Detecting Inaccurate Predictions of Pediatric Surgical Durations

Abstract

Accurate predictions of surgical case lengths areuseful for patient scheduling in hospitals. In pediatric hospitals, this prediction problem is particularly difficult. Predictions aretypically provided by highly trained medical staff, but thesepredictions are not necessarily accurate. We present a noveldecision support tool that detects when expert predictions areinaccurate so that these predictions can be re-evaluated. We explore several different algorithms. We provide methodologicalinsights and suggest directions of future work.