Hospitals have been promised prediction for a decade. The models that survive are the ones wired into workflow: a risk score nobody sees prevents nothing.
Proven use cases
Thirty-day readmission risk, in-patient deterioration early warning, appointment no-show prediction, and staffing demand forecasts. Each has published evidence and clear operational owners.
The deployment discipline
Define the action a score triggers, who takes it, and within what time. Validate on your own population before go-live — models trained elsewhere can miscalibrate badly across demographics.
Monitor for drift and bias
Patient populations shift; models decay. Schedule recalibration and fairness audits as routine operations. Datastrel builds prediction services on hospital data platforms with the monitoring layer included from day one.