Addressing the variation of post-surgical inpatient census with computer simulation.

OBJECTIVE: This study describes the development of a Discrete Event Simulation (DES) of a large pediatric perioperative department, and its use to compare the effectiveness of increasing the number of post-surgical inpatient beds vs. implementing a new discharge strategy on the proportion of patients admitted to the surgical unit to recover. MATERIALS AND METHODS: A DES of the system was developed and simulated data were compared with 1 year of inpatient data to establish baseline validity. Ten years of simulated data generated by the baseline simulation (control) was compared to 10 years of simulated data generated by the simulation for the experimental scenarios. Outcome and validation measures include percentage of patients recovering in post-surgical beds vs. "off floor" in medical beds, and daily census of inpatient volumes. RESULTS: The proportion of patients admitted to the surgical inpatient unit rose from 79.0% (95% CI, 77.9-80.1%) to 89.4% (95% CI, 88.7-90.0%) in the discharge strategy scenario, and to 94.2% (95% CI, 93.5-95.0%) in the additional bed scenario. The daily mean number of patients admitted to medical beds fell from 9.3 ± 5.9 (mean ± SD) to 4.9 ± 4.5 in the discharge scenario, and to 2.4 ± 3.2 in the additional bed scenario. DISCUSSION: Every hospital is tasked with placing the right patient in the right bed at the right time. Appropriately validated DES models can provide important insight into system dynamics. No significant variation was found between the baseline simulation and real-world data. This allows us to draw conclusions about the ramifications of changes to system capacity or discharge policy, thus meeting desired system performance measures.

Incorporating discrete event simulation into quality improvement efforts in health care systems.

Quality improvement (QI) efforts are an indispensable aspect of health care delivery, particularly in an environment of increasing financial and regulatory pressures. The ability to test predictions of proposed changes to flow, policy, staffing, and other process-level changes using discrete event simulation (DES) has shown significant promise and is well reported in the literature. This article describes how to incorporate DES into QI departments and programs in order to support QI efforts, develop high-fidelity simulation models, conduct experiments, make recommendations, and support adoption of results. The authors describe how DES-enabled QI teams can partner with clinical services and administration to plan, conduct, and sustain QI investigations.