, we’ve been able to decrease hospital readmissions for patients with congestive heart failure by over 60 percent.
Seth Serxner: With respect to care models, opportunities exist in personalization of messaging and recommendations, as well as care and treatment models. For example, AI can support relevant messaging based on the patient or health plan member’s experience, their individual social and demographic characteristics, their level of prior engagement in services and their modes of engagement. AI gets smarter and learns from the basis of knowledge, which allows messaging to become even more relevant. This is critical in shaping the consumer experience and the consumer’s expectation for tailored communication. Ultimately, AI will support the latest evidence-based approaches that are available and individualized to the consumer.
Brian Solow: Health care data doubles every 73 days, which includes EHR data, pharmacy, labs, medical claims, sensors and genetics. So, we have lots of data, but the bad news is that can be a little overwhelming. We need to think about how we can aggregate it, link and map it, and frame it in that longitudinal patient view so that we can be more holistic. Using machine learning and other forms of AI, we can also extract data from doctors’ notes — and that’s where all the great data is. This data is invaluable, but its unstructured. So, we can use AI to mine those descriptions in those notes and make them available for a more structured analysis. I think that’s a game changer.