A decision support simulation model for bed management in healthcare.

PURPOSE: In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions. DESIGN/METHODOLOGY/APPROACH: A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department's performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients. FINDINGS: Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds. ORIGINALITY/VALUE: This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

Large-scale assessment of missed opportunity risks in a complex hospital setting.

In this research, we apply a large-scale logistic regression analysis to assess the patient missed opportunity risks at a complex VA (US Department of Veterans Affairs) hospital in three categories, namely, no-show alone, no-show combined with late patient cancellation and no-show combined with late patient and clinic cancellations. The analysis includes unique explanatory variables related to VA patients for predicting missed opportunity risks. Furthermore, we develop two aggregated weather indices by combining many weather measures and include them as explanatory variables. The results indicate that most of the explanatory variables considered are significant factors for predicting the missed opportunity risks. Patients with afternoon appointment, higher percentage service connected, and insurance, married patients, shorter lead time and appointments with longer appointment length are consistently related to lower risks of missed opportunity. Furthermore, the VA patient-related factors and the two proposed weather indices are useful predictors for the risks of no-show and patient cancellation. More importantly, this research presents an effective procedure for VA hospitals and clinics to analyze the missed opportunity risks within the complex VA information technology system, and help them to develop proper interventions to mitigate the adverse effects caused by the missed opportunities.