AI Hospital Readmission Prediction

Readmission prediction uses clinical, administrative, and sometimes social information to estimate the likelihood of a later hospital return. CMS’s Hospital Readmissions Reduction Program uses condition-specific, risk-standardized measures to assess hospital performance and payment adjustments. A local prediction score is a separate tool: it may help identify patients for additional care-transition support, but it does not establish that a return is avoidable or caused by poor care. AHRQ notes that readmission models may omit health status, illness severity, functioning, or social determinants and may perform poorly. Models also differ in outcome window, data timing, and patient population. A score generated only after discharge may be too late to guide inpatient planning, while an early score may have incomplete data. Risk can reflect barriers to care, so using it to restrict services could worsen inequities. Teams should pair predictions with useful interventions such as medication reconciliation, clear discharge instructions, and follow-up coordination rather than treating scores as judgments about patients. Hospitals should validate a model locally, monitor calibration and errors by subgroup, and measure whether the response improves care. Use CMS measures as defined, rather than treating all readmissions as preventable. Document which variables are available at the time of decision and how staff can override or question a score. Patients should understand what support is offered and retain access to care regardless of model output.