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AI-Enabled Risk Stratification: Moving from Risk Scores to Timely Member Action
A member with several months of high utilization is easy to flag. The harder and more valuable task is identifying a member whose risk is changing before the pattern becomes obvious through paid claims. That requires models capable of drawing from a broader mix of information, detecting movement across time, and separating actionable risk from expensive events that a care management program is unlikely to influence. AI can strengthen that process, but health plans still have to decide what risk they want to predict, how scores will affect work queues, which interventions correspond to each signal, and how member responses will feed back into future decisions. Without those connections, a more sophisticated score can become another data point competing for attention.
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