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

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: The researchers 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. The team 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. The results were published in a simple Microsoft Excel toolbox to allow administrators to conduct sensitivity analysis. Results: To achieve the hospital's goal of rooming patients within 30 to 60 minutes of IP bed requests, the model predicted that 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%. Conclusion: 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.