The following is a summary of “Accuracy of race & ethnicity data in US based healthcare databases: A systematic review,” published in the OCTOBER 2023 issue of Surgery by Johnson, et al.

The quality and availability of data on a patient’s race and ethnicity vary across different healthcare databases. The discrepancies in data quality can have a detrimental impact on efforts to study health disparities.

A systematic review was conducted to compile and organize information on the accuracy of race and ethnicity data, stratified by database type and specific race and ethnicity categories.

The review incorporated 43 studies. Disease registries consistently exhibited high levels of data completeness and accuracy. In contrast, electronic health records (EHRs) frequently display incomplete and/or inaccurate patient race and ethnicity data. Databases generally maintained high levels of accurate data for White and Black patients but had relatively high rates of misclassification and incomplete data for Hispanic/Latinx patients. The most significant misclassification was observed for Asians, Pacific Islanders, and American Indian/Alaska Native (AI/AN) populations. Interventions focused on improving self-reported data within healthcare systems were shown to enhance data quality.

Data on race and ethnicity, collected to facilitate research and quality improvement, were the most reliable. However, data accuracy can vary based on a patient’s racial or ethnic background, emphasizing the need for improved collection standards in healthcare databases.

Source: americanjournalofsurgery.com/article/S0002-9610(23)00197-6/fulltext