StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions

• Published in: Journal of medical systems 2015-10, Vol.39 (10), p.130-130, Article 130
• Main Authors: Devapriya, Priyantha, Strömblad, Christopher T. B., Bailey, Matthew D., Frazier, Seth, Bulger, John, Kemberling, Sharon T., Wood, Kenneth E
• Format: Article
• Language: English
• Subjects: Arrivals; Computer Simulation; Construction; Critical Pathways - statistics & numerical data; Decision making; Decision Support Techniques; Efficiency, Organizational; Electronic health records; Emergency medical care; Health care; Health Informatics; Health Sciences; Hospital Administration; Hospital Bed Capacity - statistics & numerical data; Hospitalization - statistics & numerical data; Hospitals; Humans; Intensive care; Length of Stay; Mathematical models; Medicine; Medicine & Public Health; Models, Statistical; Patient safety; Patient satisfaction; Patients; Quality improvement; Receiving; Reproducibility of Results; Simulation; Statistics for Life Sciences; Systems engineering; Systems-Level Quality Improvement; Time Factors; United States; Waiting Lists
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-015-0325-0

The ability to accurately measure and assess current and potential health care system capacities is an issue of local and national significance. Recent joint statements by the Institute of Medicine and the Agency for Healthcare Research and Quality have emphasized the need to apply industrial and systems engineering principles to improving health care quality and patient safety outcomes. To address this need, a decision support tool was developed for planning and budgeting of current and future bed capacity, and evaluating potential process improvement efforts. The Strategic Bed Analysis Model (StratBAM) is a discrete-event simulation model created after a thorough analysis of patient flow and data from Geisinger Health System’s (GHS) electronic health records. Key inputs include: timing, quantity and category of patient arrivals and discharges; unit-level length of care; patient paths; and projected patient volume and length of stay. Key outputs include: admission wait time by arrival source and receiving unit, and occupancy rates. Electronic health records were used to estimate parameters for probability distributions and to build empirical distributions for unit-level length of care and for patient paths. Validation of the simulation model against GHS operational data confirmed its ability to model real-world data consistently and accurately. StratBAM was successfully used to evaluate the system impact of forecasted patient volumes and length of stay in terms of patient wait times, occupancy rates, and cost. The model is generalizable and can be appropriately scaled for larger and smaller health care settings.