Healthcare AI: measuring what changes for the patient
I was catching up on healthcare news from the past week when I stumbled upon this: https://www.fiercehealthcare.com/finance/healthleap-banks-38m-funding-backed-sequoia-capital-first-round-capital-and-hummingbird - it brings attention to a practical application of healthcare AI: identifying patients whose clinical needs might otherwise be missed. It got me thinking about a broader question around healthcare AI implementations: once an AI model identifies a patient who needs attention, what does the organization do differently, and how will it know whether that change helped? When we evaluate a model, we usually examine how well it predicts. During implementation, we may also track adoption, usage, and the number of alerts generated. These measures tell us whether the technology performs and whether people use it. Establishing patient benefit requires following the prediction further. Following a prediction through care Let’s consider a hypothetical hospital implementing a model to iden...