Methods for identifying health status from routinely collected health data: An overview
The use of routinely collected health data (RCD) is currently helping to accelerate publications that evaluate the effectiveness and safety of medicines and medical devices. One fundamental step in using these data is developing algorithms to identify health status for use in observational studies....
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Integrative Medicine Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213422024000805 |
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| author | Mei Liu Ke Deng Mingqi Wang Qiao He Jiayue Xu Guowei Li Kang Zou Xin Sun Wen Wang |
| author_facet | Mei Liu Ke Deng Mingqi Wang Qiao He Jiayue Xu Guowei Li Kang Zou Xin Sun Wen Wang |
| author_sort | Mei Liu |
| collection | DOAJ |
| description | The use of routinely collected health data (RCD) is currently helping to accelerate publications that evaluate the effectiveness and safety of medicines and medical devices. One fundamental step in using these data is developing algorithms to identify health status for use in observational studies. However, the processes and methodologies for determining health status using RCD remain insufficiently understood. While most current methods rely on the World Health Organization’s International Classification of Diseases (ICD) codes, they may not be universally applicable. Although machine learning methods are promising for more accurately identifying health status, they currently remain underutilized in RCD studies. To address these significant methodological gaps, we outline key steps and methodological considerations for identifying health statuses in observational studies using RCD. This review has the potential to reinforce the credibility of findings from observational studies that use RCD. |
| format | Article |
| id | doaj-art-b55338ea694244d2b860c83d444792e5 |
| institution | OA Journals |
| issn | 2213-4220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Integrative Medicine Research |
| spelling | doaj-art-b55338ea694244d2b860c83d444792e52025-08-20T02:07:17ZengElsevierIntegrative Medicine Research2213-42202025-03-0114110110010.1016/j.imr.2024.101100Methods for identifying health status from routinely collected health data: An overviewMei Liu0Ke Deng1Mingqi Wang2Qiao He3Jiayue Xu4Guowei Li5Kang Zou6Xin Sun7Wen Wang8Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, ChinaInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, ChinaInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, ChinaInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, ChinaInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, ChinaDepartment of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada; Center for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, China; Biostatistics Unit, Research Institute at St. Joseph's Healthcare Hamilton, Hamilton, ON, CanadaInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, ChinaInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, China; West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Corresponding authors at: Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, China; Corresponding authors at: Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.The use of routinely collected health data (RCD) is currently helping to accelerate publications that evaluate the effectiveness and safety of medicines and medical devices. One fundamental step in using these data is developing algorithms to identify health status for use in observational studies. However, the processes and methodologies for determining health status using RCD remain insufficiently understood. While most current methods rely on the World Health Organization’s International Classification of Diseases (ICD) codes, they may not be universally applicable. Although machine learning methods are promising for more accurately identifying health status, they currently remain underutilized in RCD studies. To address these significant methodological gaps, we outline key steps and methodological considerations for identifying health statuses in observational studies using RCD. This review has the potential to reinforce the credibility of findings from observational studies that use RCD.http://www.sciencedirect.com/science/article/pii/S2213422024000805Routinely collected health dataHealth statusMachine learning algorithmsRule-based algorithms |
| spellingShingle | Mei Liu Ke Deng Mingqi Wang Qiao He Jiayue Xu Guowei Li Kang Zou Xin Sun Wen Wang Methods for identifying health status from routinely collected health data: An overview Integrative Medicine Research Routinely collected health data Health status Machine learning algorithms Rule-based algorithms |
| title | Methods for identifying health status from routinely collected health data: An overview |
| title_full | Methods for identifying health status from routinely collected health data: An overview |
| title_fullStr | Methods for identifying health status from routinely collected health data: An overview |
| title_full_unstemmed | Methods for identifying health status from routinely collected health data: An overview |
| title_short | Methods for identifying health status from routinely collected health data: An overview |
| title_sort | methods for identifying health status from routinely collected health data an overview |
| topic | Routinely collected health data Health status Machine learning algorithms Rule-based algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S2213422024000805 |
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