A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record SystemFunding This study received support from New York-Presbyterian Hospital and Weill Cornell Medical College, including the Clinical and Translational Sciences Center (ULI TR000457) and Joint Clinical Trials Office.
Background Although federal regulations mandate documentation of structured race data according to Office of Management and Budget (OMB) categories in electronic health record (EHR) systems, many institutions have reported gaps in EHR race data that hinder secondary use for population-level research focused on underserved populations. When evaluating race data available for research purposes, we found our institution's enterprise EHR contained structured race data for only 51% (1.6 million) of patients.
Objectives We seek to improve the availability and quality of structured race data available to researchers by integrating values from multiple local sources.
Methods To address the deficiency in race data availability, we implemented a method to supplement OMB race values from four local sources—inpatient EHR, inpatient billing, natural language processing, and coded clinical observations. We evaluated this method by measuring race data availability and data quality with respect to completeness, concordance, and plausibility.
Results The supplementation method improved race data availability in the enterprise EHR up to 10% for some minority groups and 4% overall. We identified structured OMB race values for more than 142,000 patients, nearly a third of whom were from racial minority groups. Our data quality evaluation indicated that the supplemented race values improved completeness in the enterprise EHR, originated from sources in agreement with the enterprise EHR, and were unbiased to the enterprise EHR.
Conclusion Implementation of this method can successfully increase OMB race data availability, potentially enhancing accrual of patients from underserved populations to research studies.
Keywordselectronic health records and systems - data quality - data completeness - clinical research informatics - data collection - recruitment
Protection of Human and Animal Subjects
The study was performed in compliance with the “Federal Policy for the Protection of Human Subjects” by the U.S. Department of Health and Human Services and was reviewed by the WCM Institutional Review Board.
Received: 29 May 2020
Accepted: 16 September 2020
25 November 2020 (online)
© 2020. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
- 1 Commonwealth Fund. Who, when, and how: the current state of race, ethnicity, and primary language data collection in hospitals. Commonwealth Fund; . Available at: https://www.commonwealthfund.org/publications/fund-reports/2004/may/who-when-and-how-current-state-race-ethnicity-and-primary. Accessed July 15, 2020
- 2 Office of Management and Budget. Recommendations from the interagency committee for the review of the racial and ethnic standards to the office of management and budget concerning changes to the standards for the classification of federal data on race and ethnicity. Fed Regist 1997; 62 (131) 36873-36945
- 3 Burchard EG, Ziv E, Coyle N. et al. The importance of race and ethnic background in biomedical research and clinical practice. N Engl J Med 2003; 348 (12) 1170-1175
- 4 Vyas DA, Eisenstein LG, Jones DS. Hidden in plain sight — reconsidering the use of race correction in clinical algorithms. N Engl J Med 2020; 383 (09) 874-882
- 5 Risch N, Burchard E, Ziv E, Tang H. Categorization of humans in biomedical research: genes, race and disease. Genome Biol 2002; 3 (07) t2007
- 6 Chen Jr MS, Lara PN, Dang JHT, Paterniti DA, Kelly K. Twenty years post-NIH Revitalization Act: enhancing minority participation in clinical trials (EMPaCT): laying the groundwork for improving minority clinical trial accrual: renewing the case for enhancing minority participation in cancer clinical trials. Cancer 2014; 120 (Suppl. 07) 1091-1096
- 7 Sholle ET, Kabariti J, Johnson SB. et al. Secondary use of patients' electronic records (SUPER): an approach for meeting specific data needs of clinical and translational researchers. AMIA Annu Symp Proc 2018; 2017: 1581-1588
- 8 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med 2010; 363 (06) 501-504
- 9 Polubriaginof FCG, Ryan P, Salmasian H. et al. Challenges with quality of race and ethnicity data in observational databases. J Am Med Inform Assoc 2019; 26 (8-9): 730-736
- 10 Sholle ET, Pinheiro LC, Adekkanattu P. et al. Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation. J Am Med Inform Assoc 2019; 26 (8-9): 722-729
- 11 McDonald CJ, Huff SM, Suico JG. et al. LOINC, a universal standard for identifying laboratory observations: a 5-year update. Clin Chem 2003; 49 (04) 624-633
- 12 Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; 20 (01) 144-151
- 13 Polubriaginof F, Boland MR, Perotte A, Vawdrey D. Quality of race and ethnicity data in electronic health records. AMIA. Available at: https://knowledge.amia.org/amia-59309-cri2016-1.3011827/t004-1.3012641/f004-1.3012642/a097-1.3012734/a099-1.3012729?qr=1. Accessed 2016
- 14 Thyvalikakath TP, Duncan WD, Siddiqui Z. National Dental PBRN Collaborative Group. et al; Leveraging electronic dental record data for clinical research in the national dental PBRN practices. Appl Clin Inform 2020; 11 (02) 305-314
- 15 Koppel R, Lehmann CU. Implications of an emerging EHR monoculture for hospitals and healthcare systems. J Am Med Inform Assoc 2015; 22 (02) 465-471
- 16 Polubriaginof F, Salmasian H, Shapiro AW. et al. Patient-provided Data Improves Race and Ethnicity Data Quality in Electronic Health Records. AMIA. Available at: https://knowledge.amia.org/amia-59309-cri2016-1.3011827/t004-1.3012641/f004-1.3012642/a097-1.3012734/a099-1.3012729?qr=1. Accessed 2016