Joining forces to reduce disparities: Lessons learned from the National Health Plan Collaborative

The Institute of Medicine's seminal report in 2002, Unequal Treatment, was an important catalyst for the health care delivery sector in beginning to identifying and addressing racial and ethnic health disparities.  Health insurance plans realized that that collection and utilization of data on race, ethnicity, and primary language of their enrollees are key steps in the implementation of targeted interventions for improving overall quality and reducing racial and ethnic disparities in health and health care.

However, fundamental barriers to the collection of such data exist. Unlike state-sponsored health plans, commercial plans, which cover the majority of insured in the United States, still have limited race/ethnicity data on their enrollees.  Enhancements and upgrades of the technological infrastructure and databases needed to house the data are expensive investments.  Redesign of work flows and data processes to collect the data systematically also require significant investment in human resources and training costs.  Even when health plans, such as Aetna, have full backing and substantial resources from its leadership to collect self-reported race/ethnicity data, the actual process can take years to complete, or reach a saturation point where a portion of the insured will always decline to provide such data.

Without the availability of race and ethnicity data on their insured populations, health plan are unable to design targeted and culturally sensitive quality improvement interventions that have the potential to generate a greater impact on the health of their members relative to the broader, “one-size-fits-all” interventions, and an impact on reducing disparities in care. 

As the largest health benefits company in terms of medical membership in the United States, WellPoint, Inc. is committed to reducing health disparities among its members.  To meet health plan quality improvement, operations and business planning needs, WellPoint applied the Rand Corporation’s preliminary work on indirect data methodologies and independently refined the process to estimate member race / ethnicity using regression analyses and a combination of name analyses and geocoded addresses with census data.  The resulting data estimates allow WellPoint's health plans to examine differences between racial / ethnic groups in various health indicators, such as diabetes, colorectal and mammography screening rates.  Along with the traditional graphs and charts, GIS tools have allowed further detailed examination of screening rates.  At WellPoint, indirect methodology and maps has been used to identify hotspots of unscreened members, study provider access for minority members, identify minority members for culturally/linguistically appropriate health screening reminders and health education materials, and determine member threshold language needs to meet regulatory requirements.