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DRG grouping by machine learning: from expert-oriented to data-based method
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576915/ https://www.ncbi.nlm.nih.gov/pubmed/34753472 http://dx.doi.org/10.1186/s12911-021-01676-7 |
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author | Liu, Xiaoting Fang, Chenhao Wu, Chao Yu, Jianxing Zhao, Qi |
author_facet | Liu, Xiaoting Fang, Chenhao Wu, Chao Yu, Jianxing Zhao, Qi |
author_sort | Liu, Xiaoting |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8576915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85769152021-11-10 DRG grouping by machine learning: from expert-oriented to data-based method Liu, Xiaoting Fang, Chenhao Wu, Chao Yu, Jianxing Zhao, Qi BMC Med Inform Decis Mak Research 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. BioMed Central 2021-11-09 /pmc/articles/PMC8576915/ /pubmed/34753472 http://dx.doi.org/10.1186/s12911-021-01676-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 Liu, Xiaoting Fang, Chenhao Wu, Chao Yu, Jianxing Zhao, Qi DRG grouping by machine learning: from expert-oriented to data-based method |
title | 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_short | 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 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576915/ https://www.ncbi.nlm.nih.gov/pubmed/34753472 http://dx.doi.org/10.1186/s12911-021-01676-7 |
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