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患者地域指数:一种基于门诊大数据对临床科室进行排名的新方法。

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

机构信息

Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China.

Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107, Yanjiang West Road, Yuexiu District, Guangzhou, China.

出版信息

BMC Med Res Methodol. 2024 Aug 31;24(1):192. doi: 10.1186/s12874-024-02309-z.

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.

摘要

背景

许多现有的医疗保健排名系统都非常复杂。同行评审和评估的标准在不同专业之间往往存在差异,导致各种排名系统之间的结果相互矛盾。因此,非常需要一种易于理解和一致的专业评估模式。

方法

这项定量研究旨在根据中国南方一家大型综合医院的 10097795 份门诊记录,评估临床专业对患者来源的区域分布的影响。我们提出了患者区域指数(PRI),这是一种新的度量标准,用于量化医院专业的区域影响,使用统计分布的代表点原理。此外,还构建了一个二维度量标准,通过整合 PRI 和门诊量来衡量医院专业的重要性。

结果

我们计算了 8 年来 16 个相关专业的 PRI。PRI 的纵向变化准确地捕捉到了 2017 年中国医疗改革和 2020 年 COVID-19 大流行对医院专业的影响。最后,我们设计的二维评估模型有效地说明了医院专业之间的明显特征。

结论

我们提出了一种新颖、简单、可解释的指数,用于量化医院专业的影响。该指数基于门诊数据,仅需要患者的来源,因此便于在不同背景的专业之间广泛采用和比较。这种基于数据的方法提供了一种以患者为中心的专业影响力视角,与传统的依赖专家意见的方法不同。因此,它是对现有排名系统的有益补充。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a5/11365139/4ccb7cfa8923/12874_2024_2309_Fig1_HTML.jpg

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