Patient flow modeling and simulation to study HAI incidence in an Emergency Department

• Published in: Smart health (Amsterdam) Vol. 32; p. 100467
• Main Authors: Murtaza Sara, Sarawat, Thota, Ravi Chandra, Uddin, Md Yusuf Sarwar, Bani-Yaghoub, Majid, Sutkin, Gary, Abourraja, Mohamed Nezar
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-06-2024
• Subjects: Discrete-event simulation; Emergency departments; Healthcare-Associated Infection (HAI); Patient flow; 68U99; Emergency departments; 93C65; Patient flow; Healthcare-Associated Infection (HAI); 62-07; 92C60; Discrete-event simulation
• ISSN: 2352-6483; 2352-6483
• DOI: 10.1016/j.smhl.2024.100467

Healthcare-associated infections (HAIs), or nosocomial infections, refer to patients getting new infections while getting treatment for an existing condition in a healthcare facility. HAI poses a significant challenge in healthcare delivery that results in higher rates of mortality and morbidity as well as a longer duration of hospital stay. While the real cause of HAI in a hospital varies widely and in most cases untraceable, it is popularly believed that patient flow in a hospital—which hospital units patients visit and where they spend the most time since their admission into the hospital—can trace back to HAI incidence in the hospital. Based on this observation, we, in this paper, model and simulate patient flow in an emergency department of a hospital and then utilize the developed model to study HAI incidence therein. We obtain (a) a flowchart of patient movement (admission to discharge) and (b) anonymous patient data from University Health Medical Center for a duration of 11 months (Aug 2022–June 2023). Based on these data, we develop and validate the patient flow model. Our model captures patient movement in different areas of a typical emergency department, such as triage, waiting room, and minor procedure rooms. We employ the discrete-event simulation (DES) technique to model patient flow and associated HAI infections using the simulation software, Anylogic. Our simulation results show that the rates of HAI incidence are proportional to both the specific areas patients occupy and the duration of their stay. By utilizing our model, hospital administrators and infection control teams can implement targeted strategies to reduce the incidence of HAI and enhance patient safety, ultimately leading to improved healthcare outcomes and more efficient resource allocation.