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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Summary The goal of eliminating disparities in health care in the United States remains elusive. The findings of the National Healthcare Disparities Report reveal that even as quality improves on specific measures, disparities often persist (AHRQ, 2008a, 2008b). Addressing these disparities must begin with the fundamental step of bringing the nature of the disparities and the groups at risk for those disparities to light by collecting health care quality information stratified by race, ethnicity and language data. Then attention can be focused on where interventions might be best applied, and on planning and evaluating those efforts to inform the development of policy and the application of resources. A lack of standardization of categories for race, ethnicity, and language data has been suggested as one obstacle to achieving more widespread collection and utilization of these data. Many types of entities participate in initiatives to improve the quality of health care; health plans, hospitals, other providers, and health systems can and should obtain race, ethnicity, and language data so these data can be used to identify gaps and improve care for all individuals. The purpose of this report is to identify standardized categories for the variables of race, ethnicity, and language that can be used to facilitate the sharing, compilation, and comparison of quality data stratified by the standard categories. The Institute of Medicine, under a contract with the Agency for Healthcare Research and Quality (AHRQ), Department of Health and Human Services (HHS), formed the Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement to identify current models for collecting and coding race, ethnicity, and language data; to ascertain the challenges involved in obtaining these data in health care settings; and to make recommendations for improvement. The language in the statement of task (Box S-1)—“in healthcare quality improvement” and “assess and report on quality of care”—led the subcommittee to focus its discussion and recommendations on data collection in the domain of health care services. EXISTING GUIDANCE ON RACE, ETHNICITY, AND LANGUAGE CATEGORIES The concepts of race and ethnicity are defined socially and culturally and, in the case of federal data collection, by legislative and political necessity (Hayes-Bautista and Chapa, 1987). With the aim of identifying important cultural and social groups for statistical reporting and civil rights monitoring, the Office of Management and Budget (OMB) has developed a minimum set of standardized categories for reporting on race and Hispanic ethnicity by federal agencies and recipients of federal funds (OMB, 1977, 1997b). The five race categories are now Black or African American, White, Asian, American Indian or Alaska Native, and Native Hawaiian or Other Pacific Islander.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement BOX S-1 Statement of Task: Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement A subcommittee of experts will report to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports regarding the lack of standardization of collection of race and ethnicity data at the federal, state, local, and private sector levels due to the fact that the federal government has yet to issue comprehensive, definitive guidelines for the collection and disclosure of race and ethnicity data in healthcare quality improvement. The subcommittee will focus on defining a standard set of race/ethnicity and language categories and methods for obtaining this information to serve as a standard for those entities wishing to assess and report on quality of care across these categories. The subcommittee will carry out an appropriate level of detailed, in-depth analysis and description which can be included in the overall report by the committee and as a separate stand alone report. OMB describes these categories as the minimum set and encourages the collection of more detailed data provided those data can be aggregated back to the minimum categories (OMB, 1997a). Progress has been made in incorporating these categories into the collection and presentation of data in health care settings. However, some health care–related data collection efforts still do not employ these basic standard categories. While OMB has not established a list of language categories, the collection of language data has been pivotal in determining whether there has been discrimination by “national origin” under Title VI of the Civil Rights Act of 1964,1,2 and federal policies state that “reasonable steps” need to be taken so that persons of limited English proficiency can have “meaningful access” to programs or activities without charge for language services.3 Additionally in 2000, HHS released its National Standards on Culturally and Linguistically Appropriate Services (CLAS), which encourage all health care organizations and individual providers “to make their practices more culturally and linguistically accessible,” including the use of race, ethnicity, and language data in program assessments and incorporation of these data into health records and organizational management systems (HHS, 2007). CATEGORIZING RACE AND ETHNICITY DATA The OMB race and Hispanic ethnicity categories represent broad population groups used for an array of statistical reporting and analytic purposes, including health care quality assessment and identification of disparities (AHRQ, 2008a; Cohen, 2008; Flores and Tomany-Korman, 2008; IOM, 2008; Kaiser Family Foundation, 2009). Chapter 2 illustrates that these categories alone, however, are insufficient to illuminate many disparities and to target quality improvement efforts where they may be most needed. Since disparities can exist within those broad OMB categories, there is value in collecting and utilizing data incorporating more fine-grained categories than those of OMB (Blendon et al., 2007; Jerant et al., 2008; Read et al., 2005; Shah and Carrasquillo, 2006). The subcommittee recommends a separate question to collect data on granular ethnicity—defined as “a person’s ethnic origin or descent, ‘roots,’ or heritage, or the place of birth of the person or the person’s parents or ancestors…” (U.S. Census Bureau, 2008)—in addition to soliciting data in the OMB race and Hispanic ethnicity categories (Figure S-1). Research also shows that not all individuals identify with the current OMB race categories so the subcommittee recommends expanding the race categories to six choices by including a “Some other race” option 1 The Civil Rights Act of 1964, Public Law 88-352, 78 Stat. 241, 88th Cong., 2nd sess. (July 2, 1964). 2 Lau v Nichols, 414 U.S. 563 (1974). 3 Improving Access to Services for Persons with Limited English Proficiency, Executive Order 13166, August 11, 2000.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement FIGURE S-1 Recommended variables for standardized collection of race, ethnicity, and language need. NOTE: Additional categories for HIT tracking might include whether respondents have not yet responded (unavailable), refuse to answer (declined), or do not know (unknown), as well as whether responses are self-reported or observer-reported. a The preferred order of questioning is Hispanic ethnicity first, followed by race, as OMB recommends, and then granular ethnicity. b The U.S. Census Bureau received OMB permission to add “Some other race” to the standard OMB categories in Census 2000 and subsequent Census collections. c Additional codes will be needed for categories added to the CDC/HL7 list. d Need is determined on the basis of two questions, with asking about proficiency first. Limited English proficiency is defined for health care purposes as speaking English less than very well. SOURCES: CDC, 2000; Office of Management and Budget, 1997b; Shin and Bruno, 2003; U.S. Census Bureau, 2002.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement to provide a response category for those Hispanics and others who do not relate to the current choices. Additionally, the subcommittee favors the collection and retention for analysis of specific multiple-race combinations (i.e., having data on each race that an individual selects), rather than losing that detail by only offering the more general category of “multiracial,” whenever possible. In Chapter 3, the subcommittee considers whether a national “OMB Plus” set of 10 to 15 granular ethnicity categories, similar to the Census Bureau approach, should be identified that would be optimal for collection by all health care entities. However, such a set would not be specific to and appropriate for the diverse communities in which health care entities operate. Instead, the subcommittee concludes that individual entities should select the granular ethnicity categories representative of their service population selected from a national list of standardized categories. Whenever a limited list of categories is offered to respondents, the list should include an open-ended response option of “Other, please specify:__” so that each individual who desires to do so can self-identify. Recommendation 3-1: An entity collecting data from individuals for purposes related to health and health care should: Collect data on granular ethnicity using categories that are applicable to the populations it serves or studies. Categories should be selected from a national standard list (see Recommendation 6-1a) on the basis of health and health care quality issues, evidence or likelihood of disparities, or size of subgroups within the population. The selection of categories should also be informed by analysis of relevant data (e.g., Census data) on the service or study population. In addition, an open-ended option of “Other, please specify:__” should be provided for persons whose granular ethnicity is not listed as a response option. Elicit categorical responses consistent with the current OMB standard race and Hispanic ethnicity categories, with the addition of a response option of “Some other race” for persons who do not identify with the OMB race categories. While several organizations provide lists of granular ethnicities (e.g., Centers for Disease Control and Prevention [CDC]/Health Level 7 [HL7] and the Commonwealth of Massachusetts/Brookings Institution), none of these lists is sufficient for a standard national set from which locally relevant choices could be made (CDC, 2000; Taylor-Clark et al., 2009). A merged list provides a template from which such a national standard set can be developed (see Appendix E). When a person does not check off an OMB race or Hispanic ethnicity and provides only a granular ethnicity response, a process for rolling granular ethnicity categories up to the OMB categories will, in some cases, be necessary for analysis and reporting purposes. However, some ethnicities do not correspond to a single OMB race category, necessitating a “no determinate OMB race classification” for analytic purposes (see Appendix F). Recommendation 3-2: Any entity collecting data from individuals for purposes related to health and health care should collect granular ethnicity data in addition to data in the OMB race and Hispanic ethnicity categories and should select the granular ethnicity categories to be used from a national standard set. When respondents do not self-identify as one of the OMB race categories or do not respond to the Hispanic ethnicity question, a national scheme should be used to roll up the granular ethnicity categories to the applicable broad OMB race and Hispanic ethnicity categories to the extent feasible. Eliciting accurate and reliable race, Hispanic ethnicity, and granular ethnicity data depends on the ways in which the questions are asked, the instructions provided to respondents (e.g., “Select one or more”), and the format of the questions (i.e., OMB one-question versus two-question format). This latter issue is especially relevant to how Hispanic populations self-identify. Pilot projects and further study are necessary to confirm the best ways to collect accurate data that are useful for health care quality improvement.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Recommendation 3-3: To determine the utility for health and health care purposes, HHS should pursue studies on different ways of framing the questions and related response categories for collecting race and ethnicity data at the level of the OMB categories, focusing on completeness and accuracy of response among all groups. Issues addressed should include use of the one- or two-question format for race and Hispanic ethnicity, whether all individuals understand and identify with the OMB race and Hispanic ethnicity categories, and the increasing size of populations identifying with “Some other race.” The results of such studies, together with parallel studies by the Census Bureau and other agencies, may reveal the need for an OMB review across all agencies to determine the best format for improving response among all groups. IMPROVING THE COLLECTION OF DATA ON LANGUAGE Compelling evidence exists that having limited English proficiency (LEP) affects the delivery and quality of health care and can result in significant disparities in access to care (Hu and Covell, 1986; Weinick and Krauss, 2000), a decreased likelihood of having a usual source of care (Kirkman-Liff and Mondragon, 1991; Weinick and Krauss, 2000), an increased probability of receiving unnecessary diagnostic tests (Hampers et al., 1999), more serious adverse outcomes from medical errors (Divi et al., 2007), and more drug-related complications (Gandhi et al., 2000). To achieve safe, effective, patient-centered communication, attention must be paid to the language needs of patients, as addressed in Chapter 4. Language Questions Assessing each individual’s language need is an essential first step toward ensuring effective health care communication. The subcommittee concludes that spoken language need can best be assessed by asking two questions: one aimed at determining whether an individual speaks English less than very well and a second aimed at identifying the individual’s preferred spoken language during a health care encounter (Figure S-1). Having this information for each individual allows its use to ensure the quality of services in subsequent encounters, in analysis of health care disparities, and in system-level planning (e.g., determining the need for interpreters and matching patients to language-concordant providers). The subcommittee establishes a hierarchy among the possible language questions, with questions about English proficiency and preferred spoken language identified as a higher priority than questions on language spoken at home or on preferred language for written materials. On average, 55 percent of those who speak another language at home speak English very well (Shin and Bruno, 2003), but asking about language spoken at home helps provide a window into the health beliefs and practices of the home environment. The correlation between those who need spoken and written language assistance appears to be high in many settings. Recommendation 4-1: To assess patient/consumer language and communication needs, all entities collecting data from individuals for purposes related to health and health care should: At a minimum, collect data on an individual’s assessment of his/her level of English proficiency and on the preferred spoken language needed for effective communication with health care providers. For health care purposes, a rating of spoken English-language proficiency of less than very well is considered limited English proficiency. Where possible and applicable, additionally collect data on the language spoken by the individual at home and the language in which he/she prefers to receive written materials. When the individual is a child, the language need of the parent/guardian must be determined. Similarly, if an adult has a guardian/conservator, that individual’s language need must be assessed.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Languages in Use More than 600 languages are in use in the United States although a smaller number may be in use in health care contexts. In Chapter 4 the subcommittee evaluates options for determining what language categories entities should use for data collection (e.g., a uniform set for all entities, percentage or numerical thresholds based on the presence of languages in a service area, or local choice). Local choice informed by data on the languages spoken most frequently in the service area by persons with LEP is the preferred option. A single list does not suit all areas given that the top non-English languages vary greatly from area to area (for instance, Spanish is in the top 10 languages in 3,122 of 3,141 counties in the United States, while Turkish is in the top 10 in 12 counties, Laotian in 125, Navaho in 74, SerboCroatian in 58, and Portuguese in 229) (U.S. English Foundation, 2009). The aim is to have data on each individual’s specific language need, but when an entity designs its collection instruments, whether paper or electronic, it may, because of space considerations, have to use a limited number of response categories. Therefore, such a response list should always include an “Other, please specify:__” option. Some electronic data collection systems are more sophisticated, and by using keystroke recognition can accommodate hundreds of languages. Recommendation 4-2: The choice of response categories for spoken and written language questions should be informed by analysis of relevant data on the service area (e.g., Census data) or service population, and any response list should include an option of “Other, please specify:__” for persons whose language is not listed. The subcommittee has developed a template of languages used in the United States based on Census data and the experiences of certain health care providers. This template can serve as a basis for the national standard set called for in recommendations in Chapter 6 (see Appendix I for template). A uniform set of codes can facilitate sharing of data. Two possible language coding systems already exist (the Census and International Organization for Standardization [ISO] code sets) (SIL International, 2009; U.S. Census Bureau, 2007). Recommendation 4-3: When any health care entity collects language data, the languages used as response options or categories for analysis should be selected from a national standard set of languages in use in the United States. The national standard set should include sign language(s) for spoken language and Braille for written language. IMPROVING DATA COLLECTION ACROSS THE HEALTH CARE SYSTEM As discussed in Chapter 5, while each of the entities involved in the nation’s health care system has some capability for the collection of race, ethnicity, and language data, some are better positioned than others to collect these data through self-report, the generally agreed-upon best way to define a person’s racial and ethnic identity. In the future, information infrastructure may enable integrated data exchange so that all entities will not need to collect all data. For now, however, all health and health care entities have roles to play in collecting these data directly from individuals. Hospitals, community health centers, physician practices, health plans, and local, state, and federal agencies can all identify next steps toward improving or implementing direct data collection by understanding the unique contexts in which they operate. Across all these entities, these data must be collected and stored responsibly. Training of staff, upgrades to health information technology (HIT) systems, and communication with patients and enrollees are potential avenues for improved data collection and building of trust. In the subcommittee’s proposed framework, optional categories are offered (e.g., declined, unavailable, unknown, self-reported, observer-reported); these are not for patient response, but for tracking the portion of the patient population for which an entity has been able to collect data or the nature of the data collection. Until directly collected data are sufficient for analytic and quality improvement purposes, indirect estimation of race and ethnicity through techniques such as geocoding and surname analysis is useful for bridging data gaps.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Recommendation 5-1: Where directly collected race and ethnicity data are not available, entities should use indirect estimation to aid in the analysis of racial and ethnic disparities and in the development of targeted quality improvement strategies, recognizing the probabilistic and fallible nature of such indirectly estimated identifications. Race and ethnicity identifications based on indirect estimation should be distinguished from self-reports in data systems, and if feasible, should be accompanied by probabilities. Interventions and communications in which race and ethnicity identifications are based on indirect estimation may be better suited to population-level interventions and communications and less well suited to use in individual-level interactions. An indirectly estimated probability of an individual’s race and ethnicity should never be placed in a medical record or used in clinical decision making. Analyses using indirectly estimated race and ethnicity should employ statistically valid methods that deal with probabilistic identifications. IMPLEMENTING COLLECTION OF STANDARDIZED DATA Now is an opportune time for action on standardization of the categories used to collect race, ethnicity, and language data. Efforts to share and evaluate quality data across states, regions, or payers would be facilitated by standardized categories. HHS is a prime locus of the subcommittee’s recommendations in Chapter 6 for implementation of improved collection of standardized data because of its focus on resolving health and health care disparities and its history of promoting the collection of race, ethnicity, and language data to ensure compliance with applicable statutes and regulations. National development of standardized categories and coding by HHS, along with a responsive updating process, would relieve each state and entity of having to develop its own set of categories and coding scheme, which could be incompatible with others. The subcommittee templates of categories along with an updated CDC/HL7 Code Set can form the basis for standardized race, Hispanic ethnicity, and granular ethnicity data while a determination will have to be made on coding for languages. Recommendation 6-1a: HHS should develop and make available national standard lists of granular ethnicity categories and spoken and written languages, with accompanying unique codes and rules for rollup procedures. HHS should adopt a process for routine updating of those lists and procedures as necessary. Sign languages should be included in national lists of spoken languages and Braille in lists of written languages. HHS should ensure that any national hierarchy used to roll up granular ethnicity categories to the broad OMB race and Hispanic ethnicity categories takes into account responses that do not correspond to one of the OMB categories. Standardization would support achievement of the goal set forth in the American Recovery and Reinvestment Act of 20094 (ARRA) of having a national electronic health record (EHR)5 for each individual by 2014 that incorporates collection of data on the person’s race, ethnicity, and primary language. Having the standards adopted by the other components of the health care industry, including the makers of HIT systems, would help ensure that a sufficient set of data fields are available to accommodate each element recommended for collection by the subcommittee. 4 American Recovery and Reinvestment Act of 2009, Public Law 111-5 § 3002(b)(2)(B)(vii), 111th Cong., 1st sess. (February 17, 2009). 5 In this document, EHR means a patient record owned and maintained by a provider entity; a personal health record is a medical or health record owned and maintained by a patient him- or herself.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Recommendation 6-1b: HHS and the Office of the National Coordinator for Health Information Technology (ONC) should adopt as standards for including in electronic health records the variables of race, Hispanic ethnicity, granular ethnicity, and language need identified in this report. Recommendation 6-1c: HHS and ONC should develop standards for electronic data transmission among health care providers and plans that support data exchange and possible aggregation of race, Hispanic ethnicity, granular ethnicity, and language need data across entities to minimize redundancy in data collection. Performance incentive programs tend not to be designed with reduction of disparities in mind, yet can have positive or negative effects on disparities in health care and on underresourced primary care safety net providers (Chien et al., 2007; Rust and Cooper, 2007; Williams, 2009). The subcommittee does not take a stand on whether incentive payments in HIT programs should exist, but when they do exist, the collection of race, ethnicity, and language data would be one activity for which positive incentives should be offered. Recommendation 6-1d: The Centers for Medicare and Medicaid Services (CMS), as well as others sponsoring payment incentive programs, should ensure that the awarding of such incentives takes into account collection of the recommended data on race, Hispanic ethnicity, granular ethnicity, and language need so these data can be used to identify and address disparities in care. Numerous past and present legislative and policy efforts stress the importance of collecting race, ethnicity, and language data in federal programs. HHS administers programs supporting the health care delivery system to provide care to persons at risk of receiving suboptimal care, and these programs present opportunities to influence the quality of care delivered to millions of Americans. Because the subcommittee’s charge relates to health care, the following recommendation focuses on the HHS programs that deliver health cares services, pay for those services through insurance mechanisms, or administer surveys that increase knowledge on health care needs and outcomes. The Secretary, however, may find it useful to extend the standardized approach of this report to other HHS health-related programs or other data gathering activities. Recommendation 6-1e: HHS should issue guidance that recipients of HHS funding (e.g., Medicare, the Children’s Health Insurance Program [CHIP], Medicaid, community health centers) include data on race, Hispanic ethnicity, granular ethnicity, and language need in individual health records so these data can be used to stratify quality performance metrics, organize quality improvement and disparity reduction initiatives, and report on progress. Having quality-of-care information from large federal delivery systems such as the Department of Veterans Affairs, the Department of Defense, and other federally funded programs, such as community health centers, stratified by the same variables and categories recommended in this report would provide rich sources for comparative analysis. Recommendation 6-2: HHS, the Department of Veterans Affairs, and the Department of Defense should coordinate their efforts to ensure that all federally funded health care delivery systems collect the variables of race, Hispanic ethnicity, granular ethnicity, and language need as outlined in this report, and include these data in the health records of individuals for use in stratifying quality performance metrics, organizing quality improvement and disparity reduction initiatives, and reporting on progress. Accreditation organizations and other professional and standards-setting bodies can play a key role in fostering the collection of race, ethnicity, and language data. Hospitals, health plans, and physicians have reported that a lack of standardization has been a barrier to using these data in quality improvement efforts (Bilheimer and Sisk,
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement 2008; Lurie et al., 2008; NCQA, 2009; Siegel et al., 2008). The Joint Commission, the National Committee for Quality Assurance (NCQA), and URAC6 have developed CLAS-like standards for their organizational reviews. The National Quality Forum (NQF) encourages the collection of race, ethnicity, and language data in accordance with the Health Research & Educational Trust (HRET) Toolkit (NQF, 2008); the subcommittee’s recommendations include modifications to that toolkit. The American Medical Association, the National Medical Association, and the National Hispanic Medical Association’s Commission to End Health Care Disparities have reaffirmed their collective commitment to bringing an end to health care disparities by increasing awareness in the physician community and promoting better data collection (AMA, 2005, 2009). Recommendation 6-3: Accreditation and standards-setting organizations should incorporate the variables of race, Hispanic ethnicity, granular ethnicity, and language need outlined in this report and associated categories (as updated by HHS) as part of their accreditation standards and performance measure endorsements. The Joint Commission, NCQA, and URAC should ensure collection in individual health records of the variables of race, Hispanic ethnicity, granular ethnicity, and language need as outlined in this report so these data can be used to stratify quality performance metrics, organize quality improvement and disparity reduction initiatives, and report on progress. NQF should review and amend its recommendations on the collection and use of data on race, Hispanic ethnicity, granular ethnicity, and language need to accord with the categories and procedures outlined in this report. Medical societies and medical boards should review and endorse the variables, categories, and procedures outlined in this report and educate their members on their use for quality improvement. States have an opportunity to shape the level of detail of race, ethnicity, and language data collected in their programs whether for use in reporting on quality measures by insurance programs, in disease registries, in hospital discharges, in health care surveys, in patient safety reporting, or in other activities. Through Medicaid and CHIP programs, states have leverage with managed care organizations and providers to require collection of the recommended data and their use in quality improvement. Medicaid provides coverage for a large portion of minority groups, and states have an interest in ensuring that the population covered is receiving appropriate quality care (Angeles and Somers, 2007). Recommendation 6-4: Through their certification, regulation, and monitoring of health care providers and organizations within their jurisdiction, states should require the collection of data on the race, Hispanic ethnicity, granular ethnicity, and language need variables as outlined in this report so these data can be used to stratify quality performance metrics, organize quality improvement and disparity reduction initiatives, and report on progress. CONCLUSION Efforts are under way to establish national standards for health care technology, performance measurement, and data aggregation and exchange that complement local data collection and experiences with performance improvement and reporting (Roski, 2009). To date, it has been difficult to either combine or compare performance data stratified by race, ethnicity, or language need across payment and delivery systems, which has limited the utility of such data for assessing the performance of the health system as a whole or in specific geographic regions with respect to disparities. Yet, these analyses have implications for the design of appropriate interventions by federal, state, and local policy makers and health care plans and providers. 6 Formerly known as the Utilization Review Accreditation Commission.
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Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement Standardization of the categories used to collect these data would promote greater comparability of patient-focused data collected directly by care providers or health plans, or, for instance, transferred from providers to multiple plans. Standardization would also eliminate the need for all health care entities to develop their own categorization schemes. Still, additional resources and leadership at the local, state, and national levels will be required to implement these recommendations. Although broad application of EHRs will take a number of years, the data collection issues for current systems do not differ significantly from those involved in future EHR applications, so providers could institute today the processes for the capture and sharing of race, ethnicity, and language data proposed in this report. There is strong evidence that the quality of health care varies by race, ethnicity, and language. Quality metrics stratified by race, Hispanic ethnicity, granular ethnicity and language need can inform point-of-care services, application of resources, and decisions in patient–provider interactions in ways that can assist in improving overall quality and reducing disparities. REFERENCES AHRQ (Agency for Healthcare Research and Quality). 2008a. National Healthcare Disparities Report. Rockville, MD: AHRQ. ———. 2008b. The National Healthcare Quality Report. Rockville, MD: AHRQ. AMA (American Medical Association). 2005. Opinion 9.121 - racial and ethnic health care disparities. http://www.ama-assn.org/ama/pub/physician-resources/medical-ethics/code-medical-ethics/opinion9121.shtml (accessed May 22 2009). ———. 2009. Commission to End Health Care Disparities, five year summary. Chicago, IL: American Medical Association. Angeles, J., and S. A. Somers. 2007. From policy to action: Addressing racial and ethnic disparities at the ground-level. Hamilton, NJ: Center for Health Care Strategies, Inc. Bilheimer, L. T., and J. E. Sisk. 2008. Collecting adequate data on racial and ethnic disparities in health: The challenges continue. Health Affairs 27:383-391. Blendon, R. J., T. Buhr, E. F. Cassidy, D. J. Perez, K. A. Hunt, C. Fleischfresser, J. M. Benson, and M. J. Herrmann. 2007. Disparities in health: Perspectives of a multi-ethnic, multi-racial America. Health Affairs 26(5):1437-1447. CDC (Centers for Disease Control and Prevention). 2000. Race and ethnicity Code set version 1.0. Atlanta, GA: Centers for Disease Control and Prevention. Chien, A. T., M. H. Chin, A. M. Davis, and L. P. Casalino. 2007. Pay for performance, public reporting, and racial disparities in health care: How are programs being designed? Medical Care Research and Review 64(5 Suppl):283S-304S. Cohen, L. L. 2008. Racial/ethnic disparities in hospice care: A systematic review. Journal of Palliative Medicine 11(5):763-768. Divi, C., R. G. Koss, S. P. Schmaltz, and J. M. Loeb. 2007. Language proficiency and adverse events in US hospitals: A pilot study. International Journal for Quality in Health Care 19(2):60-67. Flores, G., and S. C. Tomany-Korman. 2008. Racial and ethnic disparities in medical and dental health, access to care, and use of services in US children. Pediatrics 121(2):e286-e298. Gandhi, T. K., H. R. Burstin, E. F. Cook, A. L. Puopolo, J. S. Haas, T. A. Brennan, and D. W. Bates. 2000. Drug complications in outpatients. Journal of General Internal Medicine 15(3):149-154. Hampers, L. C., S. Cha, D. J. Gutglass, H. J. Binns, and S. E. Krug. 1999. Language barriers and resource utilization in a pediatric emergency department. Pediatrics 103(6 Pt 1):1253-1256. Hayes-Bautista, D. E., and J. Chapa. 1987. Latino terminology: Conceptual bases for standardized terminology. American Journal of Public Health 77:61-68. HHS (U.S. Department of Health and Human Services), Office of Minority Health (OMH). 2007. National standards on culturally and linguistically appropriate services (CLAS). http://www.omhrc.gov/templates/browse.aspx?lvl=2&lvlID=15 (accessed May 13, 2009). Hu, D. J., and R. M. Covell. 1986. Health care usage by Hispanic outpatients as function of primary language. Western Journal of Medicine 144(4):490-493. IOM (Institute of Medicine). 2008. Challenges and successes in reducing health disparities: Workshop summary. Washington, DC: The National Academies Press. Jerant, A., R. Arellanes, and P. Franks. 2008. Health status among US Hispanics: Ethnic variation, nativity, and language moderation. Medical Care 46(7):709-717. Kaiser Family Foundation. 2009. Putting women’s health care disparities on the map: Examining racial and ethnic disparities at the state level. Menlo Park, CA: The Henry J. Kaiser Family Foundation. Kirkman-Liff, B., and D. 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