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Spatiotemporal Data Set for Out-of-Hospital Cardiac Arrests

Author

Listed:
  • Janiele E. S. C. Custodio

    (Department of Engineering Management and Systems Engineering, School of Engineering and Applied Sciences, The George Washington University, Washington, District of Columbia 20052)

  • Miguel A. Lejeune

    (Department of Decision Sciences, School of Business, The George Washington University, Washington, District of Columbia 20052)

Abstract

We present a spatiotemporal data set of all out-of-hospital sudden cardiac arrests (OHCA) dispatches for the City of Virginia Beach. We also develop a modular toolkit that can be used to process the data and generate problem instances based on user-defined input. The data were collected from multiple sources, and our analysis process was validated by Virginia Beach officials. The data set consists of detailed information about each dispatch made in response to an OHCA; it includes the time the call for service arrived, the response time of the first unit on scene, the address, and the coordinates of each OHCA incident. It also contains detailed spatial information for all existing first-responder stations and both the great-circle and the road distances between all first-responder stations and OHCA incidents. The raw data files were very large in size and were processed using SAS ® , MATLAB, and QGIS. In conjunction with the database, we provide a MATLAB code that allows generating multiple random test instances based on user-defined input. The library of problems can be used in healthcare emergency problems and also for facility location models, bilocation problems, and drone studies. The data set was organized such that it can be readily used by researchers in the field of healthcare operations research and those studying the spatiotemporal distribution of OHCAs. Given the difficulty to access OHCA data at the level of detail we provide, the data set will facilitate the implementation of data-driven models to design emergency medical response networks and to study the distribution of OHCAs. Additionally, the provision of data and the toolkit will be very useful in benchmarking algorithms and solvers, which is valuable to the data-driven optimization community in general. Summary of Contribution: The paper provides a data set of spatiotemporal information out-of-hospital cardiac arrests (OHCAs) for the City of Virginia Beach. The complete data set also includes spatial information about all fire, emergency medical services, and police stations in the city and both the road and haversine distances between each pair of stations and OHCA incident. Additionally, we provide a toolkit to generate random instances based on user input. To the best of our knowledge, it is the first time that an OHCA database is made publicly available in such level of detail, and there is no precedent of such in IJOC. OHCAs are a leading cause of death worldwide, and emergency medical services still encounter difficulties in providing care in a timely manner. Given the criticality of OHCAs, we believe that making this data set publicly available can help the implementation of data-driven models by researchers in the field of operations research.

Suggested Citation

  • Janiele E. S. C. Custodio & Miguel A. Lejeune, 2022. "Spatiotemporal Data Set for Out-of-Hospital Cardiac Arrests," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 4-10, January.
    • Handle: RePEc:inm:orijoc:v:34:y:2022:i:1:p:4-10
    DOI: 10.1287/ijoc.2020.1022
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    References listed on IDEAS

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    1. Cristiana L. Lara & Francisco Trespalacios & Ignacio E. Grossmann, 2018. "Global optimization algorithm for capacitated multi-facility continuous location-allocation problems," Journal of Global Optimization, Springer, vol. 71(4), pages 871-889, August.
    2. Christian Wankmüller & Christian Truden & Christopher Korzen & Philipp Hungerländer & Ewald Kolesnik & Gerald Reiner, 2020. "Optimal allocation of defibrillator drones in mountainous regions," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 785-814, September.
    3. Thije van Barneveld, 2016. "The Minimum Expected Penalty Relocation Problem for the Computation of Compliance Tables for Ambulance Vehicles," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 370-384, May.
    4. Stefan Poikonen & Bruce Golden & Edward A. Wasil, 2019. "A Branch-and-Bound Approach to the Traveling Salesman Problem with a Drone," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 335-346, April.
    5. Laura A. McLay & Maria E. Mayorga, 2013. "A Dispatching Model for Server-to-Customer Systems That Balances Efficiency and Equity," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 205-220, May.
    6. Timothy C. Y. Chan & Derya Demirtas & Roy H. Kwon, 2016. "Optimizing the Deployment of Public Access Defibrillators," Management Science, INFORMS, vol. 62(12), pages 3617-3635, December.
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