Assessment of structured data elements for social risk factors

• Published in: The American journal of managed care 2022-01, Vol.28 (1), p.e14-e23
• Main Authors: Vest, Joshua R, Adler-Milstein, Julia, Gottlieb, Laura M, Bian, Jiang, Campion, Jr, Thomas R, Cohen, Genna R, Donnelly, Nathan, Harper, Jeremy, Huerta, Timothy R, Kansky, John P, Kharrazi, Hadi, Khurshid, Anjum, Kooreman, Harold E, McDonnell, Cara, Overhage, J Marc, Pantell, Matthew S, Parisi, Wendy, Shenkman, Elizabeth A, Tierney, William M, Wiehe, Sarah, Harle, Christopher A
• Format: Article
• Language: English
• Subjects: Delphi Technique; Electronic Health Records; Health Information Exchange; Humans; Risk Factors
• ISSN: 1088-0224; 1936-2692
• DOI: 10.37765/AJMC.2022.88816

Computable 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. Technical expert panel. A 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). Panelists 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. Routinely 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.