Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement

Study Charge and Approach

The IOM, under a contract with the Agency for Healthcare Research and Quality (AHRQ), formed the Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement to report on the issue of standardization of race, ethnicity, and language variables; define a standard set of race, ethnicity, and language categories; and define methods of obtaining race, ethnicity, and language data (Box 1-3). To address this charge, the subcommittee identifies categories and types of questions that allow for the development of uniform standards for the collection, aggregation, and reporting of race, ethnicity, and language data for quality improvement in health care settings.

The subcommittee's title and its charge refer specifically to health care but not health in general. The subcommittee recognizes that health care is one element that contributes to people's health, and that the effects of race, ethnicity, and language on health in general are important. However, the language in the statement of task, specifically "in healthcare quality improvement" and "report on quality of care," led the subcommittee to focus its discussion and recommendations on the health care domain. In its recommendations regarding the collection of race, ethnicity, and language data, the subcommittee emphasizes areas such as care delivery sites (e.g., hospitals, physician practices) and public and private insurers involved in measuring and improving the quality of health care. Nonetheless, recommendations can apply to data collection activities in public health (e.g., state-sponsored immunization registries) when those data can be used to target interventions and resources to ensure equity in care and health outcomes. The subcommittee's recommendations include surveys addressing the quality of care or the utilization of care.

Vital statistics data sets present a special case, since data from birth or death certificates may be linked to data from health care settings to identify disparities in health care and health outcomes. Knowledge about differentials in mortality along race and ethnicity lines can help care providers focus inquiries about specific populations to determine the quality of their care. However, these data collection activities are organized and supported for purposes beyond health care and health care quality improvement, and recommendations set in the narrower context of health care quality improvement may conflict with other important considerations. The subcommittee did not focus its discussions on vital statistics data collection processes, nor do its recommendations specifically include those processes. New national standards have been set for birth and death records, incorporating categories beyond those set by the Office of Management and Budget (OMB); states and localities are free to use additional categories and are encouraged to do so along the lines of the subcommittee's recommendations.

The subcommittee was formed in conjunction with the Committee on Future Directions for the National Healthcare Quality and Disparities Reports. The subcommittee met in person four times during the course of the four-month study and conducted additional deliberations through telephone conferences. It heard public testimony from a wide range of experts during two public workshops and additional interviews. Staff and committee members met with and received information from a variety of stakeholders and interested organizations, including health plans, advocacy groups, health services researchers, and Health IT implementation experts.

The subcommittee has approached its task by evaluating the two interrelated purposes and uses of data collection (Figure 1-3): improvements in individual patient—provider care interactions, and system-level improvement. In patient—provider interactions, effective two-directional communication is essential to the provision of high-quality, patient-centered care. Quality care can depend on a provider's identification and understanding of the cultural beliefs and experiences of his or her patients, and on the expression and understanding of health care needs communicated by patients. Health services researchers have adopted the term cultural competence to describe the goal of creating a health care system and workforce that are capable of delivering high-quality care to all patients through an array of efforts, including training of physicians and availability of health care interpreters (Betancourt et al., 2005). Knowledge of a patient's race, ethnicity, and language and communication needs can assist in the provision of patient-centered care by accounting for the "impact of emotional, cultural, social, and psychological issues on the main biomedical ailment" (Hedrick, 1999, p. 154). At the system level, race, ethnicity, and language data serve an evidentiary purpose for improving population health, health care quality, and equity by identifying variations related to these characteristics. System-level analyses include variations across a broad range of health care entities, including physician practices, community health centers, hospitals, health plans, state government bodies, and federal agencies.

The subcommittee approached its task by defining two terms in its framework for recommendations; the term variable refers to the dimensions of race, ethnicity, and language on which is it important to have data; the term categories refers to the possible discrete groupings of individuals that can occur in any variable. The subcommittee developed principles to guide its deliberations, including the need for:

• Nomenclature for each variable and its categories that would maximize individuals' ease and consistency of identification with those categories.
• Local decision making about categories that would be useful given the size and diversity of the population served or surveyed, as well as the consideration that quality improvement activities tend to be locally based.
• A framework that would allow some flexibility in approaches to collection but retain uniform categories, in recognition of the different capacities of information systems.
• Fostering comparability across the variety of actors that collect and use these data.

Building on Previous Studies

In developing its rationale and framework for standardization, the subcommittee considers previous research on the categorization, collection, and use of race, ethnicity, and language data in health care settings. In 2000, Congress asked the National Academies to assess the ability of HHS data collection systems to measure racial, ethnic, and socioeconomic disparities. The request resulted in the 2004 National Research Council report Eliminating Health Disparities: Measurement and Data Needs, which recommends actions for HHS to take to ensure the routine collection and reporting of race and ethnicity data. The report acknowledges the importance of collecting data on race, ethnicity, socioeconomic status, and language and acculturation for use in making statistical inferences about disparities, but notes the lack of standardized collection and reporting of these data across all entities (NRC, 2004b).

NCVHS has historically emphasized to its HHS counterparts the necessity and benefits of collecting race, ethnicity, and language data, among other variables, under the premise that these data are essential to monitoring the health of the nation (NCVHS, 2001, 2004, 2005). In several reports over the past decade, the NCVHS Subcommittee on Populations has discussed challenges to collecting and using these data. The present report addresses these data collection challenges and proposes a framework for moving forward with standardized data collection across all health and health care entities, not just within HHS agencies or by recipients of federal funds. Previous reports have reiterated the importance of collecting more detailed ethnicity data than are captured by the OMB standard categories; this report proposes a template of categories so that entities wishing to collect detailed data can do so in systematic, uniform ways.

Limitations of the Study

Like previous IOM committees, the subcommittee recognizes the linkages among socioeconomic status, health literacy, and immigration with race, ethnicity, and language; however, these dimensions were beyond the scope of its charge. Lower socioeconomic status has been associated in the literature with poor health outcomes and high mortality rates since at least the early twentieth century (Isaacs and Schroeder, 2004; Link and Phelan, 1996; Lutfey and Freese, 2005). Time in the United States and immigration status also have implications for one's health and access to health care (Kagawa-Singer, 2006, 2009; Oh et al., 2002; Portes and Hao, 2002; Wadsworth and Kubrin, 2007).

While the subcommittee focuses exclusively on the categorization of race, ethnicity, and language—as it was charged to do—it recognizes that some differences in health care among racial, ethnic, and language groups reflect differences in socioeconomic status, immigration, and health literacy. Studying the roles of these constructs nevertheless presumes categorizations of race, ethnicity, and language of reasonable credibility and consistency for patients from whom the data are collected, providers who collect the data, and those analyzing the data for quality improvement purposes.

While the subcommittee concludes that a full consideration of Health IT technicalities is beyond the scope of its charge, its members are mindful of Health IT considerations in its recommendations. The subcommittee also notes the timeliness and relevance of its work to Section 13001 of ARRA¹⁰. The intersection between the subcommittee's work and emerging Health IT standards will be further discussed in Chapter 6 of this report.

Overview of the Report

The subcommittee is charged with recommending standards for the categorization and collection of race, ethnicity, and language data. Collection of data at various levels of the health care system implies that the data must be amenable to reporting and aggregation in consistent ways. To frame how the purposes and uses outlined in Figure 1-3 could best be met, the subcommittee addresses the following areas:

• Defining the specific variables to be collected: race (including the applicability of the OMB categories), ethnicity (whether limited to Hispanic ethnicity or expanded to other groupings), language (whether encompassing English language proficiency and spoken and/or written language needed for effective communication).
• Describing the nomenclature for each variable to ensure that the categories for each contain as valid and reliable data as possible.
• Defining a classification system for race and ethnicity that allows a hierarchical rollup so categorical data can be combined.
• Suggesting standardized approaches to coding race, ethnicity, and language categories to foster data linkages.
• Addressing key points of leverage to ensure both patient—provider and system-level improvement.

Chapter 2 reviews the available research on how more discrete categorization of ethnicity can reveal disparities and allow more precise targeting of initiatives for health care quality improvement. Chapter 3 addresses the utility of the OMB categories in capturing important cultural and social groups for statistical reporting before considering the collection of more granular ethnicity data and how standard coding of categories can allow for the sharing of data beyond a single service site. The chapter examines the geographic distribution of racial and ethnic groups across the United States and the need for balance between nationally uniform categories for data collection and flexibility in how different subsets of categories are used for local quality improvement. Chapter 4 reviews different approaches germane to the collection of language data, explores the need for data on spoken and written language, and examines language coding practices. Chapter 5 covers the challenges and barriers faced by health care organizations and providers of care in collecting these variables. The chapter explores how these challenges can be addressed through direct collection methods and use of indirect estimation techniques. Chapter 6 examines the role of various entities in informing and shaping the uptake of standardized categories of race, ethnicity, and language data. The chapter describes the opportunities afforded through the adoption of EHRs and more integrated Health IT systems that are likely to extend the capabilities of health care providers at all levels to collect and use these data systematically.

Race, ethnicity, and language data are tools for fighting discrimination, understanding disparities, and providing culturally and linguistically relevant services (Burdman, 2003). Thus, these data are useful and important for identifying and, ultimately, acting to reduce and eliminate disparities in health status and health care. These data alone, however, cannot address how to fix the issues brought to light in Chapter 2. Measurement cannot ensure the provision of culturally and linguistically appropriate care that incorporates racial and ethnic sensitivities, accommodates diverse views and approaches, and reduces disparities by improving access and quality.

References

AHIP (America's Health Insurance Plans). 2009. A legal perspective for health insurance plans: Data collection on race, ethnicity, and primary language. Washington, DC: America's Health Insurance Plans.

AHRQ (Agency for Healthcare Research and Quality). 2008. National Healthcare Disparities Report. Rockville, MD: AHRQ.

American Cancer Society. 2009. Can breast cancer be found early? http://www.cancer.org/docroot/CRI/content/CRI_2_4_3X_Can_breast_cancer_be_found_early_5.asp (accessed June 13, 2009).

AMIA (American Medical Informatics Association). 2007. Data stewardship definition. http://www.amia.org/files/definition_of_data_stewardship.pdf (accessed July 7, 2009).

Bennett, C. 2000. Racial categories used in the decennial censuses, 1790 to the present. Government Information Quarterly 17(2):161-180.

Berry, E. R., S. Hitov, J. Perkins, D. Wong, and V. Woo. 2001. Assessment of state laws, regulations and practices affecting the collection and reporting of racial and ethnic data by health insurers and managed care plans. Washington, DC: National Health Law Program (NHeLP).

Betancourt, J. R., A. R. Green, J. Emilio Carrillo, and E. R. Park. 2005. Cultural competence and health care disparities: Key perspectives and trends. Health Affairs 24(2):499-505.

Burdman, P. 2003. Exposing the truth and fiction of racial data. California Journal 11:40-46.

Byrd, W., and L. Clayton. 2000. A medical history of African Americans and the problem of race: Beginnings to 1900, An American health dilemma, Volume 1. New York: Routledge.

Cavalli-Sforza, L. L., P. Menozzi, and A. Piazza. 1994. The history and geography of human genes. Princeton, NJ: Princeton University Press.

Coltin, K. 2009. Implementation challenges for health plan collection of race, ethnicity & language data. Harvard Pilgrim Health Care. Presentation to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, February 9, 2009. Washington DC. PowerPoint Presentation.

Cooper, R., and R. David. 1986. The biological concept of race and its application to public health and epidemiology. Journal of Health Politics, Policy and Law 11(1):97-116.

Diamond, J. 1994. Race without color. Discover 15:82-89.

Ford, M. E., and P. A. Kelly. 2005. Conceptualizing and categorizing race and ethnicity in health services research. Health Services Research 40(5):1658-1675.

Friedman, D. J., B. B. Cohen, A. R. Averbach, and J. M. Norton. 2000. Race/ethnicity and OMB Directive 15: Implications for state public health practice. American Journal of Public Health 90:1714-1719.

Grady, D. 2009. Foreign ways and war scars test hospital. New York Times, March 28, A1.

Grieco, E. M., and R. C. Cassidy. 2001. Overview of race and Hispanic origin. Washington, DC: U.S. Census Bureau.

Hahn, R. A. 1992. The state of federal health statistics on racial and ethnic groups. JAMA 267(2):268-271.

Hasnain-Wynia, R., and D. W. Baker. 2006. Obtaining data on patient race, ethnicity, and primary language in health care organizations: Current challenges and proposed solutions. Health Services Research 41(4):1501-1518.

Hasnain-Wynia, R., and S. S. Rittner. 2008. Improving quality and equity in health care by reducing disparities. Chicago, IL: Northwestern University.

Hasnain-Wynia, R., D. Pierce, and M. A. Pittman. 2004. Who, when, and how: The current state of race, ethnicity, and primary language data collection in hospitals. New York: The Commonwealth Fund.

Hasnain-Wynia, R., D. Pierce, A. Haque, C. H. Greising, V. Prince, and J. Reiter. 2007. Health Research and Educational Trust Disparities Toolkit. www.hretdisparities.org (accessed December 18, 2008).

Hayes-Bautista, D. E., and J. Chapa. 1987. Latino terminology: Conceptual bases for standardized terminology. American Journal of Public Health 77:61-68.

Hedrick, H. L. 1999. Cultural competence compendium. Chicago, IL: American Medical Association.

HHS (U.S. Department of Health and Human Services). 2003. Guidance to federal financial assistance recipients regarding Title VI prohibition against national origin discrimination affecting limited English proficient persons. Washington, DC: U.S. Department of Health & Human Services.

Higgins, P. C., and E. F. Taylor. 2009. Measuring racial and ethnic disparities in health care: Efforts to improve data collection. Princeton, NJ: Mathematica Policy Research.

IOM (Institute of Medicine). 2001. Crossing the quality chasm: A new health system for the 21st Century. Washington, DC: National Academy Press.
2003. Unequal treatment: Confronting racial and ethnic disparities in healthcare. Edited by B. D. Smedley, A. Y. Stith and A. R. Nelson. Washington, DC: The National Academies Press.

Isaacs, S. L., and S. A. Schroeder. 2004. Class: The ignored determinant of the nation's health. New England Journal of Medicine 351(11):1137-1142.

Izquierdo, J. N., and V. J. Schoenbach. 2000. The potential and limitations of data from population-based state cancer registries. American Journal of Public Health 90(5):695-698.

Kagawa-Singer, M. 2006. Population science is science only if you know the population. Journal of Cancer Education 21:S22-S31.
2009. Measure of race, ethnicity and culture: Population science isn't science unless you know the population. UCLA School of Public Health. Presentation to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, March 12, 2009. Newport Beach, CA. PowerPoint Presentation.

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.

Kandula, N., R. Hasnain-Wynia, J. Thompson, E. Brown, and D. Baker. 2009. Association between prior experiences of discrimination and patients' attitudes towards health care providers collecting information about race and ethnicity. Journal of General Internal Medicine 24(7):789-794.

Kilbourne, A. M., G. Switzer, K. Hyman, M. Crowley-Matoka, and M. J. Fine. 2006. Advancing health disparities research within the health care system: A conceptual framework. American Journal of Public Health 96(12):2113-2121.

Kornblet, S., J. Prittsa, M. Goldstein, T. Perez, and S. Rosenbaum. 2008. Policy brief 4: Patient race and ethnicity data and quality reporting: A legal "roadmap" to transparency. Washington, DC: The George Washington University School of Public Health and Health Services.

Link, B. G., and J. C. Phelan. 1996. Understanding sociodemographic differences in health: The role of fundamental social causes. American Journal of Public Health 86:471-473.

Lurie, N., M. Jung, and R. Lavizzo-Mourey. 2005. Disparities and quality improvement: Federal policy levers. Health Affairs 24(2):354-364.

Lutfey, K., and J. Freese. 2005. Toward some fundamentals of fundamental causality: Socioeconomic status and health in the routine clinic visit for diabetes. The American Journal of Sociology 110(5):1326-1372.

Macdonald, S., V. Stone, R. Arshad, and P. de Lima. 2005. Ethnic identity and the Census. http://www.scotland.gov.uk/Resource/Doc/54357/0013571.pdf (accessed September 2, 2009).

NCVHS (National Committee on Vital and Health Statistics). 2001. Medicaid managed care data collection and reporting. Hyattsville, MD: U.S. Department of Health and Human Services.
2004. Recommendations on the nation's data for measuring and eliminating health disparities associated with race, ethnicity, and socioeconomic position. Hyattsville, MD: U.S. Department of Health and Human Services.
2005. Eliminating health disparities: Strengthening data on race, ethnicity, and primary language in the United States. Hyattsville, MD: U.S. Department of Health and Human
2007. Enhanced protections for uses of health data: A stewardship framework for 'secondary uses' of electronically collected and transmitted health data. Hyattsville, MD: U.S. Department of Health and Human Services.

NHPC (National Health Plan Collaborative). 2008. Toolkit to reduce racial & ethnic disparities in health care. Washington, DC: National Health Plan Collaborative.

NQF (National Quality Forum). 2008. National voluntary consensus standards for ambulatory care—measuring healthcare disparities. Washington, DC: National Quality Forum.

NRC (National Research Council). 2004a. The 2000 Census: Counting Under Adversity. Edited by C. F. Citro, D. L. Cork, and J. L. Norwood. Washington, DC: The National Academies Press.
— 2004b. Eliminating health disparities: Measurement and data needs. Edited by M. V. Ploeg and E. Perrin. Washington, DC: The National Academies Press.
— 2004c. Measuring racial discrimination. Edited by R. M. Blank, M. Dabady and C. F. Citro. Washington, DC: The National Academies Press.
2006. Multiple origins, uncertain destinies: Hispanics and the American future. Edited by M. Tienda and F. Mitchell. Washington, DC: The National Academies Press.

Office of Minority Health. 2001. National standards for culturally and linguistically appropriate services in health care. Washington, DC: U.S. Department of Health and Human Services.

Oh, Y., G. F. Koeske, and E. Sales. 2002. Acculturation, stress and depressive symptoms among Korean immigrants in the United States. Journal of Social Psychology 142:511-526.

OMB (Office of Management and Budget). 1997a. Recommendations from the Interagency Committee for the Review of the Racial and Ethnic Standards to the Office of Management and Budget concerning changes to the standards for the classification of federal data on race and ethnicity. Federal Register (3110-01):36873-36946.
—1997b. Revisions to the standards for the classification of federal data on race and ethnicity. Federal Register 62:58781-58790.

Pachter, L. M., S. C. Weller, R. D. Baer, J. E. Garcia, A. Garcia, R. T. Trotter, M. Glazer, and R. Klein. 2002. Variation in asthma beliefs and practices among mainland Puerto Ricans, Mexican-Americans, Mexicans and Guatemalans. Journal of Asthma 39(2):119-134.

Pavkov, M. E., W. C. Knowler, R. L. Hanson, and R. G. Nelson. 2008. Diabetic nephropathy in American Indians, with a special emphasis on the Pima Indians. Current Diabetes Reports 8:486-493.

Perot, R. T., and M. Youdelman. 2001. Racial, ethnic, and primary language data collection in the health care system: An assessment of federal policies and practices. New York, NY: The Commonwealth Fund.

Portes, A., and L. Hao. 2002. The price of uniformity: Language, family, and personality adjustment in the immigrant second generation. Ethnic and Racial Studies 25:889-912.

Regenstein, M., and D. Sickler. 2006. Race, ethnicity, and language of patients: Hospital practices regarding collection of information to address disparities in health care. Princeton, NJ: Robert Wood Johnson Foundation.

Rosenbaum, S., S. Kornblet, and P. C. Borzi. 2007. An assessment of legal issues raised in "high performing" health plan quality and efficiency tiering arrangements: Can the patient be saved? Washington, DC: The George Washington University School of Public Health and Health Services.

Schoenman, J. A., J. P. Sutton, A. Elixhauser, and D. Love. 2007. Understanding and enhancing the value of hospital discharge data. Medical Care Research and Review 64(4):449-468.

Siegel, B., J. Bretsch, V. Sears, M. Regenstein, and M. Wilson. 2007. Assumed equity: Early observations from the first Hospital Disparities Collaborative. Journal for Healthcare Quality 29(5):11-15.

Smedley, A. 1999. Race in North America: Origin and evolution of a worldview, second edition. Boulder, Colorado: Westview Press.

Thernstrom, S., A. Orlov, and O. Handlin. 1980. Harvard encyclopedia of American ethnic groups, second edition. Boston, MA: Harvard University Press.

Thomas, S. B. 2001. The color line: Race matters in the elimination of health disparities. American Journal of Public Health 91(7):1046-1048.

Ting, G. 2009. Applications of indirect estimation of race/ethnicity data in health plan activities. Wellpoint. Presentation to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, March 12, 2009. Newport Beach, CA. PowerPoint Presentation.

U.S. Census Bureau. 2000. State & County Quick Facts. http://quickfacts.census.gov/qfd/meta/long_68184.htm (accessed June 14, 2009).
—2001. Questions and answers for Census 2000 data on race. http://www.census.gov/Press-Release/www/2001/raceqandas.html (accessed April 17, 2009).
—2009. United States Census 2010 Form D-1(UL). Washington, DC: U.S. Census Bureau.

Wadsworth, T., and C. E. Kubrin. 2007. Hispanic suicide in U.S. metropolitan areas: Examining the effects of immigration, assimilation, affluence, and disadvantage. The American Journal of Sociology 112(6):1848-1885.

Wallman, K. K., S. Evinger, and S. Schechter. 2000. Measuring our nation's diversity: Developing a common language for data on race/ethnicity. American Journal of Public Health 90(11):1704-1708.

Wei, I. I., B. A. Virnig, D. A. John, and R. O. Morgan. 2006. Using a Spanish surname match to improve identification of Hispanic women in Medicare administrative data. Health Services Research 41(4):1469-1481.

Weinick, R. M., K. Flaherty, and S. J. Bristol. 2008. Creating equity reports: A guide for hospitals. Boston, MA: The Disparities Solution Center at Massachusetts General Hospital.

Williams, D. R. 1994. The concept of race in health services research: 1966 to 1990. Health Services Research 29(3):261-274.

Williams, D. R., R. Lavizzo-Mourey, and R. C. Warren. 1994. The concept of race and health status in America. Public Health Reports109(1):26-41.

Witzig, R. 1996. The medicalization of race: Scientific legitimization of a flawed social construct. Annals of Internal Medicine125(8):675-679.

Yankauer, A. 1987. Hispanic/Latino: What's in a name? American Journal of Public Health 77(1):15-17.

Youdelman, M., and S. Hitov. 2001. The current federal landscape in health care regarding the collection and reporting of data on race, ethnicity and primary language: A survey of the laws, regulations, policies, practices and data collection vehicles. In Racial, ethnic and primary language data collection: An assessment of federal policies, practices and perceptions, volume 2. Washington, DC: National Health Law Program (NHeLP).

¹ The 2000 Census: Counting Under Adversity provides an extensive review of the historical development of the racial and ethnic classifications used by the Bureau of the Census. Chapter 3 in Multiple Origins, Uncertain Destinies: Hispanics and the American Future reviews the origins of Hispanic ethnicity and its relationship to race.
² Other definitions of race abound. For example, OMB states that race and ethnicity should not be interpreted as being primarily biological or genetic in reference, but rather, thought of in terms of social and cultural characteristics as well as ancestry (OMB, 1997b). The Census Bureau complies with the OMB standards, noting that the standards "generally reflect a social definition of race recognized in this country. They do not conform to any biological, anthropological or genetic criteria " (U.S. Census Bureau, 2001).
³ EHRs are further defined in Chapter 6 of this report.
⁴ California, Maryland, New Hampshire, New Jersey, New York, and Pennsylvania prohibit insurers from requesting an applicant's race, ethnicity, religion, ancestry, or national origin in applications, but the states allow insurers to request such information from individuals after enrollment (AHIP, 2009).
⁵ A list of legislation relevant to race, ethnicity, and language data is included in Appendix B.
⁶The Civil Rights Act of 1964, Public Law 88-352, 78 Stat. 241, 88th Cong., 2d sess. (July 2, 1964).
⁷ Medicare Improvements for Patients and Providers Act of 200, Public Law 110-275 § 118, 110th Cong., 2d sess. (July 15, 2008).
⁸American Recovery and Reinvestment Act of 2009, Public Law 111-5 § 3002(b)(2)(B)(vii), 111th Cong., 1st sess. (February 17, 2009).
⁹ Health Insurance Portability and Accountability Act of 1996, Public Law 104-191, 104th Cong., 2d sess. (August 21, 1996).
¹⁰ Section 13001 is known as the Health Information Technology for Economic and Clinical Health Act or the Health ITECH Act.