Data Collection and Reporting for Healthcare Disparities
Collecting accurate equity data supports efforts to reduce healthcare disparities and create equal care for all
by Jennifer Hornung Garvin , PhD, MBA, RHIA; Theresa D. Jones , MHA, RHIA; Lydia Washington , MS, RHIA, CPHIMS; and Christine Weeks , BA
The benefits of complete and accurate data capture have far-reaching impact, including improving the quality of care by addressing disparities in care.
In the 2002 landmark study Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, the Institute of Medicine documented evidence that race and ethnicity are significant predictors of the quality of care, observing that minorities who had the same insurance, status, and income as nonminorities received a lower quality of care.1
In that study IOM described racial and ethnic healthcare disparities as racial or ethnic differences in the quality of healthcare that are not due to access-related factors or clinical needs, preferences, and appropriateness of intervention. Other studies and reports have demonstrated a similar relationship between healthcare disparities and the quality of healthcare.
Addressing such disparities requires that providers capture better data about race, ethnicity, and socioeconomic status, an effort complicated by the sensitive nature of the data and the challenges of categorizing them appropriately.
Addressing Health Disparities
The IOM study provides recommendations for research and addresses the importance of data collection that affects care disparities. Federal and state organizations have turned their attention to the issue. The Agency for Healthcare Research and Quality, the Centers for Medicare and Medicaid Services, and state public health entities have ongoing initiatives to address healthcare disparities.
Accrediting agencies are also focusing on aspects of care that could be associated with disparities. For example, the Joint Commission’s Hospitals, Language and Culture Project has identified the challenges associated with cultural and language barriers in hospital settings and offers a framework and organizational self-assessment tool for addressing these barriers and meeting the needs of diverse patient populations.2,3
At the heart of these and other efforts to develop effective strategies to address healthcare disparities is the need for accurate and complete data. However, data describing racial, ethnic, language, cultural, and socioeconomic characteristics are frequently inaccurate, incomplete, and lacking in detail in the healthcare setting. Sometimes they are not collected at all.
This may be due to the nature of the information itself. Information about an individual’s race, ethnicity, and socioeconomic status—sometimes referred to as “equity data”—may be considered to be of a sensitive nature both by those collecting it and the individuals to whom it pertains.
Even so, equity data are essential for research, analysis, planning, measurement, and implementation of initiatives that could reduce healthcare disparities, and healthcare organizations are increasingly being called on to ensure that the data they capture can meet these needs. Similar to diagnosis and procedure coding, equity data developed during a healthcare encounter have many uses, and this multidimensional aspect of their use should be a consideration in the assignment of racial and ethnic categories.
Uniform Data Collection Process
The Uniform Hospital Discharge Data Set (UHDDS), issued by the Centers for Disease Control and Prevention, has been considered the de facto standard for collecting data on inpatients related to race and ethnicity.4 The UHDDS currently describes race using the following categories: American Indian/Eskimo/Aleut, Asian or Pacific Islander, Black, White, Other Race, and Unknown.5 The data set defines ethnicity as Spanish origin/Hispanic, Non-Spanish origin/Non-Hispanic, and Unknown. The Uniform Ambulatory Care data set uses the same definitions for race and ethnicity, making it easier to compare data for inpatients and ambulatory patients in the same facility.6
The limits imposed by the UHDDS categories may need to be addressed in order to facilitate the use of racial and ethnic categories for performance measurement, administrative planning, and regulatory purposes. The Office of Management and Budget and the US Census Bureau both use more extensive descriptors for race and ethnicity, and these descriptors may well need to be evaluated and harmonized for use in revised UHDDS racial and ethnic categories.
The following data are actual racial summary data reported from one hospital using the UHDDS. In this example, the number of patients in the “unknown” category represents the third largest racial designation, suggesting an overuse of the category. One possible contributor may be that there are too few categories.
In this case the UHDDS race and ethnicity classifications may result in data that lack the specificity required for use in quality assessment and improvement of health disparities. The industry would benefit from an analysis of the data set categories to ensure that the categorizations are accurate and adequate.
The optimal number of categories is that which sufficiently differentiates between groups with unique needs and issues while affording individuals the opportunity to self-identify their group or groups. For example, studies have shown that Latinos frequently do not make a distinction between race and ethnicity, sometimes necessitating one or more categories that capture both race and ethnicity (e.g., Hispanic/White; Hispanic/Black; Hispanic/Declined).7 Broad categories such as “Asian” may not capture important ethnic information when such a category could pertain to individuals from countries as culturally diverse as India, Japan, and Vietnam.
In addition to the US Office of Management and Budget’s Standards for the Classification of Federal Data on Race and Ethnicity, the UHDDS, and the Census Bureau classifications, the HRET Toolkit recommends code sets and guidelines for systematically collecting data on race, ethnicity, and primary language (see sidebar above). Healthcare organizations should assess which code set best meets their needs and make procedural modifications as necessary to capture the information they need to address inequities in the populations they serve.8
Regardless of how many categories an organization uses, the process that staff use to collect the data is important to the quality of the data. Typically, accuracy increases dramatically when individuals are allowed to self-identify their race or ethnicity, rather than admission staff recording the information by observation or assumption.9 Therefore it is highly recommended that individuals be allowed to self-select as few or as many categories as they feel are necessary to describe themselves.
Although equity data are frequently collected during the registration or initial assessment or intake process, HIM departments play an important role in ensuring the quality of the data. This may include conducting a performance improvement assessment (as illustrated in the sidebar, at left), developing policies and procedures, and conducting training for those involved in direct collection and follow-up auditing. All are important initiatives that will ensure high quality data.
In the earlier example of summary race data, the hospital used a performance improvement process to determine that the “unknown” category should not be used by registrars unless the patient is not coherent and there is no one accompanying who can provide the information. The hospital also determined the need for training for registrars on how to talk with patients to obtain accurate information. As a result the use of the category decreased.
Important questions for an assessment include:
Barbara Odom-Wesley, PhD, RHIA, FAHIMA
Rachelle Stewart, DrPH, RHIA, FAHIMA
Mattie Wilson, MA, RHIA
Agency for Healthcare Research and Quality. “National Healthcare Disparities Report.” 2007. Available online at www.ahrq.gov/qual/qrdr07.htm#nhdr.
Health Research and Educational Trust. “Collecting Race, Ethnicity, and Primary Language Data: Tools to Improve Quality of Care and Reduce Health Care Disparities.” 2005. Available online at www.HRETdisparities.org.
Jennifer Hornung Garvin (email@example.com) is research health science specialist at IDEAS Center SLCVA and assistant professor in the Division of Clinical Epidemiology, University of Utah. Theresa D. Jones (firstname.lastname@example.org) is director of clinical information services at Abington Memorial Hospital, Abington, PA. Lydia Washington is a practice director at AHIMA. Christine Weeks is communications coordinator at the Center for Health Equity Research and Promotion, Philadelphia VA Medical Center.