To identify the next steps toward improving data collection, it is helpful to understand these opportunities and challenges in the context of current practices.
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While a range of health and health care entities collect data, the data do not flow among these entities in a cohesive or standardized way.
Entities within the health care system face challenges when collecting race, ethnicity, and language data from patients, enrollees, members, and respondents.
Explicitly expressing the rationale for the data collection and training staff, organizational leadership, and the public to appreciate the need to use valid collection mechanisms may improve the situation.
Nevertheless, some entities face health information technology (Health IT) constraints and internal resistance.
Indirect estimation techniques, when used with an understanding of the probabilistic nature of the data, can supplement direct data collection efforts Addressing health and health care disparities requires the full involvement of organizations that have an existing infrastructure for quality measurement and improvement.
Although hospitals, community health centers (CHCs), physician practices, health plans, and local, state, and federal agencies can all play key roles by incorporating race, ethnicity, and language data into existing data collection and quality reporting efforts, each faces opportunities and challenges in attempting to achieve this objective.
Next is a discussion of steps that can be taken to address these issues and improve data collection processes.