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

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.

The quality of social determinants data in the electronic health record: a systematic review.

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.

Prostate Cancer Screening Patient Decision Aids: A Systematic Review and Meta-analysis.

CONTEXT: Although screening recommendations for prostate cancer using prostate-specific antigen testing often include shared decision making, the effect of patient decision aids on patients' intention and uptake is unclear. This study aimed to review the effect of decision aids on men's screening intention, screening utilization, and the congruence between intentions and uptake. EVIDENCE ACQUISITION: Data sources were searched through April 6, 2018, and included MEDLINE, Scopus, CENTRAL, CT.gov, Cochrane report, PsycARTICLES, PsycINFO, and reference lists. This study included RCTs and observational studies of decision aids that measured prostate screening intention or behavior. The analysis was completed in April 2018. EVIDENCE SYNTHESIS: Eighteen studies (13 RCTs, four before-after studies, and one non-RCT) reported data on screening intention for ≅8,400 men and screening uptake for 2,385 men. Compared with usual care, the use of decision aids in any format results in fewer men (aged ≥40 years) planning to undergo prostate-specific antigen testing (risk ratio=0.88, 95% CI=0.81, 0.95, p=0.006, I2=66%, p<0.001, n=8). Many men did not follow their screening intentions during the first year after using a decision aid; however, most men who were planning to undergo screening did so (probability that men who wanted to be screened would receive screening was 95%). CONCLUSIONS: Integration of decision aids in clinical practice may result in a decrease in the number of men who elect prostate-specific antigen testing, which may in turn reduce screening uptake. To ensure high congruence between intention and screening utilization, providers should not delay the shared decision-making discussion after patients use a decision aid.