Issues With Variability in Electronic Health Record Data About Race and Ethnicity: Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave

Abstract

Background The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. Objective This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. Methods At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as “Declined” were grouped with “Refused,” and “Multiple Race” was grouped with “Two or more races” and “Multiracial.” Results “No matching concept” was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. Conclusions Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy.

Pre-existing autoimmunity is associated with increased severity of COVID-19: A retrospective cohort study using data from the National COVID Cohort Collaborative (N3C)

“…Harmonization of these disparate data thus requires assumptions and inferences to be made that could incur systematic biases. Similarly, the ability to accurately determine race within N3C is diminished by variations in how race is reported in different healthcare systems 30 Nonetheless, we highlight the meticulous efforts from the N3C collaborative in evaluating and improving the quality of phenotypes generated within 31 N3C. Further, missingness is a known issue with N3C’s vaccination data, as patients may receive vaccine doses at pop-up clinics, drugstores, or at their place of employment, which may not end up recorded in the patient’s records.…”

Clinical algorithms, racism, and “fairness” in healthcare: A case of bounded justice

“…Race and ethnicity data are collected from patients by healthcare workers, using these standard categories as a guide (Cruz and Smith, 2021). Once stored within the EHR, this data may be exchanged and aggregated with data from other health systems (Cook et al, 2022). When this data is extracted for analysis, missing data points may be imputed, new categories may be created, and others may be dropped entirely.…”

DEIA is essential to advance the goals of translational science: Perspectives from NCATS

“…A recent analysis in N3C reported race and ethnicity biases and how these affect the quality of the harmonized data within N3C. 29 Notably, the analysis emphasizes the importance of making transparent the process of data harmonization to enable informed decisions on generating race and ethnicity phenotypes. We note that that this transparency is built into the design of N3C and is accessible to users of the system.…”

“…The N3C platform enables critical insights into the relative burden of COVID-19 infections, hospitalizations, mortality by race/ethnicity and gender, and other social determinants of health factors. A recent analysis of N3C reported race and ethnicity biases and how these affect the quality of the harmonized data within N3C [29]. Notably, the analysis emphasizes the importance of making transparent the process of data harmonization to enable informed decisions on generating race and ethnicity phenotypes.…”