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Optimization of the stroke hospital selection strategy and the distribution of endovascular thrombectomy resources

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Abstract

Nowadays, emergency medical technicians (EMTs) decide to send a suspected stroke patient to a primary stroke center (PSC) or to an endovascular thrombectomy (EVT)-capable hospital, based on the Cincinnati Prehospital Stroke Scale (CPSS) and the number of symptoms a patient presents at the scene. Based on existing studies, the patient is likely to have a better functional outcome after three months if the time between the onset of symptoms and receiving EVT treatment is shorter. However, if an acute ischemic stroke (AIS) patient with large vessel occlusion (LVO) is first sent to a PSC, and then needs to be transferred to an EVT-capable hospital, the time to get definitive treatment is significantly increased. For this purpose, We formulate an integer programming model to minimize the expected time to receive a definitive treatment for stroke patients. We then use real-world data to verify the validity of the model. Also, we expand our model to find the optimal redistribution and centralization of EVT resources. It will enable therapeutic teams to increase their experience and skills more efficiently within a short period of time.

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Data availability statement

The data that support the findings of this study are available from National Taiwan University Hospital. Restrictions apply to the availability of these data, which were used under licence for this study. Data are available from Ming-Ju Hsieh with the permission of National Taiwan University Hospital.

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Acknowledgements

We would like to thank the Taipei City Fire Department for their administrative support. This work was supported, in part, by the Ministry of Science and Technology of Taiwan under Grants MOST 107-2221-E-007-074-MY3d

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Authors and Affiliations

  1. Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan

    Chun-Han Wang

  2. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

    Chun-Han Wang & Yu-Ching Lee

  3. Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan

    Ming-Ju Hsieh

Authors
  1. Chun-Han Wang
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  2. Yu-Ching Lee
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  3. Ming-Ju Hsieh
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Correspondence to Yu-Ching Lee.

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Appendix A

Appendix A

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Information Page

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Fig. 4
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Display Page

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Fig. 5
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Operating procedures

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   In this appendix, we introduce the real-time medical treatment decision system as a website. The EMTs will input the relevant information of the suspected stroke patient and get the required result after the back-end calculation. The information of input and output is shown in Table 5.

Steps for using the web-based system: Step 1: Go to the Homepage and choose Acute Stroke. Step 2: On the Acute Stroke page, there are interactive questions and information, which the user needs to answer and fill in (Fig. 3). Step 3: Press the submit button, and the information will be returned to the database and calculated in the back end. Step 4: After it has been calculated, two kinds of results will show on the display page. One will be the results of our model recommending 5 hospitals with the shortest expected time for a patient to receive definitive treatment. The second will recommend 5 hospitals with the shortest routes from the scene to the hospitals. It will also show the time for a patient to receive definitive treatment in each hospital, and the functions provided by each hospital (Fig. 4). Step 5: Select the final choice of hospital, and the result will be kept in the database. The operating procedures are shown in Fig. 5.

We use PHP programming language to create the website with HTML and CSS. In every operation, we send the data back to the database; MySQL records it and sends it to the R language for calculation with the gmapsdistance package to obtain all the drive times; CPLEX computes the minimum expected time, and then returns the results to MySQL for storage, which are finally shown on the display page. The CPU time to solve the model for an optimal solution is 0.12 seconds on average for the instance of Taipei City on a personal computer with Intel Core (TM) i5-6500 CPU @ 3.20GHz and 16G RAM.

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Wang, CH., Lee, YC. & Hsieh, MJ. Optimization of the stroke hospital selection strategy and the distribution of endovascular thrombectomy resources. Health Care Manag Sci (2024). https://doi.org/10.1007/s10729-023-09663-2

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