Use of a Novel Patient-Flow Model to Optimize Hospital Bed Capacity for Medical Patients

Abstract Background There is no known method for determining the minimum number of beds in hospital inpatient units (IPs) to achieve patient waiting-time targets. This study aims to determine the relationship between patient waiting time–related performance measures and bed utilization, so as to optimize IP capacity decisions. Methods We simulated a novel queueing model specifically developed for the IPs. The model takes into account salient features of patient-flow dynamics and was validated against hospital census data. We used the model to evaluate inpatient capacity decisions against multiple waiting time outcomes: (1) daily average, peak-hour average, and daily maximum waiting times, and (2) proportion of patients waiting strictly more than 0, 1, and 2 hours. We published the results in a simple Microsoft Excel tool to allow administrators to conduct sensitivity analysis. Results To achieve our hospital's goal of rooming patients within 30 to 60 minutes of IP bed requests, our model predicted the optimal daily average occupancy levels should be 89%–92% (182–188 beds) in the Medicine cohort, 74%–79% (41–43 beds) in the Cardiology cohort, and 72%–78% (23–25 beds) in the Observation cohort. Larger IP cohorts can achieve the same queueing-related performance measure as smaller ones, while tolerating a higher occupancy level. Moreover, patient waiting time increases rapidly as the occupancy level approaches 100%. Conclusions No universal optimal IP occupancy level exists. Capacity decisions should therefore be made on a cohort-by-cohort basis, incorporating the comprehensive patient-flow characteristics of each cohort. To this end, patient-flow queueing models tailored to the IPs are needed.