ArticleThe American journal of managed care2022
Assessment of structured data elements for social risk factors.
Article in The American journal of managed care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Integrating a risk prediction score in a clinical decision support to identify patients with health-related social needs in the emergency department.JAMIA open · 2025Article
- Physician Documentation of Social Determinants of Health: Results from Two National Surveys.Journal of general internal medicine · 2025Article
- Development and validation of computable social phenotypes for health-related social needs.JAMIA open · 2025Article
- Article
- A Clinical Decision Support System for Addressing Health-Related Social Needs in Emergency Department: Defining End User Needs and Preferences.Applied clinical informatics · 2024Article
- Toward Better Semantic Interoperability of Data Element Repositories in Medicine: Analysis Study.JMIR medical informatics · 2024Article
- Article
- Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review.BMC medical research methodology · 2023Review
- Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology.JMIR medical informatics · 2022Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
objectivesComputable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. STUDY
designTechnical expert panel.
methodsA 2-round Delphi technique included 17 experts with an in-depth knowledge of available EHR and/or HIE data. The first-round identification sessions followed a nominal group approach to generate candidate data elements that may relate to socioeconomics, cultural context, social relationships, and community context. In the second-round survey, panelists rated each data element according to overall data quality and likelihood of systematic differences in quality across populations (ie, bias).
resultsPanelists identified a total of 89 structured data elements. About half of the data elements (n = 45) were related to socioeconomic characteristics. The panelists identified a diverse set of data elements. Elements used in reimbursement-related processes were generally rated as higher quality. Panelists noted that several data elements may be subject to implicit bias or reflect biased systems of care, which may limit their utility in measuring social factors.
conclusionsRoutinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.
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Registered trials
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