A data envelopment analysis model for optimizing transfer time of ischemic stroke patients under endovascular thrombectomy

Abstract

This study applies Data Envelopment Analysis (DEA) to optimize transfer times and futile transfers of eligible ischemic stroke patients receiving Endovascular Thrombosis (EVT) in Primary Stroke Centers (PSC) in Nova Scotia. The study aims to assess healthcare delivery in Nova Scotia over two periods. It seeks to improve stroke care for rural populations by examining nine inputs, including age and distance between PSCs and the Comprehensive Stroke Centre (CSC) that provided EVT treatment, concerning a single output variable: whether EVT is performed or not. In the first phase, 115 patients were treated as Decision-Making Units (DMUs) for ten PSCs by applying an input-oriented Variable Returns to Scale (VRS) assisted by super-efficiency analysis using the Python-based PyDEA tool. This tool is known for its unrestricted capacity to handle DMUs, inputs, and outputs. In the second phase, eight PSCs with low patient numbers were merged into four DMUs, each consisting of two PSCs. These two merged PSCs have limited patients, and the selected PSCs are also geographically close. Two PSCs have been kept separate because they had sufficient patient volume. In the first phase, VRS generated more reasonable efficiency scores for evaluation, while in the second phase, Constant Returns to Scale (CRS) outperformed VRS, yielding better results. In the initial stage of the second phase, ten PSCs were considered as six DMUs using the input-oriented CRS and VRS for 115 patients. Super-efficiency measures were applied in this stage to improve the evaluation process further. In the second part of the second phase, a comparison between the first period (2018–2019) and the second period (2020–2021) was conducted using the Malmquist Productivity Index (MPI), considering CRS and VRS to evaluate the relative efficiency and productivity change of six DMUs over time.