Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes.

Journal: Journal of medical Internet research
Published Date: May 12, 2025

Abstract

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.

Authors

  • Feiqing Huang
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
  • Jue Hou
    Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.
  • Ningxuan Zhou
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Kimberly Greco
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
  • Chenyu Lin
    Department of Engineering, University of Toronto, Toronto, ON, Canada.
  • Sara Morini Sweet
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Jun Wen
    School of Pharmacy, Second Military Medical University, Shanghai, 200433, China.
  • Lechen Shen
    Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, United States.
  • Nicolas Gonzalez
    Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, United States.
  • Sinian Zhang
    Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, United States.
  • Katherine P Liao
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Tianrun Cai
    Harvard-MIT Center for Regulatory Science, Harvard Medical School, Boston, MA, United States.
  • Zongqi Xia
    Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Florence T Bourgeois
    Harvard-MIT Center for Regulatory Science, Harvard Medical School, Boston, MA, United States.
  • Tianxi Cai
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States.

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