Comparing medical history data derived from electronic health records and survey answers in the All of Us Research Program. (9th April 2022)

• Record Type: Journal Article
• Authors: Sulieman, Lina; Cronin, Robert M; Carroll, Robert J; Natarajan, Karthik; Marginean, Kayla; Mapes, Brandy; Roden, Dan; Harris, Paul; Ramirez, Andrea
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 7(2022)
• Page Start: 1131
• Page End: 1141
• Publication Date: 2022-04-09
• Subjects: All of Us -- survey -- electronic health records -- phenotype -- medical history; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac046
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: A participant's medical history is important in clinical research and can be captured from electronic health records (EHRs) and self-reported surveys. Both can be incomplete, EHR due to documentation gaps or lack of interoperability and surveys due to recall bias or limited health literacy. This analysis compares medical history collected in the All of Us Research Program through both surveys and EHRs. Materials and Methods: The All of Us medical history survey includes self-report questionnaire that asks about diagnoses to over 150 medical conditions organized into 12 disease categories. In each category, we identified the 3 most and least frequent self-reported diagnoses and retrieved their analogues from EHRs. We calculated agreement scores and extracted participant demographic characteristics for each comparison set. Results: The 4th All of Us dataset release includes data from 314 994 participants; 28.3% of whom completed medical history surveys, and 65.5% of whom had EHR data. Hearing and vision category within the survey had the highest number of responses, but the second lowest positive agreement with the EHR (0.21). The Infectious disease category had the lowest positive agreement (0.12). Cancer conditions had the highest positive agreement (0.45) between the 2 data sources. Discussion and Conclusion: Our study quantified the agreement of medical history between 2 sources—EHRs and self-reported surveys. Conditions that are usually undocumented in EHRs had low agreement scores, demonstrating that survey data can supplement EHR data. Disagreement between EHR and survey can help identify possible missing records and guide researchers to adjust for biases.