The quality of social determinants data in the electronic health record: a systematic review. (19th October 2021)

• Record Type: Journal Article
• Authors: Cook, Lily A; Sachs, Jonathan; Weiskopf, Nicole G
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 1(2022)
• Page Start: 187
• Page End: 196
• Publication Date: 2021-10-19
• Subjects: data quality -- social determinants of health -- healthy equity -- bias -- Hispanic Americans; 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/ocab199
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH). Materials and Methods: We conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process. Results: The most commonly represented quality issue associated with SDoH data is plausibility ( n = 31, 41%). Factors related to race and ethnicity have the largest body of literature ( n = 40, 53%). The first theme, noted in 62% ( n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias ( n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully. Discussion: The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups. Conclusion: Consideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.