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A Dataset of Service Time and Related Patient Characteristics from an Outpatient Clinic

Author

Listed:
  • Haolin Feng

    (School of Business, Sun Yat-sen University, Guangzhou 510275, China)

  • Yiwu Jia

    (Lingnan College, Sun Yat-sen University, Guangzhou 510275, China)

  • Siyi Zhou

    (Lingnan College, Sun Yat-sen University, Guangzhou 510275, China)

  • Hongyi Chen

    (Lingnan College, Sun Yat-sen University, Guangzhou 510275, China
    Hangu TCM Innovation and Research Institute, Guangzhou 510627, China)

  • Teng Huang

    (School of Business, Sun Yat-sen University, Guangzhou 510275, China)

Abstract

Outpatient clinics’ productivity largely depends on their appointment scheduling systems. It is crucial for appointment scheduling to understand the intrinsic heterogeneity in patient and service types and act accordingly. This article describes an outpatient clinic dataset of consultation service time with heterogeneous characteristics. The dataset contains 6637 consultation records collected from 381 half-day sessions between 2018 and 2019. Each record includes encrypted session and patient IDs, consultation start and (approximated) end times, the month and day of the week, whether it was on a holiday, the patient’s visit count for a specific medical condition, gender, whether the consultation was cancer-related, and the distance from the patient’s mailing address to the clinic. These features can be used to classify patients into heterogeneous groups in studies of appointment scheduling. Therefore, this dataset with rich, heterogeneous patient characteristics provides a valuable opportunity for healthcare operations management researchers to develop, test, and benchmark the performance of their models and methods. It can also be used for studying appointment scheduling in other service industries. More generally, it provides pedagogical value in areas related to management science and operations research, applied statistics, and machine learning.

Suggested Citation

  • Haolin Feng & Yiwu Jia & Siyi Zhou & Hongyi Chen & Teng Huang, 2023. "A Dataset of Service Time and Related Patient Characteristics from an Outpatient Clinic," Data, MDPI, vol. 8(3), pages 1-15, February.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:3:p:47-:d:1080180
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    References listed on IDEAS

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    1. Guido Kaandorp & Ger Koole, 2007. "Optimal outpatient appointment scheduling," Health Care Management Science, Springer, vol. 10(3), pages 217-229, September.
    2. Erjie Ang & Sara Kwasnick & Mohsen Bayati & Erica L. Plambeck & Michael Aratow, 2016. "Accurate Emergency Department Wait Time Prediction," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 141-156, February.
    3. Zheng Zhang & Xiaolan Xie, 2015. "Simulation-based optimization for surgery appointment scheduling of multiple operating rooms," IISE Transactions, Taylor & Francis Journals, vol. 47(9), pages 998-1012, September.
    4. Francis de Véricourt & Otis B. Jennings, 2011. "Nurse Staffing in Medical Units: A Queueing Perspective," Operations Research, INFORMS, vol. 59(6), pages 1320-1331, December.
    5. Marynissen, Joren & Demeulemeester, Erik, 2019. "Literature review on multi-appointment scheduling problems in hospitals," European Journal of Operational Research, Elsevier, vol. 272(2), pages 407-419.
    6. Seokjun Youn & H. Neil Geismar & Michael Pinedo, 2022. "Planning and scheduling in healthcare for better care coordination: Current understanding, trending topics, and future opportunities," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4407-4423, December.
    7. William P. Millhiser & Emre A. Veral, 2019. "A decision support system for real-time scheduling of multiple patient classes in outpatient services," Health Care Management Science, Springer, vol. 22(1), pages 180-195, March.
    8. Seung Jun Lee & Gregory R. Heim & Chelliah Sriskandarajah & Yunxia Zhu, 2018. "Outpatient Appointment Block Scheduling Under Patient Heterogeneity and Patient No†Shows," Production and Operations Management, Production and Operations Management Society, vol. 27(1), pages 28-48, January.
    9. Refael Hassin & Sharon Mendel, 2008. "Scheduling Arrivals to Queues: A Single-Server Model with No-Shows," Management Science, INFORMS, vol. 54(3), pages 565-572, March.
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