DRG grouping by machine learning: from expert-oriented to data-based method

Abstract Background Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawb...

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Autores principales:	Xiaoting Liu, Chenhao Fang, Chao Wu, Jianxing Yu, Qi Zhao
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Diagnosis-related groups (DRGs) Grouping Machine learning China Healthcare Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/31ec824a340b43ceb3c3c1c11d27c3fd
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spelling	oai:doaj.org-article:31ec824a340b43ceb3c3c1c11d27c3fd2021-11-14T12:29:18ZDRG grouping by machine learning: from expert-oriented to data-based method10.1186/s12911-021-01676-71472-6947https://doaj.org/article/31ec824a340b43ceb3c3c1c11d27c3fd2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01676-7https://doaj.org/toc/1472-6947Abstract Background Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost. Methods This study proposes a method of data-based grouping, designed and updated by machine learning algorithms, which could be trained by real cases, or even simulated cases. It inherits the decision-making rules from the expert-oriented grouping and improves performance by incorporating continuous updates at low cost. Five typical classification algorithms were assessed and some suggestions were made for algorithm choice. The kappa coefficients were reported to evaluate the performance of grouping. Results Based on tenfold cross-validation, experiments showed that data-based grouping had a similar classification performance to the expert-oriented grouping when choosing suitable algorithms. The groupings trained by simulated cases had less accuracy when they were tested by the real cases rather than simulated cases, but the kappa coefficients of the best model were still higher than 0.6. When the grouping was tested in a new DRGs system, the average kappa coefficients were significantly improved from 0.1534 to 0.6435 by the update; and with enough computation resources, the update process could be completed in a very short time. Conclusions As a new potential option, the data-based grouping meets the requirements of the DRGs system and has the advantages of high transparency and low cost in the design and update process.Xiaoting LiuChenhao FangChao WuJianxing YuQi ZhaoBMCarticleDiagnosis-related groups (DRGs)GroupingMachine learningChinaHealthcareComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Diagnosis-related groups (DRGs) Grouping Machine learning China Healthcare Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Diagnosis-related groups (DRGs) Grouping Machine learning China Healthcare Computer applications to medicine. Medical informatics R858-859.7 Xiaoting Liu Chenhao Fang Chao Wu Jianxing Yu Qi Zhao DRG grouping by machine learning: from expert-oriented to data-based method
description	Abstract Background Diagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost. Methods This study proposes a method of data-based grouping, designed and updated by machine learning algorithms, which could be trained by real cases, or even simulated cases. It inherits the decision-making rules from the expert-oriented grouping and improves performance by incorporating continuous updates at low cost. Five typical classification algorithms were assessed and some suggestions were made for algorithm choice. The kappa coefficients were reported to evaluate the performance of grouping. Results Based on tenfold cross-validation, experiments showed that data-based grouping had a similar classification performance to the expert-oriented grouping when choosing suitable algorithms. The groupings trained by simulated cases had less accuracy when they were tested by the real cases rather than simulated cases, but the kappa coefficients of the best model were still higher than 0.6. When the grouping was tested in a new DRGs system, the average kappa coefficients were significantly improved from 0.1534 to 0.6435 by the update; and with enough computation resources, the update process could be completed in a very short time. Conclusions As a new potential option, the data-based grouping meets the requirements of the DRGs system and has the advantages of high transparency and low cost in the design and update process.
format	article
author	Xiaoting Liu Chenhao Fang Chao Wu Jianxing Yu Qi Zhao
author_facet	Xiaoting Liu Chenhao Fang Chao Wu Jianxing Yu Qi Zhao
author_sort	Xiaoting Liu
title	DRG grouping by machine learning: from expert-oriented to data-based method
title_short	DRG grouping by machine learning: from expert-oriented to data-based method
title_full	DRG grouping by machine learning: from expert-oriented to data-based method
title_fullStr	DRG grouping by machine learning: from expert-oriented to data-based method
title_full_unstemmed	DRG grouping by machine learning: from expert-oriented to data-based method
title_sort	drg grouping by machine learning: from expert-oriented to data-based method
publisher	BMC
publishDate	2021
url	https://doaj.org/article/31ec824a340b43ceb3c3c1c11d27c3fd
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DRG grouping by machine learning: from expert-oriented to data-based method

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