Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Hao, Xiaoxiong, Han, Lei, Zheng, Danyang, Jin, Xiaozhi, Li, Chenguang, Huang, Lvshuai, Huang, Zhaohui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375627/
https://www.ncbi.nlm.nih.gov/pubmed/37507799
http://dx.doi.org/10.1186/s12913-023-09803-y
_version_ 1785079074485436416
author Hao, Xiaoxiong
Han, Lei
Zheng, Danyang
Jin, Xiaozhi
Li, Chenguang
Huang, Lvshuai
Huang, Zhaohui
author_facet Hao, Xiaoxiong
Han, Lei
Zheng, Danyang
Jin, Xiaozhi
Li, Chenguang
Huang, Lvshuai
Huang, Zhaohui
author_sort Hao, Xiaoxiong
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09803-y.
format Online
Article
Text
id pubmed-10375627
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-103756272023-07-29 Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China Hao, Xiaoxiong Han, Lei Zheng, Danyang Jin, Xiaozhi Li, Chenguang Huang, Lvshuai Huang, Zhaohui BMC Health Serv Res Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09803-y. BioMed Central 2023-07-28 /pmc/articles/PMC10375627/ /pubmed/37507799 http://dx.doi.org/10.1186/s12913-023-09803-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hao, Xiaoxiong
Han, Lei
Zheng, Danyang
Jin, Xiaozhi
Li, Chenguang
Huang, Lvshuai
Huang, Zhaohui
Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China
title Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China
title_full Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China
title_fullStr Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China
title_full_unstemmed Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China
title_short Assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in China
title_sort assessing resource allocation based on workload: a data envelopment analysis study on clinical departments in a class a tertiary public hospital in china
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375627/
https://www.ncbi.nlm.nih.gov/pubmed/37507799
http://dx.doi.org/10.1186/s12913-023-09803-y
work_keys_str_mv AT haoxiaoxiong assessingresourceallocationbasedonworkloadadataenvelopmentanalysisstudyonclinicaldepartmentsinaclassatertiarypublichospitalinchina
AT hanlei assessingresourceallocationbasedonworkloadadataenvelopmentanalysisstudyonclinicaldepartmentsinaclassatertiarypublichospitalinchina
AT zhengdanyang assessingresourceallocationbasedonworkloadadataenvelopmentanalysisstudyonclinicaldepartmentsinaclassatertiarypublichospitalinchina
AT jinxiaozhi assessingresourceallocationbasedonworkloadadataenvelopmentanalysisstudyonclinicaldepartmentsinaclassatertiarypublichospitalinchina
AT lichenguang assessingresourceallocationbasedonworkloadadataenvelopmentanalysisstudyonclinicaldepartmentsinaclassatertiarypublichospitalinchina
AT huanglvshuai assessingresourceallocationbasedonworkloadadataenvelopmentanalysisstudyonclinicaldepartmentsinaclassatertiarypublichospitalinchina
AT huangzhaohui assessingresourceallocationbasedonworkloadadataenvelopmentanalysisstudyonclinicaldepartmentsinaclassatertiarypublichospitalinchina