Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes
Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those availab...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-05-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e71873 |
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| author | Feiqing Huang Jue Hou Ningxuan Zhou Kimberly Greco Chenyu Lin Sara Morini Sweet Jun Wen Lechen Shen Nicolas Gonzalez Sinian Zhang Katherine P Liao Tianrun Cai Zongqi Xia Florence T Bourgeois Tianxi Cai |
| author_facet | Feiqing Huang Jue Hou Ningxuan Zhou Kimberly Greco Chenyu Lin Sara Morini Sweet Jun Wen Lechen Shen Nicolas Gonzalez Sinian Zhang Katherine P Liao Tianrun Cai Zongqi Xia Florence T Bourgeois Tianxi Cai |
| author_sort | Feiqing Huang |
| collection | DOAJ |
| description |
Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction. |
| format | Article |
| id | doaj-art-86df3ff395794e0bab17ec61e67f2e7f |
| institution | DOAJ |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-86df3ff395794e0bab17ec61e67f2e7f2025-08-20T02:57:36ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-05-0127e7187310.2196/71873Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease OutcomesFeiqing Huanghttps://orcid.org/0009-0008-9193-5149Jue Houhttps://orcid.org/0000-0002-9015-1827Ningxuan Zhouhttps://orcid.org/0000-0002-7623-2276Kimberly Grecohttps://orcid.org/0000-0003-1790-0737Chenyu Linhttps://orcid.org/0009-0007-8275-8622Sara Morini Sweethttps://orcid.org/0009-0002-0206-0114Jun Wenhttps://orcid.org/0000-0001-5067-2647Lechen Shenhttps://orcid.org/0009-0007-1258-5834Nicolas Gonzalezhttps://orcid.org/0009-0003-3661-2316Sinian Zhanghttps://orcid.org/0009-0004-2760-6982Katherine P Liaohttps://orcid.org/0000-0002-4797-3200Tianrun Caihttps://orcid.org/0000-0001-5772-7460Zongqi Xiahttps://orcid.org/0000-0003-1500-2589Florence T Bourgeoishttps://orcid.org/0000-0001-7798-4560Tianxi Caihttps://orcid.org/0000-0002-5379-2502 Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction.https://www.jmir.org/2025/1/e71873 |
| spellingShingle | Feiqing Huang Jue Hou Ningxuan Zhou Kimberly Greco Chenyu Lin Sara Morini Sweet Jun Wen Lechen Shen Nicolas Gonzalez Sinian Zhang Katherine P Liao Tianrun Cai Zongqi Xia Florence T Bourgeois Tianxi Cai Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes Journal of Medical Internet Research |
| title | Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes |
| title_full | Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes |
| title_fullStr | Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes |
| title_full_unstemmed | Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes |
| title_short | Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes |
| title_sort | advancing the use of longitudinal electronic health records tutorial for uncovering real world evidence in chronic disease outcomes |
| url | https://www.jmir.org/2025/1/e71873 |
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