IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v80y2022ics0038012121001385.html
   My bibliography  Save this article

Predicting ambulance offload delay using a hybrid decision tree model

Author

Listed:
  • Li, Mengyu
  • Vanberkel, Peter
  • Zhong, Xiang

Abstract

Ambulance offload delay (AOD) is a growing health care concern in Canada. It refers to the delay in transferring an ambulance patient to a hospital emergency department (ED) due to ED congestion. It can negatively affect the ability of the ambulance service to respond to future calls and reduce the efficiency of the system when the delay is significant. Using integrated historical data from a partnering hospital and an Emergency Medical Services (EMS) provider, we developed a decision-support tool using a hybrid decision tree model to predict the severity of AOD occurring within 1–5 h in an EMS system. The primary objective of this study is to provide an AOD prediction model based on the current system status, hour of the day, and day of the week. With this information, decision-makers can be proactive with efforts to mitigate AOD. Various prediction models are developed with different focuses and forecast periods. This research demonstrates a novel hybrid decision tree method applied with administrative data in a health care setting. A naïve Bayes classifier is first used to remove noisy training observations before decision tree induction. This hybrid decision tree algorithm was tested against the basic classification and regression tree (CART) algorithm, using classification accuracy, precision, sensitivity, and specificity analysis. The results indicate that the hybrid algorithm shows improvements in performance in the classification of the real-world problem. It is anticipated that the prediction model for AOD produced from this study will be directly transferable. It can be generalized to other EMS systems, where predicting AOD is important for efficient operations.

Suggested Citation

  • Li, Mengyu & Vanberkel, Peter & Zhong, Xiang, 2022. "Predicting ambulance offload delay using a hybrid decision tree model," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:soceps:v:80:y:2022:i:c:s0038012121001385
    DOI: 10.1016/j.seps.2021.101146
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012121001385
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2021.101146?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Almehdawe, Eman & Jewkes, Beth & He, Qi-Ming, 2016. "Analysis and optimization of an ambulance offload delay and allocation problem," Omega, Elsevier, vol. 65(C), pages 148-158.
    2. Yu Jin Lee & Sang Do Shin & Eui Jung Lee & Jin Seong Cho & Won Chul Cha, 2015. "Emergency Department Overcrowding and Ambulance Turnaround Time," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-9, June.
    3. Mateo Restrepo & Shane Henderson & Huseyin Topaloglu, 2009. "Erlang loss models for the static deployment of ambulances," Health Care Management Science, Springer, vol. 12(1), pages 67-79, March.
    4. Almehdawe, Eman & Jewkes, Beth & He, Qi-Ming, 2013. "A Markovian queueing model for ambulance offload delays," European Journal of Operational Research, Elsevier, vol. 226(3), pages 602-614.
    5. Rita B Patel & Maya B Mathur & Michael Gould & Timothy M Uyeki & Jay Bhattacharya & Yang Xiao & Nayer Khazeni, 2014. "Demographic and Clinical Predictors of Mortality from Highly Pathogenic Avian Influenza A (H5N1) Virus Infection: CART Analysis of International Cases," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    6. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    7. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
    8. Mengyu Li & Peter Vanberkel & Alix J. E. Carter, 2019. "A review on ambulance offload delay literature," Health Care Management Science, Springer, vol. 22(4), pages 658-675, 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. Mengyu Li & Peter Vanberkel & Alix J. E. Carter, 2019. "A review on ambulance offload delay literature," Health Care Management Science, Springer, vol. 22(4), pages 658-675, December.
    2. Li, Mengyu & Carter, Alix & Goldstein, Judah & Hawco, Terence & Jensen, Jan & Vanberkel, Peter, 2021. "Determining ambulance destinations when facing offload delays using a Markov decision process," Omega, Elsevier, vol. 101(C).
    3. Shuwan Zhu & Wenjuan Fan & Shanlin Yang & Panos M. Pardalos, 2023. "Scheduling operating rooms of multiple hospitals considering transportation and deterioration in mass-casualty incidents," Annals of Operations Research, Springer, vol. 321(1), pages 717-753, February.
    4. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    5. Koen W. de Bock & Arno de Caigny, 2021. "Spline-rule ensemble classifiers with structured sparsity regularization for interpretable customer churn modeling," Post-Print hal-03391564, HAL.
    6. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    7. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    8. Acuna, Jorge A. & Zayas-Castro, José L. & Charkhgard, Hadi, 2020. "Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    9. Shuwan Zhu & Wenjuan Fan & Xueping Li & Shanlin Yang, 2023. "Ambulance dispatching and operating room scheduling considering reusable resources in mass-casualty incidents," Operational Research, Springer, vol. 23(2), pages 1-37, June.
    10. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    11. Amir Rastpour & Armann Ingolfsson & Bora Kolfal, 2020. "Modeling Yellow and Red Alert Durations for Ambulance Systems," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1972-1991, August.
    12. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    13. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
    14. Youngkeun Choi & Jae W. Choi, 2023. "Assessing the Predictive Performance of Machine Learning in Direct Marketing Response," International Journal of E-Business Research (IJEBR), IGI Global, vol. 19(1), pages 1-12, January.
    15. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    16. Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    17. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    18. Amit Neil Ramkissoon & Wayne Goodridge, 2022. "Enhancing the Predictive Performance of Credibility-Based Fake News Detection Using Ensemble Learning," The Review of Socionetwork Strategies, Springer, vol. 16(2), pages 259-289, October.
    19. Narendra Singh & Pushpa Singh & Mukul Gupta, 2020. "An inclusive survey on machine learning for CRM: a paradigm shift," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 447-457, December.
    20. Petra P. Šimović & Claire Y. T. Chen & Edward W. Sun, 2023. "Classifying the Variety of Customers’ Online Engagement for Churn Prediction with a Mixed-Penalty Logistic Regression," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 451-485, January.

    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:eee:soceps:v:80:y:2022:i:c:s0038012121001385. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    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.