Characterizing the value of predictive analytics in facilitating hospital patient flow

Abstract

We apply discrete event simulation to characterize the patient flow affects of using admission predictions and current state information, generated in an Emergency Department (ED), to influence the prioritization of inpatient unit (IU) physicians between treating and discharging IU patients. Shared information includes crowding levels and total expected bed need (based on the sum of individual patients’ imperfect admission predictions and perfect admission predictions). It is found that sharing prediction and crowding information to influence inpatient staff priorities, using specific information sensitivity schedules, can result in statistically significant (p ≪ 0.05) reductions in boarding time (between 11.69% and 18.38% compared to baseline performance). The range of improvement is dependent on varying simulated hospital configurations.