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基于工作量评估资源配置:中国一家三甲公立医院临床科室的数据包络分析研究。

Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China.

机构信息

Department of Health Service, General Hospital of Central Theater Command, Wuhan, 430070, People's Republic of China.

Department of Critical Care Medicine, General Hospital of Central Theater Command, Wuhan, 430070, People's Republic of China.

出版信息

BMC Health Serv Res. 2023 Jul 28;23(1):808. doi: 10.1186/s12913-023-09803-y.


DOI:10.1186/s12913-023-09803-y
PMID:37507799
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10375627/
Abstract

OBJECTIVE: Today, the development mode of public hospitals in China is turning from expansion to efficiency, and the management mode is turning from extensive to refined. This study aims to evaluate the efficiency of clinical departments in a Chinese class A tertiary public hospital (Hospital M) to analyze the allocation of hospital resources among these departments providing a reference for the hospital management. METHODS: The hospitalization data of inpatients from 32 clinical departments of Hospital M in 2021 are extracted from the hospital information system (HIS), and a dataset containing 38,147 inpatients is got using stratified sampling. Considering the non-homogeneity of clinical departments, the 38,147 patients are clustered using the K-means algorithm based on workload-related data labels including inpatient days, intensive care workload index, nursing workload index, and operation workload index, so that the medical resource consumption of inpatients from non-homogeneous clinical departments can be transformed into the homogeneous workload of medical staff. Taking the numbers of doctors, nurses, and beds as input indicators, and the numbers of inpatients assigned to certain clusters as output indicators, an input-oriented BCC model is built named the workload-based DEA model. Meanwhile, a control DEA model with the number of inpatients and medical revenue as output indicators is built, and the outputs of the two models are compared and analyzed. RESULTS: Clustering of 38,147 patients into 3 categories is of better interpretability. 14 departments reach DEA efficient in the workload-based DEA model, 10 reach DEA efficient in the control DEA model, and 8 reach DEA efficient in both models. The workload-based DEA model gives a relatively rational judge on the increase of income brought by scale expansion, and evaluates some special departments like Critical Care Medicine Dept., Geriatrics Dept. and Rehabilitation Medicine Dept. more properly, which better adapts to the functional orientation of public hospitals in China. CONCLUSION: The design of evaluating the efficiency of non-homogeneous clinical departments with the workload as output proposed in this study is feasible, and provides a new idea to quantify professional medical human resources, which is of practical significance for public hospitals to optimize the layout of resources, to provide real-time guidance on manpower grouping strategies, and to estimate the expected output reasonably.

摘要

目的:当前,我国公立医院发展模式正由扩张型向效率型转变,管理模式也正由粗放型向精细化转变。本研究旨在评价某三甲公立医院(医院 M)临床科室的效率,分析各科室间医院资源配置情况,为医院管理提供参考。

方法:本研究从医院信息系统(HIS)中提取了医院 M 32 个临床科室 2021 年的住院患者数据,采用分层抽样方法获得包含 38147 例住院患者的数据集。鉴于临床科室间的异质性,本研究基于与工作量相关的标签(包括住院日、重症监护工作量指数、护理工作量指数和手术工作量指数),采用 K-均值算法对 38147 例患者进行聚类,使来自不同质临床科室的患者的医疗资源消耗转化为同质的医务人员工作量。以医生人数、护士人数和床位数为投入指标,以分配到特定聚类的患者人数为输出指标,构建了一个名为基于工作量的 DEA 模型的投入导向 BCC 模型。同时,构建了一个以患者人数和医疗收入为输出指标的控制 DEA 模型,并对两个模型的输出进行比较和分析。

结果:将 38147 例患者聚类为 3 类具有更好的可解释性。在基于工作量的 DEA 模型中,有 14 个科室达到 DEA 效率,在控制 DEA 模型中,有 10 个科室达到 DEA 效率,在两个模型中均达到 DEA 效率的科室有 8 个。基于工作量的 DEA 模型对规模扩张带来的收入增长做出了相对合理的判断,并对一些特殊科室(如重症医学科、老年病科和康复医学科)进行了更合理的评估,更符合中国公立医院的功能定位。

结论:本研究提出的以工作量为产出评价异质临床科室效率的设计是可行的,为量化专业医疗人力资源提供了新思路,对公立医院优化资源配置、为人力分组策略提供实时指导、合理估计预期产出具有重要的现实意义。

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[2]
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本文引用的文献

[1]
Levels, trends, and determinants of effectiveness on the hierarchical medical system in China: Data envelopment analysis and bootstrapping truncated regression analysis.

Front Public Health. 2022

[2]
Factors Influencing the Efficiency of Public Hospitals in Saudi Arabia: A Qualitative Study Exploring Stakeholders' Perspectives and Suggestions for Improvement.

Front Public Health. 2022

[3]
Key Indicators Affecting Hospital Efficiency: A Systematic Review.

Front Public Health. 2022-3-14

[4]
Estimating staffing requirements using workload indicators of staffing need at Braun District Hospital in Morobe Province, Papua New Guinea.

Hum Resour Health. 2022-1-28

[5]
Defining nursing workload predictors: A pilot study.

J Nurs Manag. 2022-3

[6]
Using data envelopment analysis to perform benchmarking in intensive care units.

PLoS One. 2021

[7]
Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis.

Front Public Health. 2021-10-14

[8]
Performance Evaluation of Combining with Medical and Old-Age Care in Pension Institutions of China: A Two-Stage Data Envelopment Analysis.

Risk Manag Healthc Policy. 2021-10-8

[9]
Decoupling of Medical Public-Private Partnership Efficiency and Pollution to Improve Public Health: A Three-Stage DEA Analysis.

Front Public Health. 2021

[10]
A two-stage robust optimization approach for the master surgical schedule problem under uncertainty considering downstream resources.

Health Care Manag Sci. 2022-3

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