Patient regional index: a new way to rank clinical specialties based on outpatient clinics big data.

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Xiaoling Peng, Moyuan Huang, Xinyang Li, Tianyi Zhou, Guiping Lin, Xiaoguang Wang
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引用次数: 0

Abstract

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.

患者区域指数:基于门诊大数据的临床专科排名新方法。
背景:现有的许多医疗排名系统都错综复杂。不同专科的同行评审和评估标准往往不尽相同,导致各种排名系统的结果相互矛盾。因此,亟需一种可理解且一致的专科评估模式:本定量研究以华南某大型综合医院的 10,097,795 份门诊病历为基础,旨在评估临床专科对患者籍贯区域分布的影响。我们提出了患者区域指数(PRI),这是一种利用统计分布代表点原理量化医院专科区域影响的新指标。此外,我们还通过整合患者区域指数和门诊量,构建了衡量医院专科重要性的二维指标:我们计算了 16 个相关专科连续八年的 PRI。PRI 的纵向变化准确反映了 2017 年中国医疗改革和 2020 年 COVID-19 大流行对医院专科的影响。最后,我们设计的二维评估模型有效地说明了各医院专科的不同特点:我们提出了一个新颖、简单、可解释的指数来量化医院专科的影响力。该指数建立在门诊数据的基础上,只需提供患者的籍贯,因此便于在不同背景的专科中广泛采用和比较。这种以数据为导向的方法提供了一种以患者为中心的专科影响力视角,有别于传统的依赖专家意见的方法。因此,它是对现有排名系统的宝贵补充。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
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