Enhancing Clinical Content and Race/Ethnicity Data in Statewide Hospital Administrative Databases: Obstacles Encountered, Strategies Adopted, and Lessons Learned

There is increasing demand for comprehensive databases for understanding population health and assessing the benefits and harms of alternative health care interventions (Marko and Weil 2010; Shah, Drozda, and Peterson 2010). By enhancing hospitals’ administrative data with carefully selected clinical, demographic, and resource utilization data elements, existing datasets can provide invaluable information for health care stakeholders and decision makers (Concato et al. 2010; Fleurence, Naci, and Jansen 2010). Prior research has demonstrated the value of enhancing administrative claims databases with additional elements including numerical laboratory values, other test results, and present-on-admission (POA) modifiers. Specifically, the incorporation of POA modifiers of ICD-9-CM diagnosis codes into claims databases has improved the validity of risk-adjustment models (Pine et al. 2007) and resulted in improved quality measurement (Dalton et al. 2013). Similarly, previous studies have shown improvements in predictions of inpatient mortality and complications as a result of linking claims data to hospital numerical laboratory data (Jordan et al. 2007; Pine et al. 2007). Improvements in the processes and protocols to collect patient race/ethnicity/primary language (R/E/L) information are needed to address disparities in health care and health outcomes attributable to differences in race, ethnicity, and primary language, which are well documented and persistent even after adjusting for differences in related characteristics such as education, income, insurance, access to care, and health status. It has been estimated that racial and ethnic disparities in health and health care cost the United States $1.24 trillion between 2003 and 2006: over $200 billion for direct medical expenses, and another $1 trillion for indirect costs such as lost quality of life years and lost productivity (LaVeist, Gaskin, and Richard 2011). In recent years, there has been increased focus on eliminating disparities in access to care and health outcomes for racially, ethnically, and linguistically diverse populations in the United States. In 2010, in an effort to build on prior work initiated by state data organizations and ultimately create comprehensive, validated, and sustainable data infrastructures for comparative effectiveness research (CER), AHRQ, under the American Recovery and Reinvestment Act, funded eight 3-year infrastructure development research grants. Grantees from Florida, Hawaii, Minnesota, New Jersey, and New York collaborated with state and health care provider organizations to enhance existing hospital claims and discharge databases with clinical information and to demonstrate the utility of the new data infrastructure by using the new databases in a CER study. Enhancements included linking existing hospital administrative data to hospital laboratory data; hospital inpatient, ambulatory, and emergency room claims and discharge data; birth and death certificate data; inpatient pharmacy order data; and preadmission emergency medical services data. Grantees from California, New Mexico, and the Improving Data and Enhancing Access-Northwest (IDEA-NW) project—which included partners from Idaho, Oregon, and Washington—improved the quality of their race and ethnicity data so that their enhanced databases would improve analyses of disparities in health care services and resulting clinical outcomes. All eight grantees recognized a deficiency in the availability and use of robust, longitudinal data systems to support CER and disparities research in their states (Salihu, Salinas, and Mogos 2013; Salinas-Miranda et al. 2013) and disciplines, and felt that enhancement of their databases with detailed, individual-level sociodemographic, health, and geospatial information would be valuable in improving epidemiological, clinical, and health services research. While widespread adoption of these enhancements would likely increase the value of both state data and nationally integrated data for tracking population health, monitoring risk-adjusted clinical performance, and studying the comparative effectiveness of various health care interventions, a variety of administrative, technical, and logistical problems confronted grantees. Hurdles faced by all grantees included obtaining approval for use and linkage of new databases, and garnering active participation from key stakeholders (e.g., hospitals and state health departments). Clinical data grantees also struggled with designing effective protocols for linking diverse data elements with minimal identifying information from disparate sources. Grantees working to improve race, ethnicity, and tribal data faced unique challenges in ensuring the data collected would conform to Office of Management and Budget standards. This paper details obstacles encountered, lessons learned, and both successful and unsuccessful strategies adopted by grantees as they aimed to improve the clinical content and race/ethnicity/language data in their states’ hospital-based, encounter-level databases.