As value-based care increasingly becomes the norm in healthcare, it’s driving health systems to find new ways of engaging patients sooner rather than later in the name of keeping them out of the hospital.
In a recent commentary, John Showalter, MD, Chief Product Officer at Jvion, describes both the challenges health systems face and how AI can go a long way toward offering a solution.
One of the core challenges Showalter sees providers struggling with is how to identify those patients most in need of proactive engagement.
“Over the past 18 months we have seen a flurry of patient engagement, communication and outreach platforms come to market,” he notes, “(a)ll of them with the intention of enabling better member engagement. . . . The challenge is, a platform alone does not help a case manager who is likely responsible for managing the care of hundreds of patients, and who every day must decide which individuals they should spend their limited time checking in on. Predictive models can help, but their usefulness only goes so far.”
Here, he says, is where AI can help.
“To save care managers time, prescriptive AI can analyze patients’ data to identify the factors driving their holistic risk — overlaying their medical data with an expanded data set including behavioral, environmental, and other social determinants. This is probably the greatest value of AI: its ability to consider nearly unlimited data sets quickly and with purpose.”
A big part of the problem, Showalter says, is that “(a) patient’s risk trajectory is influenced by more than the medical data stored in the electronic health record: It’s estimated that up to 80% of health outcomes are linked to social determinants of health. . . And yet, care teams have almost no visibility on these risk factors outside of what their patients tell them. By analyzing external data, such as the databases compiled by the US Census and other government agencies, AI can provide insight on these SDOH risk factors, empowering care managers to focus SDOH into their discussions with patients as well as their care plans.”
Furthermore, “AI can analyze historical data and connect the dots on what interventions and engagement strategies worked to improve outcomes for patients with similar risk factors, and provide care managers with patient-centric and clinically-validated recommendations. These insights enable care managers to focus their time with patients on the right questions, resulting in more informed — and ultimately effective — conversations.”
The bottom line, says Showalter, is that “(w)e can no longer wait until patients are having a medical emergency to take action. With its vast data processing power, AI can provide care management teams with the guidance they need to proactively engage patients at risk — in a way that effectively prevents them from needing more intensive (and costly) care in the future.”
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