发表状态 | 已发表Published |
题名 | Patient regional index: a new way to rank clinical specialties based on outpatient clinics big data |
作者 | |
发表日期 | 2024-12-01 |
发表期刊 | BMC Medical Research Methodology
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ISSN/eISSN | 1471-2288 |
卷号 | 24期号:1 |
摘要 | Background: Many existing healthcare ranking systems are notably intricate. The standards for peer review and evaluation often differ across specialties, leading to contradictory results among various ranking systems. There is a significant need for a comprehensible and consistent mode of specialty assessment. Methods: This quantitative study aimed to assess the influence of clinical specialties on the regional distribution of patient origins based on 10,097,795 outpatient records of a large comprehensive hospital in South China. We proposed the patient regional index (PRI), a novel metric to quantify the regional influence of hospital specialties, using the principle of representative points of a statistical distribution. Additionally, a two-dimensional measure was constructed to gauge the significance of hospital specialties by integrating the PRI and outpatient volume. Results: We calculated the PRI for each of the 16 specialties of interest over eight consecutive years. The longitudinal changes in the PRI accurately captured the impact of the 2017 Chinese healthcare reforms and the 2020 COVID-19 pandemic on hospital specialties. At last, the two-dimensional assessment model we devised effectively illustrates the distinct characteristics across hospital specialties. Conclusion: We propose a novel, straightforward, and interpretable index for quantifying the influence of hospital specialties. This index, built on outpatient data, requires only the patients’ origin, thereby facilitating its widespread adoption and comparison across specialties of varying backgrounds. This data-driven method offers a patient-centric view of specialty influence, diverging from the traditional reliance on expert opinions. As such, it serves as a valuable augmentation to existing ranking systems. |
关键词 | Outpatient big data Patient regional index Representative points of statistical distributions Specialty influence Two-dimensional assessment model |
DOI | 10.1186/s12874-024-02309-z |
URL | 查看来源 |
收录类别 | SCIE |
语种 | 英语English |
WOS研究方向 | Health Care Sciences & Services |
WOS类目 | Health Care Sciences & Services |
WOS记录号 | WOS:001302526800001 |
Scopus入藏号 | 2-s2.0-85202704338 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/12067 |
专题 | 理工科技学院 |
通讯作者 | Wang, Xiaoguang |
作者单位 | 1.Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science,BNU-HKBU United International College,Zhuhai,China 2.Sun Yat-sen Memorial Hospital,Sun Yat-sen University,Guangzhou,No. 107, Yanjiang West Road, Yuexiu District,China |
第一作者单位 | 北师香港浸会大学 |
推荐引用方式 GB/T 7714 | Peng, Xiaoling,Huang, Moyuan,Li, Xinyanget al. Patient regional index: a new way to rank clinical specialties based on outpatient clinics big data[J]. BMC Medical Research Methodology, 2024, 24(1). |
APA | Peng, Xiaoling, Huang, Moyuan, Li, Xinyang, Zhou, Tianyi, Lin, Guiping, & Wang, Xiaoguang. (2024). Patient regional index: a new way to rank clinical specialties based on outpatient clinics big data. BMC Medical Research Methodology, 24(1). |
MLA | Peng, Xiaoling,et al."Patient regional index: a new way to rank clinical specialties based on outpatient clinics big data". BMC Medical Research Methodology 24.1(2024). |
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