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To identify the next steps toward improving data collection, it is helpful to understand these opportunities and challenges in the context of current practices.In some instances, the opportunities and challenges are unique to each type of organization; in others, they are common to all organizations and include: Previous chapters have provided a framework for eliciting, categorizing, and coding data on race, ethnicity, and language need.With the passage of the Civil Rights Act of 1964 there was a legislative mandate for equal access to and desegregation of hospitals (Reynolds, 1997).Therefore it is not surprising that more than 89 percent of hospitals report collecting race and ethnicity data, and 79 percent report collecting data on primary language (AHA, 2008).Health care involves a diverse set of public and private data collection systems, including health surveys, administrative enrollment and billing records, and medical records, used by various entities, including hospitals, CHCs, physicians, and health plans.Data on race, ethnicity, and language are collected, to some extent, by all these entities, suggesting the potential of each to contribute information on patients or enrollees.

Nevertheless, some entities face health information technology (Health IT) constraints and internal resistance.Thus, while hospitals are an important component of the health care system and represent a major percentage of health care expenditures, they are only one element of the system for collecting and reporting race, ethnicity, and language data.Hospitals also face challenges associated with collecting accurate data and using these data for quality improvement and reduction of disparities.This culture of data collection has limitations, however.Historically, the data were never intended for quality improvement purposes, but to allow analysis to ensure compliance with civil rights provisions.

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