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运用数据包络分析确定初级卫生保健层面医学实验室的最佳规模:确定医学实验室的效率

Defining the Optimal Size of Medical Laboratories at the Primary Level of Health Care with Data Envelopment Analysis: Defining the Efficiency of Medical Laboratories.

作者信息

Lamovsek Nejc, Klun Maja, Skitek Milan, Bencina Joze

机构信息

Institute of Clinical Chemistry and Biochemistry, University Medical Centre Ljubljana, Slovenia.

Faculty of Public Administration, University of Ljubljana, Slovenia.

出版信息

Acta Inform Med. 2019 Dec;27(4):224-228. doi: 10.5455/aim.2019.27.224-228.

Abstract

INTRODUCTION

As an integral part of health care, biomedical laboratories are an important contributor to quality patient care. There are only few studies on technical and economic efficiency in the field of laboratory medicine. Nevertheless, such research is crucial to further optimize public resources.

AIM

The aim of our research is to create and verify a model for defining the scale efficiency of medical laboratories at the primary level of health care.

METHODS

Twenty-one laboratories at the primary level of health care in Slovenia were included in the analysis. The efficiency of medical laboratories was determined using data envelopment analysis. We additionally used hierarchical cluster analysis to determine the homogeneous groups within the analyzed sample of units.

RESULTS

We determined the high technical and pure technical efficiency of the analyzed laboratories. The analysis results showed that changes in work processes represent only a minuscule improvement in efficiency, while more can be achieved through a proper scaling of laboratory services. The impact of the operating scale on the efficiency of laboratories is up to twice as high as the process inefficiency. If we take into account the operating modes of laboratories, the optimal scale of services starts at 237,570 automatic tests.

CONCLUSIONS

We note that increased automation and consolidation of laboratory activities could contribute to a greater efficiency of medical laboratories and consequently reduce public spending. DEA is an appropriate tool for the efficiency analysis of public medical laboratories and of appropriate support for policy creation and evaluation in the field of laboratory medicine.

摘要

引言

作为医疗保健的一个组成部分,生物医学实验室是优质患者护理的重要贡献者。关于检验医学领域的技术和经济效率的研究很少。然而,此类研究对于进一步优化公共资源至关重要。

目的

我们研究的目的是创建并验证一个用于定义初级卫生保健层面医学实验室规模效率的模型。

方法

分析纳入了斯洛文尼亚21个初级卫生保健层面的实验室。使用数据包络分析确定医学实验室的效率。我们还使用层次聚类分析来确定所分析的单位样本中的同质组。

结果

我们确定了所分析实验室的高技术效率和纯技术效率。分析结果表明,工作流程的改变仅带来极小的效率提升,而通过适当调整实验室服务规模可实现更大的效率提升。运营规模对实验室效率的影响高达流程无效率影响的两倍。如果考虑实验室运营模式,最佳服务规模始于237,570次自动检测。

结论

我们注意到,提高实验室活动的自动化程度和整合度有助于提高医学实验室的效率,从而减少公共支出。数据包络分析是公共医学实验室效率分析以及检验医学领域政策制定和评估的适当支持的合适工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7838/7004283/c1de16e7cd13/AIM-27-224-g001.jpg

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