Challenges with quality of race and ethnicity data in observational databases

Polubriaginof, Fernanda; Ryan, Patrick B.; Salmasian, Hojjat; Shapiro, Andrea W.; Perotte, Adler J.; Safford, Monika M.; Hripcsak, George; Smith, S.P.; Tatonetti, Nicholas P.; Vawdrey, David K.

doi:10.1093/jamia/ocz113

Cited by 128 publications

(111 citation statements)

References 22 publications

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“…This left us with 22.1 million patients. The race information in the EMR has multiple discrepancies as recently discussed by Polubriaginof and authors 17 and Sholle and authors 18 . We used multiple approaches to resolve data related issues.…”

Section: Methodsmentioning

confidence: 95%

Examining multimorbidity differences across racial groups: a network analysis of electronic medical records

Kalgotra

Sharda

Croff

2020

Sci Rep

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Health disparities across ethnic or racial groups are typically examined through single behavior at a time. The syndemics and multimorbidity health disparities have not been well examined by race. In this study, we study health disparities by identifying the networks of multimorbidities among individuals from seven population groups based on race, including White, African American, Asian, Hispanic, Native American, Bi- or Multi-racial and Pacific Islander. We examined a large electronic medical record (EMR) containing health records of more than 18.7 million patients and created multimorbidity networks considering their lifetime history from medical records in order to compare the network properties among seven population groups. In addition, the networks at organ system level depicting the relationship among disorders belonging to different organ systems are also compared. Our macro analysis at the organ-level indicates that African-Americans have a stronger multimorbidity network followed by Whites and Native Americans. The networks of Asians and Hispanics are sparse. Specifically, the relationship of infectious and parasitic disorders with respiratory, circulatory and genitourinary system disorders is stronger among African Americans than others. On the other hand, the relationship of mental disorders with respiratory, musculoskeletal system and connective tissue disorders is more prevalent in Whites. Similar other disparities are discussed. Recognition and explanation of such differences in multimorbidities inform the public health policies, and can inform clinical decisions as well. Our multimorbidity network analysis identifies specific differences in diagnoses among different population groups, and presents questions for biological, behavioral, clinical, social science, and policy research.

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Section: Methodsmentioning

confidence: 95%

Examining multimorbidity differences across racial groups: a network analysis of electronic medical records

Kalgotra

Sharda

Croff

2020

Sci Rep

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“…7 However, despite efforts to improve the quality of data in EHR systems through the Meaningful Use incentive program, 8 quality of structured OMB race data remains poor, as many patients are missing data or have values discordant from patient self-report. 9 This has a concrete impact on the conduct of observational research or patient cohort discovery reliant on EHR data, given that patients missing structured race data may more likely be from underserved racial groups. 10…”

Section: Background and Significancementioning

confidence: 99%

A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record System

et al. 2020

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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.

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“…First, the study used retrospective patient medical records. There are known inaccuracies in the way that race and ethnicity are recorded in medical records; this in turn may have impacted the accuracy of our results [ 35 ]. Additionally, our analysis compared nonwhite to white patients and did not consider more nuanced categories of racial identity.…”

Section: Discussionmentioning

confidence: 99%

A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

Allen¹,

Mataraso²,

Siefkas³

et al. 2020

JMIR Public Health Surveill

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Background Racial disparities in health care are well documented in the United States. As machine learning methods become more common in health care settings, it is important to ensure that these methods do not contribute to racial disparities through biased predictions or differential accuracy across racial groups. Objective The goal of the research was to assess a machine learning algorithm intentionally developed to minimize bias in in-hospital mortality predictions between white and nonwhite patient groups. Methods Bias was minimized through preprocessing of algorithm training data. We performed a retrospective analysis of electronic health record data from patients admitted to the intensive care unit (ICU) at a large academic health center between 2001 and 2012, drawing data from the Medical Information Mart for Intensive Care–III database. Patients were included if they had at least 10 hours of available measurements after ICU admission, had at least one of every measurement used for model prediction, and had recorded race/ethnicity data. Bias was assessed through the equal opportunity difference. Model performance in terms of bias and accuracy was compared with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score II (SAPS II), and the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE). Results The machine learning algorithm was found to be more accurate than all comparators, with a higher sensitivity, specificity, and area under the receiver operating characteristic. The machine learning algorithm was found to be unbiased (equal opportunity difference 0.016, P=.20). APACHE was also found to be unbiased (equal opportunity difference 0.019, P=.11), while SAPS II and MEWS were found to have significant bias (equal opportunity difference 0.038, P=.006 and equal opportunity difference 0.074, P<.001, respectively). Conclusions This study indicates there may be significant racial bias in commonly used severity scoring systems and that machine learning algorithms may reduce bias while improving on the accuracy of these methods.

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Challenges with quality of race and ethnicity data in observational databases

Cited by 128 publications

References 22 publications

Examining multimorbidity differences across racial groups: a network analysis of electronic medical records

Examining multimorbidity differences across racial groups: a network analysis of electronic medical records

A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record System

A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study

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