IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v34y2022i1p4-10.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijoc.2020.1022
    Download Restriction: no

    File URL: https://libkey.io/10.1287/ijoc.2020.1022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    2. Niki Matinrad & Melanie Reuter-Oppermann, 2022. "A review on initiatives for the management of daily medical emergencies prior to the arrival of emergency medical services," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 251-302, March.
    3. Pei, Zhi & Dai, Xu & Yuan, Yilun & Du, Rui & Liu, Changchun, 2021. "Managing price and fleet size for courier service with shared drones," Omega, Elsevier, vol. 104(C).
    4. Yichen Lu & Chao Yang & Jun Yang, 2022. "A multi-objective humanitarian pickup and delivery vehicle routing problem with drones," Annals of Operations Research, Springer, vol. 319(1), pages 291-353, December.
    5. Christian Wankmüller & Maximilian Kunovjanek & Robert Gennaro Sposato & Gerald Reiner, 2020. "Selecting E-Mobility Transport Solutions for Mountain Rescue Operations," Energies, MDPI, vol. 13(24), pages 1-19, December.
    6. Karsu, Özlem & Morton, Alec, 2015. "Inequity averse optimization in operational research," European Journal of Operational Research, Elsevier, vol. 245(2), pages 343-359.
    7. Qiqian Zhang & Xiao Huang & Honghai Zhang & Chunyun He, 2023. "Research on Logistics Path Optimization for a Two-Stage Collaborative Delivery System Using Vehicles and UAVs," Sustainability, MDPI, vol. 15(17), pages 1-20, September.
    8. Stefan Poikonen & Bruce Golden, 2020. "The Mothership and Drone Routing Problem," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 249-262, April.
    9. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2020. "Drone routing with energy function: Formulation and exact algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 364-387.
    10. Amir Ali Nasrollahzadeh & Amin Khademi & Maria E. Mayorga, 2018. "Real-Time Ambulance Dispatching and Relocation," Manufacturing & Service Operations Management, INFORMS, vol. 20(3), pages 467-480, July.
    11. Luigi Di Puglia Pugliese & Francesca Guerriero & Maria Grazia Scutellá, 2021. "The Last-Mile Delivery Process with Trucks and Drones Under Uncertain Energy Consumption," Journal of Optimization Theory and Applications, Springer, vol. 191(1), pages 31-67, October.
    12. Argyris, Nikolaos & Karsu, Özlem & Yavuz, Mirel, 2022. "Fair resource allocation: Using welfare-based dominance constraints," European Journal of Operational Research, Elsevier, vol. 297(2), pages 560-578.
    13. Martin van Buuren & Caroline Jagtenberg & Thije van Barneveld & Rob van der Mei & Sandjai Bhulai, 2018. "Ambulance Dispatch Center Pilots Proactive Relocation Policies to Enhance Effectiveness," Interfaces, INFORMS, vol. 48(3), pages 235-246, June.
    14. C. J. Jagtenberg & S. Bhulai & R. D. Mei, 2017. "Dynamic ambulance dispatching: is the closest-idle policy always optimal?," Health Care Management Science, Springer, vol. 20(4), pages 517-531, December.
    15. Tiniç, Gizem Ozbaygin & Karasan, Oya E. & Kara, Bahar Y. & Campbell, James F. & Ozel, Aysu, 2023. "Exact solution approaches for the minimum total cost traveling salesman problem with multiple drones," Transportation Research Part B: Methodological, Elsevier, vol. 168(C), pages 81-123.
    16. Angelo Auricchio & Stefano Peluso & Maria Luce Caputo & Jost Reinhold & Claudio Benvenuti & Roman Burkart & Roberto Cianella & Catherine Klersy & Enrico Baldi & Antonietta Mira, 2020. "Spatio-temporal prediction model of out-of-hospital cardiac arrest: Designation of medical priorities and estimation of human resources requirement," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-13, August.
    17. Adam Behrendt & Vineet M. Payyappalli & Jun Zhuang, 2019. "Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1358-1381, June.
    18. Soovin Yoon & Laura A. Albert, 2018. "An expected coverage model with a cutoff priority queue," Health Care Management Science, Springer, vol. 21(4), pages 517-533, December.
    19. Madani, Batool & Ndiaye, Malick & Salhi, Said, 2024. "Hybrid truck-drone delivery system with multi-visits and multi-launch and retrieval locations: Mathematical model and adaptive variable neighborhood search with neighborhood categorization," European Journal of Operational Research, Elsevier, vol. 316(1), pages 100-125.
    20. Shuangchi He & Melvyn Sim & Meilin Zhang, 2019. "Data-Driven Patient Scheduling in Emergency Departments: A Hybrid Robust-Stochastic Approach," Management Science, INFORMS, vol. 65(9), pages 4123-4140, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orijoc:v:34:y:2022:i:1:p:4-10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.