6. Implementation (continued)
Standards-Setting and Professional Organizations
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 collecting these data for quality improvement efforts (Bilheimer and Sisk, 2008; Lurie et al., 2008; NCQA, 2009; Siegel et al., 2008).
Joint Commission, NCQA, and URAC
Accrediting organizations such as the Joint Commission, National Committee for Quality Assurance (NCQA),
and URAC14 either have developed or are developing CLAS-like standards for
their accreditation reviews or for voluntary self-analysis by organizations. These standards do not always cover
all demographic variables (e.g., those of the Joint Commission cover language and communication needs but not race
or ethnicity), or they may not go beyond requiring the collection of demographic data, leaving the use of those data
for performance improvement optional (The Joint Commission, 2008; NCQA, 2008a; URAC, 2007).
For many years, the Joint Commission's accreditation standards for hospitals and other accredited entities
(including, for example, those providing ambulatory health care, behavioral health care, home care, and hospice
care) have required that patients' culture, ethnicity, race, and religious preferences and needs be respected and
that their communication needs be met. To facilitate this patient-centered approach, in 2005 the Joint Commission
proposed a standard that would have required documentation in each patient's health record of the patient's race,
ethnicity, and language and other communication needs. The response from the field, while supportive of recording
this information for each patient, argued that unless race and ethnicity data were recorded in standardized categories,
their use for performance improvement would be limited. In light of this feedback, in January 2006 the Joint
Commission began requiring that language and other communication needs be recorded in each patient's record,
but it delayed requiring recording of race and ethnicity until a widely accepted standardized approach became
available. As of this writing, the Joint Commission is again proposing a requirement that race and ethnicity be
recorded and that these data be used in planning services to meet the needs of persons in the community and in
performance improvement (The Joint Commission, 2009). The Joint Commission anticipates the response the field
to be that standardized categories are needed.15
At this point, NCQA is planning to address CLAS as a voluntary accreditation module, to be available in
2010. It is expected that the module will address the use of race, ethnicity, and language data in stratifying quality
performance data to identify both disparities in health care and problems in meeting language needs, as well as the
use of those findings to drive quality improvement. Currently, NCQA has a program that rewards health plans for
demonstrating innovative practices in providing for culturally and linguistically appropriate services (NCQA, 2006, 2007, 2008b). Previously, NCQA, with funding from The California Endowment, provided grants and technical
assistance to small physician practices serving minority populations to learn about their needs for conducting and
sustaining quality improvement activities. As a result of this initiative, the need for standardized collection of race,
ethnicity, and language data in EHR systems was brought to light (NCQA, 2009).
National Quality Forum (NQF)
NQF is a membership organization whose mission is to "promote a common approach to measuring health
care quality and fostering system-wide capacity for quality improvement" through endorsement of consensus
standards (NQF, 2009). NQF recently released a framework for culturally and linguistically responsive services
and encourages the collection of race, ethnicity, and language data in accordance with the Hospital Research &
Education Trust (HRET) Toolkit (NQF, 2008). The subcommittee has suggested changes to elements of the HRET
Toolkit, in particular incorporating separate collection of a granular ethnicity variable, adding "Some other race"
to the Office of Management and Budget (OMB) category set, and having a more expansive list of language categories. The subcommittee also favors
the collection and retention for analysis of specific multiple-race combinations (i.e., having data on each race that
an individual selects when given the option to select one or more races), rather than losing that detail by only
offering patients the more general response option of "multiracial" as delineated in the Toolkit.
Commission to End Health Care Disparities
A collaborative partnership involving the medical community, the American Medical Association (AMA), the
National Medical Association, and the National Hispanic Medical Association's Commission to End Health Care
Disparities brings together 35 state and specialty medical societies. As a group, they have reaffirmed their collective
commitment to ending disparities in health and health care by taking steps to (AMA, 2009b):
- Increase awareness of disparities in their own practices within the physician community.
- Promote better data collection.
- Promote workforce diversity.
- Increase education and training.
The Commission is considering continuing medical education activities and exploration of core curriculum on
health disparities for medical students that might be considered a criterion for medical school accreditation. The
Commission also notes that race, ethnicity, and language proficiency data should be utilized for clinical quality
performance measurement, with disparities an appropriate area for the Physician Consortium for Performance
Improvement to focus its efforts (AMA, 2009a).
The AMA Code of Ethics guides physicians to examine their practices to ensure that differences in care are
based on clinical necessity or patient preference and do not constitute inequitable treatment. The code also states
that physicians should take steps to minimize language barriers so as to enhance both patient and physician understanding
of medical needs (AMA, 2005). Collection of race, ethnicity, and language data would allow stratification
of quality measures in physician practices to create awareness of differential practice patterns or response
among patient populations and accordingly identify opportunities for quality improvement. The ARRA provision
for "meaningful use" of EHRs applies to enabling the exchange of health information and reporting on clinical
quality measures to CMS, medical boards, private plans, and others. Medicare staff observed that CMS sees "in
legislation and in operation, . a future for measuring quality in physician offices" (McGann, 2009). CMS sponsors
quality improvement research projects at the practitioner level, such as the Generating Medicare Physician Quality
Performance Measurement Results (GEMS) program which tracked 12 HEDIS (Healthcare Effectiveness Data
and Information Set) ambulatory care measures in a physician group practice fee-for-service environment using
an amalgam of Part A, B, and D claims data and race and ethnicity data from the enrollment database (McGann,
2009; Reilly, 2009a). Having race, ethnicity, and language data for their own patients would also enable providers
to review performance at the point of care (Kmetik, 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
- 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
- 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
States have an opportunity to shape the level of detail of race, ethnicity, and language data collected in their
programs by establishing which categories of granular ethnicity and language should be used in addition to the
basic OMB categories of race and Hispanic ethnicity. Each state organizes its own programs into different administrative
units, so no attempt is made in this report to identify all state actors that have important roles in ensuring
quality improvement in health care. State health or other departments have important responsibilities related to
protecting and improving the health and health care of the population statewide, and are key players in ensuring
the adoption of standards and collection of data. However, providers and plans have reported that they receive
conflicting data requests from different agencies within the same state. Categories for race, ethnicity, and language
can be selected at the state level, with careful consideration of local as well as national stakeholder needs when
categories are defined for statewide aggregation and reporting for insurance program quality measures, disease
registries, birth and death vital statistics, hospital discharges, health care surveys, patient safety reporting, and other
activities. State-level aggregation and reporting can help illuminate the health care issues of population groups
whose disparities may not be apparent because of small sample sizes at the local level.
As large purchasers of care through Medicaid and CHIP programs, states have leverage with managed care
organizations and providers. States can use this leverage to ensure that health care entities collect the recommended
race, ethnicity, and language data and use findings from analyses of these data to design quality improvement
efforts. Medicaid provides coverage for a large portion of minority groups; thus, states have an interest in ensuring
that the population covered is receiving appropriate levels of care (Angeles and Somers, 2007). Currently, some
states report their HEDIS measures by race and ethnicity, and others do not (Michigan Department of Community
Health, 2009; NC Department of Health and Human Services, 2009).
The subcommittee concludes that state entities can play a central role as aggregators and disseminators of
provider, plan, community, and state-level quality improvement data.
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.
Although it was beyond the scope of the subcommittee's deliberations to determine the extent of the need,
representatives of state data agencies noted that one of the greatest barriers to state health departments, Medicaid
agencies, and regulatory agencies in fulfilling responsibilities related to certification, regulation, and monitoring
activities has been the lack of funding to expand and improve state data collection activities. The collection of race,
ethnicity, and language data across providers and plans in a community and state requires resources for rulemaking,
provider training, implementation of reporting, and assurance of data quality, yet many states are cutting back their
data reporting initiatives, including a reduction in workforce, because of state budget limitations (NRC, 2003).16
Efforts are under way to institute national standards for technology, performance measurement, and data
aggregation and exchange that complement local data collection and experiences with performance improvement
and reporting (HHS, 2009c; Roski, 2009). To date, it has been difficult to either combine or compare performance
data stratified by race, ethnicity, or language across payment and delivery systems, which has limited the utility
of such data for assessing the performance of the health care system as a whole or in specific geographic areas
with respect to disparities in care. Standardization of the categories of race, ethnicity, and language data will
promote greater comparability of data collected directly by providers or health plans or, for instance, transferred
from providers to plans. Estimates of health care disparities derived through indirect estimation techniques, such
as geocoding and surname analysis, can provide a helpful bridge until directly collected demographic data are
more universally available.
The subcommittee has proposed a framework for the collection of race, ethnicity, and language data that it
believes would facilitate the collection of data by individual entities, the comparison of quality of care received
by specific groups across entities and regions, and the combination of data for purposes of analyzing health care
needs and identifying disparities. While important disparities in quality of care can be identified among the race
and ethnicity groups captured by the OMB categories, those categories often are not sufficiently descriptive of local
and state populations because of the diversity of ethnic groups in different parts of the country, states, or specific
communities. A number of analyses have identified disparities among members of more granular ethnic categories
that are masked by the aggregate OMB categories. More discrete population data could be used to identify
opportunities for quality improvement and outreach without inappropriate or inefficient targeting of interventions
to an entire broad racial or ethnic category.
The subcommittee recommends for quality improvement purposes: (1) the collection and use of data on granular
ethnicity and language need, allowing local providers, communities, or states to select sets of categories from
national standard lists that are most informative about the populations they serve, and (2) the continued collection
of data in the OMB race and Hispanic ethnicity categories to support consistency across as many complementary
data collection efforts as possible (e.g., poverty statistics, educational attainment). The national categories should
be consistently coded to foster exchange among systems of like data categories across providers, states, plans, or
payers for aggregation or comparison purposes. Given space constraints of paper forms or intake screens, local
category lists may be limited in the number of choices; electronic collection systems can often be designed to
collect many more categories than would be optimal on paper forms. The categories used should be descriptive of
the population served, reflect quality issues related to the health and health care of that population, and take into
account evidence or the likelihood of disparities among ethnic groups within the population. To ensure that each
individual has the opportunity to self-identify and that these identifiers will be captured, there should always be an
opportunity to add ethnicities and languages not contained on a list of check-off boxes. Therefore, an open-ended
"Other, please specify:—" response option should be incorporated for both granular ethnicity and language
when a limited list of categories is presented for response. These responses can help identify when additional
categories may need to be added to prespecified lists on data collection instruments.
Many actors play a role in health care delivery and quality assessment, and each has a role to play in furthering
the collection of meaningful race, ethnicity, and language data for quality improvement. National development
of standardized categories 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
collection of these data in accordance with the framework proposed in this report should be reflected in guidance
to recipients of HHS and state funding, incorporated into the accreditation standards and performance measurement
endorsements of accreditation and standards-setting organizations, and coordinated across federal health
care delivery systems.
Collecting race, ethnicity, and language data using standard categories can help promote equity through
enhanced patient-provider communication and the provision of evidence-based quality care. Achieving the goals
of quality care requires monitoring to ensure that all populations receive patient-centered, safe, effective, timely,
efficient, and equitable care.
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).
— 2009a. Commission to End Health Care Disparities, five year summary. Chicago, IL: American Medical Association.
— 2009b. Eliminating health disparities. http://www.ama-assn.org/ama/pub/physician-resources/public-health/eliminating-healthdisparities.shtml (accessed May 12, 2009).
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.
Blumenthal, D. 2009. Stimulating the adoption of health information technology. New England Journal of Medicine 360(15):1477-1479.
CDC (Centers for Disease Control and Prevention). 2000. Race and Ethnicity Code Set version 1.0. Atlanta, GA: Centers for Disease Control
— 2009. Guides. http://www.cdc.gov/phin/resources/guides.html (accessed May 22, 2009).
Chien, A. T. 2007. The potential impact of performance incentive programs on racial disparities in healthcare. In Eliminating healthcare disparities
in America: Beyond the IOM report, edited by Williams, R. A. Totowa, NJ: Humana Press. p. 237-256.
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.
CMS (Centers for Medicare and Medicaid Services). 2009. Physician quality reporting initiative. http://www.cms.hhs.gov/pqri (accessed May 22, 2009).
HL7 (Health Level 7). 2009. What is HL7? http://www.hl7.org/about/hl7about.htm (accessed May 22, 2009).
HHS (U.S. Department of Health and Human Services). 2000. Healthy People 2010: Understanding and improving health
. Washington, DC: 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.
— 2009a. Fact sheets: Details for Medicare and Medicaid health information technology: Title IV of the American Recovery and Reinvestment
July 20, 2009).
— 2009b. Health IT terms: Glossary of selected terms related to e-Health. Resources
July 20, 2009).
— 2009c. Nationwide Health Information Network (NHIN): Background & scope
. http://healthit.hhs.gov/portal/server.pt (accessed July
—, 2009d. HealthIT.hhs.gov Information Related to the American Recovery and Reinvestment Act of 2009
(Accessed July 20, 2009).
— 2009e. Civil rights: Limited English proficiency (LEP)
April 3, 2009).
HHS Data Council. 1999. Improving the collection and use of racial and ethnic data in health and human services. Washington, DC: HHS.
HHS Office of Minority Health (OMH). 2007. National standards on culturally and linguistically appropriate services (CLAS). http://minorityhealth.hhs.gov/templates/browse.aspx?lvl=2&lvlID=15 (accessed May 13, 2009).
HRSA (Health Resources and Services Administration). 2008. The National Health Center Program: 2007 national aggregate UDS data. Table 4: Patients by socioeconomic characteristics. http://bphc.hrsa.gov/uds/2007data/National/NationalTable4Universal.htm (accessed
August 3, 2009).
Johnson, C. H., and M. Adamo. 2008. The SEER program coding and staging manual 2007 . Bethesda, MD: National Cancer Institute.
The Joint Commission. 2008. The Joint Commission 2008 requirements related to the provision of culturally and linguistically appropriate health care, version 2008-1. Oakbrook Terrace, IL.
— 2009. Proposed requirements to advance effective communication, cultural competence, and patient-centered care. http://www.jointcommission.org/Standards/FieldReviews
Kaiser Family Foundation. 2005. Dual eligibles as a percent of total Medicaid enrollees, 2005. http://www.statehealthfacts.org/comparemap table.jsp?ind=305&cat=6 (accessed July 17, 2009).
— 2008. Total number of Medicare beneficiaries, 2008. http://statehealthfacts.kff.org/comparemaptable.jsp?ind=290&cat=6&sub=74&yr=63&typ=1&sort=a (accessed July 17, 2009).
— 2009. Total Medicaid enrollment, FY2006. http://www.statehealthfacts.org/comparemaptable.jsp?ind=198&cat=4&sub=52&yr=29&typ=1&sort=a (accessed July 20, 2009).
Kmetik, K. 2009. American Medical Association. Presentation to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, February 10, 2009. Washington, DC. PowerPoint® Presentation.
Lurie, N., A. Fremont, S. A. Somen, K. Coltin, A. Gelzer, R. Johnson, W. Rawlins, G. Ting, W. Wong, and D. Zimmerman. 2008. The National Health Plan Collaborative to reduce disparities and improve quality. Joint Commission Journal on Quality and Patient Safety 34(5):256-265.
Lurie, N., M. Jung, and R. Lavizzo-Mourey. 2005. Disparities and quality improvement: Federal policy levers. Health Affairs 24(2):354-364.
McGann, P. 2009. Linking quality measurement to interventions: The role of CMS. Centers for Medicare & Medicaid Services. Presentation to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, February 10, 2009. Washington,
DC. PowerPoint Presentation.
Michigan Department of Community Health. 2009. Michigan Medicaid HEDIS 200 results: Statewide aggregate report. Lansing, Michigan: Health Services Advisory Group.
NCQA (National Committee for Quality Assurance). 2006. Innovative practices in multicultural health care. Washington, DC: NCQA.
— 2007. Innovative practices in multicultural health care. Washington, DC: NCQA.
— 2008a. Draft standards for culturally and linguistically appropriate services. Washington, DC: NCQA.
— 2008b. Innovative practices in multicultural health care. Washington, DC: NCQA.
— 2009. Supporting small practices: Lessons for health reform. Washington, DC: NCQA.
NCVHS (National Committee on Vital and Health Statistics). 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 Services.
Nerenz, D. R., and D. Darling. 2004. Addressing racial and ethnic disparities in the context of Medicaid managed care: A six-state demonstration
project. Rockville, MD: HRSA.
North Carolina Department of Health and Human Services. 2009. QEHO initiatives. http://www.dhhs.state.nc.us/dma/quality/ (accessed July
NQF (National Quality Forum). 2008. National voluntary consensus standards for ambulatory care—measuring healthcare disparities. Washington,
DC: National Quality Forum.
— 2009. About us. http://www.qualityforum.org/about (accessed June 10, 2009).
NRC (National Research Council). 2003. Improving racial and ethnic data on health: Report of a workshop. Washington, DC: The National
OMB (Office of Management and Budget). 1977. Statistical policy directive No. 1 , race and ethnic standards for federal statistics and administrative
reporting. http://wonder.cdc.gov/wonder/help/populations/bridged-race/Directive15.html (accessed August 3, 2009).
— 1997. Revisions to the standards for the classification of federal data on race and ethnicity. Federal Register 62:58781-58790.
Reilly, T. 2009a. Data improvement efforts: Centers for Medicare & Medicaid Services. Centers for Medicare and Medicaid Services. Presentation
to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, February 10, 2009.
Washington, DC. PowerPoint® Presentation.
— 2009b. MIPPA section 185 . Presentation at the Centers for Medicaid and Medicare Services Health Disparities Summit, May 22, 2009.
Baltimore, MD: CMS. PowerPoint® Presentation
Rosenbaum, S., S. Abramson, and P. MacTaggart. 2009. Health information law in the context of minors. Pediatrics 123 (Suppl 2):S116-121.
Roski, J. 2009. Road map for better performance information through distributed data network. The Brookings Institution. Presentation to the
IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, February 10, 2009. Washington, DC.
Rust, G., and L. A. Cooper. 2007. How can practice-based research contribute to the elimination of health disparities? Journal of the American
Board of Family Medicine 20(2):105-114.
Shin, H. B., Bruno, R. 2003. Language use and English-speaking ability: 2000. Washington, DC: U.S. Census Bureau.
Siegel, B., J. Bretsch, K. Jones, V. Sears, L. Vaquerano, and M. J. Wilson. 2008. Expecting Success: Excellence in cardiac care results from
Robert Wood Johnson Foundation Quality Improvement Collaborative. Princeton, NJ: Robert Wood Johnson Foundation.
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.
Taylor-Clark, K., A. B. Anise, Y. Joo, and M. Chin. 2009. Massachusetts Superset. Washington, DC: The Brookings Institution.
URAC. 2007. A cultural competency standards crosswalk: A tool to examine the relationship between the OMH CLAS standards and Joint
Commission/URAC/NCQA accreditation standards. Washington, DC: URAC. PDF Presentation.
U.S. Census Bureau. 2002. Modified race data summary file: 2000 Census of population and housing, technical documentation. http://www.census.gov/popest/archives/files/MRSF-01-US1.html#fig1 (accessed February 25, 2009).
— 2008. GCT-T1: Population Estimates from the 200 Population Estimates Data Set. Washington, DC: U.S. Census Bureau.
Williams, T. 2009. Healthcare quality and disparities: Implications for pay for performance. Integrated Health Association. Presentation to the IOM Committee on Future Directions for the National Healthcare Quality and Disparities Reports, March 12, 2009. Newport Beach, CA. PowerPoint Presentation.
Wisconsin Cancer Reporting System. 2008. WCRS abstract code manual, 2nd edition. Madison, WI: Division of Public Health, Wisconsin
Department of Health Services.
Youdelman, M. 2007. Medicaid and SCHIP reimbursement models for language services. Washington, DC: National Health Law Program.
1 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. The Office of the National Coordinator's definition is included in the following
section on Electronic Health Records.
2 Personal communication, S. Ganesan, Centers for Disease Control and Prevention, June 3, 2009.
3 In addition to the numerical codes, the CDC/HL7 Code Set includes an alphanumeric hierarchical code that places each category in a
hierarchical position related to the OMB categories of race and Hispanic ethnicity.
4 The Library of Congress is the registration authority for the ISO-639-2 codes, while SIL is the registration authority for the ISO-639-3
5 Personal communication, D. Pollack, Centers for Disease Control and Prevention, May 7, 2009.
6 Personal communication, S. Ganesan, Centers for Disease Control and Prevention, and B. Hamilton, National Center for Health Statistics,
June 3, 2009.
7 Personal communication, H. Shin, Census Bureau, July 13, 2009.
8 American Recovery and Reinvestment Act of 2009, Public Law 111-5 § 3002(b)(2)(B)(vii), 111th Cong., 1st sess. (February 17, 2009).
9 D. Blumenthal, ONC, HHS at the IOM Meaningful Use Workshop, July 13, 2009.
10 PQRI incentive payments are only currently authorized through 2010.
11 44.8 million Medicare beneficiaries in 2008 and 58.7 million Medicaid and CHIP recipients in 2006 with dual enrollment at about 10
million, plus 8.9 million of the 16 million served by health centers are uninsured or have insurance other than Medicare or Medicaid. The U.S.
population, as of July 1, 2008, was 304 million (HRSA, 2008; Kaiser Family Foundation, 2005, 2008, 2009; U.S. Census Bureau, 2008).
12 Children's Health Insurance Program Reauthorization Act of 2009, Public Law 111-3, 111th Cong., 1st sess. (February 4, 2009).
13 ARRA authorizes $20 billion for health information technology.
14 Formerly known as the Utilization Review Accreditation Commission.
15 Personal communication, P. Schyve, The Joint Commission, May 11, 2009.
16 Personal communication, D. Love, National Association of Health Data Organizations, and B. Rudolph, The Leapfrog Group, January
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