Improving Hospital Reporting of Patient Race and Ethnicity-Approaches to Data Auditing

Racial and ethnic health disparities are well documented (Institute of Medicine 2003; Kim et al. 2011, 2012; Agency for Healthcare Research and Quality 2012). The Institute of Medicine recommends that to improve quality of care across racial/ethnic groups that valid and reliable data on race/ethnicity must be collected (Institute of Medicine 2009). Quality improvement efforts to reduce disparities in care across groups rely upon the existence of valid and reliable measurements of patient characteristics, including race and ethnicity (R/E). The National Quality Forum now recommends that future performance measures be stratified—or calculated separately—by sociodemographic factors, including income, race, and education (National Quality Forum 2014). The Affordable Care Act requires standardized collection of race/ethnicity across federal health care databases precisely for this reason (The Patient Protection and Affordable Care Act 2010). Hospital medical records data on patient sociodemographic characteristics serve as the foundation for identifying disparities in care and disease outcomes within and across medical systems. They are also the primary source of patient information for disease-based databases, such as cancer registries, which are the basis for identifying disparities in cancer occurrence and survival (Glaser et al. 2005). Despite their importance, hospital medical record data have proven to be problematic sources of demographic data. Several studies have shown that medical record data on R/E are subject to misclassification (Stewart et al. 1999; Kressin et al. 2003; Gomez and Glaser 2005; Gomez et al. 2005). Questions remain as to the consistency in collection of these data within and across hospitals (Stewart et al. 1999; Kressin et al. 2003; Gomez and Glaser 2005; Gomez et al. 2005). Efforts have been made to improve self-reported data, including periodic contact via postcard to elicit R/E (Arday et al. 2000) and introduction of the National Consumer Assessment of Health Plans (Morales et al. 2001). However, because the collection of valid and reliable self-reported data on R/E in health care continues to lag, analytic approaches to improve the accuracy of these measures have been attempted, including name-matching techniques to identify Hispanic and Asian-Pacific Islander enrollees (Morgan, Wei, and Virnig 2004; Wei et al. 2006; Eicheldinger and Bonito 2008) and Bayesian techniques (Elliott et al. 2008, 2009). Although great effort has been expended to develop processes to improve the validity and reliability of self-reported R/E in the health care setting, a major weakness has been a lack of metrics to track and feedback the accuracy and completeness of R/E data collected by hospitals. For example, in California, the Office of Statewide Health Planning and Development (OSHPD) examines hospital discharge data for completeness (low rates of “other” or “unknown” reported) and for internal consistency (patients admitted to the same hospital have the same R/E across encounters). Unfortunately, these checks are not equivalent to accuracy, which requires comparison to gold standard (self-reported) R/E information. These self-report patient-level data are harder to obtain. Nationwide, few, if any, organizations that manage the respective statewide hospital data are currently making this a part of their data improvement efforts. In order to improve data quality, we attempted to create a measure of overall accuracy of hospital reported R/E using the California inpatient data linked to the US Census, which could be used with all-payer hospital discharge datasets collected in California and other states. The hospital measure was validated through comparison to a gold standard–derived measure of accuracy. Finally, we attempted to use the metric to assess trends in data accuracy (new metric) and in data completeness (rate of missing/unknown) in California and six other demographically diverse states that submit data to the national Healthcare Cost and Utilization Project (HCUP). Our overarching goal was to create a validated measure that could be easily employed using existing data with the recognition that as more detailed information becomes available, more refined measures will create better estimates of hospital reporting.