Discrete event simulation model of an acute stroke treatment process at a comprehensive stroke center: Determining the ideal improvement strategies for reducing treatment times

Gizem Koca; John Blake; Gordon Gubitz; Noreen Kamal

doi:10.1016/j.jns.2024.123369

Discrete event simulation model of an acute stroke treatment process at a comprehensive stroke center: Determining the ideal improvement strategies for reducing treatment times

J Neurol Sci. 2024 Dec 26:468:123369. doi: 10.1016/j.jns.2024.123369. Online ahead of print.

Authors

Gizem Koca¹, John Blake², Gordon Gubitz³, Noreen Kamal⁴

Affiliations

¹ Department of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, Canada. Electronic address: [email protected].
² Department of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, Canada; Center for Innovation, Canadian Blood Services, Halifax, Canada.
³ Division of Neurology, Department of Medicine, Dalhousie University, Halifax, Canada; Division of Neurology, QEII - Halifax Infirmary (HI) Site, Nova Scotia Health, Halifax, Canada.
⁴ Department of Industrial Engineering, Faculty of Engineering, Dalhousie University, Halifax, Canada; Division of Neurology, Department of Medicine, Dalhousie University, Halifax, Canada; Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Canada.

PMID: 39742569
DOI: 10.1016/j.jns.2024.123369

Abstract

Background: Fast treatment is crucial for ischemic stroke patients; the probability of good patient outcomes increases with faster treatment. Treatment times can be improved by making changes to the treatment process. However, it is challenging to identify the benefits of changes prior to implementation. Simulation modelling, which mimics the treatment process, can be used to evaluate changes without patient involvement. This study models the acute stroke treatment process using discrete event simulation (DES) and identifies improvement strategies to reduce treatment times.

Method: The model was developed for a comprehensive stroke center in Nova Scotia, using Python. All treatment pathways and sub-tasks were identified via an observational time and motion study conducted in the center. Nine process change scenarios were tested individually and in combinations. The primary outcome measures were door-to-CT time (DTCT), door-to-needle time (DNT), and door-to-groin puncture time (DGPT). The model simulated 500 patients 30 times.

Results: Collecting patient history on the way to the radiology department (rather than in ED) showed the highest reduction among individual scenarios for DTCT (14.2 vs 12.4 min, p < 0.001). Combining all scenarios in the door-to-CT process resulted in a reduction of the DTCT by approximately 28 %. Thrombolysing patients in the imaging department's waiting area resulted in the lowest DNT (39.4 vs 34.8 min, p < 0.001) among all individual scenarios. The highest reduction in DGPT, among all individual scenarios, was achieved by implementing Rapid angiosuite preparation (67.7 vs 51.4 min, p < 0.001). The combinations of all scenarios resulted in the lowest DTCT (14.2 vs 10.1 min, p < 0.001), DNT (39.4 vs 23.0 min, p < 0.001), and DGPT (67.9 vs 38.5 min, p < 0.001).

Conclusions: The study identified various improvement strategies in the acute stroke treatment process through a discrete-event simulation model. Combining all scenarios resulted in significant reductions for all outcome measures.

Keywords: Acute ischemic stroke (AIS); Acute stroke; Discrete event simulation (DES); Endovascular thrombectomy (EVT); Operations research; Simulation; Thrombolysis.