A Dataset of Service Time and Related Patient Characteristics from an Outpatient Clinic
Haolin Feng,
Yiwu Jia,
Siyi Zhou,
Hongyi Chen and
Teng Huang (huangt258@mail.sysu.edu.cn)
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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
Teng Huang: School of Business, Sun Yat-sen University, Guangzhou 510275, China
Data, 2023, vol. 8, issue 3, 1-15
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.
Keywords: healthcare systems; appointment scheduling; outpatient clinic; patient heterogeneity; data-driven methods; machine learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:8:y:2023:i:3:p:47-:d:1080180
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