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IOM Panel Calls for More Precise Definitions on Ethnicity, Language

Janice Simmons, for HealthLeaders Media, September 2, 2009

Hospitals, health plans, and physician practices could use stronger data that emphasizes quality metrics broken down by race, Hispanic ethnicity, "fine-grained" ethnicity, and language proficiency to help address existing disparities in healthcare—while at the same time monitoring improvements, according to findings by an Institute of Medicine (IOM) subcommittee.

Currently, efforts are under way to establish national standards for healthcare technology and performance measurements that reflect local data collection and reporting. However, it has been difficult to either combine or compare performance data stratified by race, ethnicity, or language need across payment and delivery systems, said the Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement in a new report.

In early 2009, IOM—at the request of the Agency for Healthcare Research and Quality—formed the subcommittee to examine approaches to standardization.

In its report, the subcommittee calls for the Department of Health and Human Services (HHS) to develop and make available nationally standardized lists for fined-grained or "granular" ethnicity, which is defined as locally relevant choices from a national standard list of approximately 540 ethnic categories. It also calls for making distinctions among spoken and written languages—providing a rating of spoken English language proficiency and determining one's preferred language for health related encounters.

In addition, HHS and the Office of the National Coordinator for Health Information Technology should consider adopting categories of race, Hispanic ethnicity, granular ethnicity, and language for inclusion in electronic health record standardizations, the subcommittee suggested.

Inclusion of standardized ethnicity and language data in electronic health record systems will make it possible to stratify quality performance metrics, combine data from various sources, and make comparisons across settings and payment mechanisms, the report said.

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