Applications, Key Variables, and Implementation Challenges of System Dynamics in Hospital Performance: A Scoping Review

Abstract

Context: The complexities of healthcare delivery require advanced analytical tools beyond traditional performance indicators. System dynamics (SD) models can fill this gap by identifying interrelationships and feedback loops in hospital systems, enabling more comprehensive decision-making. This review discusses the applications of SD models in optimizing hospital performance. Objective: This study aims to comprehensively investigate the application of dynamic system models in analyzing hospital performance, encompassing three primary objectives: Identifying dynamic simulation methodologies, extracting and categorizing key influential variables, and proposing practical strategies for enhancing healthcare service quality. Methods: Following Arksey and O’Malley’s framework, we systematically searched PubMed, Scopus, Web of Science, and Embase (2000 - 2024) using keywords like "system dynamics" AND "hospital performance". Of 473 screened records, 69 studies met inclusion criteria. Data were synthesized via content analysis and thematic coding. Results: The SD modeling accounts for a large share of hospital research, accounting for 79.7% of studies; this trend peaked in 2020, coinciding with the COVID-19 outbreak. The most common focus of these studies was on patient flow optimization (43.5%), followed by resource allocation (21.7%) and crisis management (13%). The emergency department (ED) received the most attention, with 23 studies (33.3%). This was followed by studies with a holistic approach at the hospital level (29%), followed by specific treatment departments (20.3%). Supply chain and support services were also examined in 12 studies (17.4%). In terms of time frame, 43.5% of the studies (30) addressed the immediate needs of hospitals, while 22% (15) targeted medium-term operational trends and 20.2% (14) targeted long-term planning. In total, 171 different variables were examined in these studies, which were classified into four main categories: Resource allocation, patient flow management, clinical outcomes, and financial management. Conclusions: The SD enhances hospital efficiency by optimizing length of stay, resource allocation, and crisis response, particularly during events like the COVID-19 pandemic, improving outcomes in areas like EDs and intensive care units (ICUs). Future research should prioritize equitable SD adoption in low-resource settings, integrate real-time data for better prediction, and standardize hybrid modeling frameworks. Addressing these gaps will enable SD to further help healthcare systems balance cost, quality, and adaptability in dynamic environments.