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Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory.

, , , , , , , , , , , , , , , , , , , , , , , and . J. Am. Medical Informatics Assoc., 30 (9): 1561-1566 (August 2023)

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Patient perspectives on the linkage of health data for research: Insights from an online patient community questionnaire., , , , , , and . Int. J. Medical Informatics, (2019)Design and analytic considerations for using patient-reported health data in pragmatic clinical trials: report from an NIH Collaboratory roundtable., , , , and . J. Am. Medical Informatics Assoc., 27 (4): 634-638 (2020)Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory., , , , , , , , , and 14 other author(s). J. Am. Medical Informatics Assoc., 30 (9): 1561-1566 (August 2023)Using natural language processing and structured medical data to phenotype patients hospitalized due to COVID-19., , , , , , and . CoRR, (2023)Corrigendum to "Patient perspectives on the linkage of health data for research: Insights from an online patient community questionnaire" Int. J. Med. Inform. 127 (2019) 9-17., , , , , , and . Int. J. Medical Informatics, (2020)Evaluating fitness-for-use of electronic health records in pragmatic clinical trials: reported practices and recommendations., , , , , , and . J. Am. Medical Informatics Assoc., 29 (5): 798-804 (2022)Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory., , , , , , , , , and 11 other author(s). J. Am. Medical Informatics Assoc., 28 (12): 2626-2640 (2021)