Healthcare generates more data than almost any other sector and converts less of it into decisions. This paper surveys where AI analytics measurably changes outcomes — predictive risk stratification, clinical decision support, capacity and staffing forecasting, and revenue-cycle operations — and where it does not.
It gives particular attention to the constraints that distinguish healthcare from other analytics domains: fragmented and inconsistently coded source data, interoperability requirements, model explainability expectations from clinicians, and the regulatory environment governing patient data.