Determining future capacity for an Ambulatory Surgical Center with discrete event simulation. (3rd July 2021)

• Record Type: Journal Article
• Authors: Reese, Kayla; Avansino, Jeff; Brumm, Maria; Martin, Lynn; Day, Theodore Eugene
• Is Part Of: International journal of healthcare management. Volume 14:Number 3(2021)
• Page Start: 920
• Page End: 925
• Publication Date: 2021-07-03
• Subjects: Ambulatory surgery center -- discrete event simulation -- healthcare management -- capacity -- pediatric surgery; Health services administration -- Periodicals; Medical care -- Marketing -- Periodicals; 362.1068
• DOI: 10.1080/20479700.2020.1720940
• Languages: English
• ISSNs: 2047-9700

Abstract

ABSTRACT: Background: Ambulatory Surgical Centers (ASC) are providing an increasing number of patients with care for outpatient surgery. They represent a step forward in efficiency and service compared with performing all outpatient surgeries in a hospital setting, allowing that capacity to be reserved for patients requiring hospital stays. When new ASCs are brought online, throughput capacity is either unknown, or estimated from construction schematics. Methods: A discrete event simulation was created to simulate the operations of the Seattle Children's Bellevue ASC, and identify throughput capacity as the number of operating rooms was increased from three to four, while the Post-anesthesia care unit (PACU) remained constant at 14 beds. The model was queried to determine the number of patients who could receive care while minimizing the duration of crowding (occupancy 13 or greater) in the PACU, limiting mean total crowding time to one hour per week. Results: The simulation was validated against current practice, and determined that up to 50 patients per day can be scheduled through four operating rooms, and the resulting mean crowded time in the PACU would be limited to approximately 59 min. Discussion: DES allows hospitals to support strategic decision making through providing predictions of system performance under a variety of loading scenarios. This allows hospital management planners to inform operations with robust analysis and have confidence in the likely outcomes of policy.