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Race and Ethnicity Data Improvement Toolkit


The Case for Improving Race, Ethnicity, and Language Data

At the highest level, the case for improving R/E/L data is easy to understand: there is clear evidence that racial, ethnic, and language-based disparities exist in healthcare. In 2003, the Institute of Medicine released a landmark report, Unequal Treatment, which documented the disparities in healthcare in the United States. Each year, AHRQ produces a congressionally mandated National Healthcare Disparities Report to track the nation’s progress in reducing disparities, and notes in the 2012 report that our system of healthcare often distributes services inefficiently and unevenly.

In recent years, there has been increased attention on eliminating disparities in access to care and health outcomes for racially, ethnically, and linguistically diverse populations in the United States. Passage of the Patient Protection and Affordable Care Act (ACA) in 2010 called for the improvement of health status and quality of healthcare for priority populations. Provisions in the ACA require states to collect R/E/L data in an effort to better understand and reduce healthcare disparities.

There is good reason to do so. Disparities in healthcare and health outcomes attributable to differences in R/E/L are well documented and persistent even after adjusting for differences in related characteristics such as education, income, insurance, access to care, and health status. It has been estimated by the Joint Center for Political and Economic Studies, that racial and ethnic disparities in health and healthcare cost the United States $1.24 trillion between 2003 and 2006: over $200 billion for direct medical expenses, and another $1 trillion for the indirect costs such as lost quality of life years and lost productivity (“The Economic Burden of Health Inequities in the United States” Disclaimer In addition, states are using R/E/L data in innovative ways to identify and reduce disparities—such as creating race and ethnic health disparities report cards, assessing statewide costs, reducing disparities through healthcare reform, and mapping healthcare disparities to identify geographic areas in need of improvement ( Therefore, there is a growing need to improve the processes and protocols to collect R/E/L patient information.

Disparities may be due to differences in access to care, provider biases, poor provider–patient communication, or poor health literacy. Americans do not always receive the care they need, or they receive care that causes harm or that is delivered too late or without full consideration of patient preferences and values. There is an obvious need to document and improve the quality of care provided to at-risk populations. Race, ethnicity, and primary language play a significant role in these disparities.

Patients with limited English proficiency and those who are members of racial/ethnic minorities are at greater risk of adverse events compared to their English-speaking white counterparts, and are more likely to be prescribed expensive tests for conditions that could have been diagnosed through basic history-taking. Such patients are also more likely to have longer hospital stays for particular medical and surgical conditions, and potentially avoidable readmissions for selected chronic conditions. When patients with limited English proficiency or those from historically underserved racial/ethnic groups have trouble understanding their medical conditions, treatment plans, or discharge instructions, it not only leads to poorer health outcomes for these patients, but also results in multiple liability exposures as well as increased costs for the treating hospital.

Benefits to Improving the Collection of R/E/L Data

The rationale for collecting R/E/L data is compelling. Accurate and thorough collection of R/E/L data is necessary for measuring disparities in care. Identifying and measuring disparities offers healthcare organizations the opportunity to exert a positive influence on the cost, quality, patient safety, and risk management factors that arise from unequal care. Reliable information on R/E/L allows healthcare providers to fulfill their obligation to be responsive to the communities they serve. Collecting valid data is essential groundwork for conducting research that will identify the reasons for R/E/L-based health disparities and lead to interventions to ensure that all patients receive quality healthcare.

Improvement in the quality of care delivered will come about when healthcare organizations use these data to improve access to and use of their services, and demonstrate improvements on metrics such as patient safety, timeliness of care, effectiveness of care, and patient-centeredness. Standardized data collection, analysis, and reporting of R/E/L data will help to not only identify differences in care, but also to guide the selection and provision of specific services needed by hospitals’ patient populations—including preventive healthcare services, interpreter services, staff cultural competency training, and culturally and linguistically appropriate health education materials.

As the population of the United States grows more racially and culturally diverse with each passing decade, healthcare workers—physicians, nurses, and other practitioners—are faced with unique challenges in providing care to the diverse communities that they serve. In their interactions with their patients, clinicians must take into account not only factors such as the patient’s age, gender, and comorbidities, but also the patient’s perceptions about illness, any religious or cultural barriers to receiving certain kinds of treatment, a preference to communicate in a language other than English, and socio-cultural background. Patient-centered care involves developing treatment plans that are not only clinically effective but also compatible with the patient's cultural values, preferences, and needs. The collection of R/E/L data will enable hospitals to pinpoint the resources needed by clinicians to provide effective patient-centered care. Resources could include translation services, patient educational materials that reflect the language preferences of cultural subgroups being served by the provider organization, and training to help clinicians deliver care in a more effective manner.

Using the Data to Track and Reduce Disparities

Translating data into actionable information allows stakeholders to move beyond simply understanding disparities. Enhanced data can be used to compare trends across groups, develop targeted strategies, and evaluate the progress of interventions.

Using data to develop reports available to the general public also serves as an important resource for consumers and communities to highlight and better understand gaps in care. Each year, the Agency for Healthcare Quality and Research (AHRQ) publishes national data examining the quality of healthcare in the United States. These reports, the National Healthcare Quality Report (NHQR) and the National Healthcare Disparities Report (NHDR) highlight the magnitude of disparities in quality of care and health status among priority populations, including racial and ethnic minorities. In addition to national-level reporting, AHRQ uses race and ethnicity data to develop snapshots of states’ disparities for benchmarking.

The HCUP Topical Reports - Race/Ethnicity Disparities page provides additional information on approaches to using race/ethnicity data for reducing disparities in the quality of health and healthcare.

Two Main Approaches Taken by the AHRQ Enhanced State Data Grantees

Each project undertaken by the grantees from California, New Mexico, and the Northwest region to improve R/E/L data had different planning requirements based on the size of the state, the scope of the project, state policies, laws and procedures, and many other factors.

The three grantees used different combinations of activities to achieve their unique goals, but all their activities fall into two main approaches:

Impacted by several state and national regulatory requirements, investigators from California aimed to improve the reliability, validity, and completeness of self-reported R/E/L data collected by hospitals. Policies related to the translation of data collection documents, utilization of U.S. Census categories (ZIP code, track, and block-group level), and implementation of needs assessments influenced the investigators’ data collection approaches to promote and standardize data.

Investigators from New Mexico approached their project in accordance with their state requirements for collecting, disseminating, and analyzing data to influence planning and policy development. Surveys were implemented to evaluate patient experiences, as well as hospital staff knowledge and attitudes regarding the collection of R/E/L data.

The Northwest region grantee conducted probabilistic record linkages with various public health datasets to evaluate and correct racial misclassification and to improve disease estimates as part of ongoing efforts to eliminate health disparities experienced by the American Indian/Alaska Native (AI/AN) population in the Northwest.

Challenges to Enhanced Data Collection

Stakeholder buy-in is a critical component of efforts aimed at improving R/E/L data. By establishing the importance and rationale for such interventions, individuals are more willing to commit. When initiating a project for data enhancement, organizations wishing to add race/ethnicity data elements to their administrative databases have to recruit stakeholders by building a strong business case for the project, developing a plan for data collection and management (including the identification of appropriate resources both in terms of staff as well as technology and external vendors), educating staff about the project, and obtaining the necessary data sharing agreements with project partners.

State and local health departments, hospital staff, healthcare organizations, researchers, policy makers, funders, and community members play a major role in efforts to improve R/E/L data. While involvement of multiple stakeholders can improve the sustainability of data improvement efforts, it also poses significant challenges due to varied approaches and standards for data collection, classification, and reporting. Facilitating discussions among these players and providing training on the importance for improving data can alleviate the barriers.

While many healthcare organizations report information on their patients’ R/E/L characteristics, the data may have problems in accuracy and completeness. When collected, R/E/L data may be based on unreliable sources such as assumptions made by hospital intake staff based on a patient’s appearance, people accompanying the patient, or information imported from earlier medical records.

Training and educational resources such as FAQs, staff scripts, and questionnaires are mechanisms utilized by investigators from California to improve data collection efforts by hospital staff. Investigators from New Mexico and California developed surveys of hospital staff and patients to evaluate factors that influence and impede data collection efforts and to better understand the patient perspective in providing race/ethnicity/tribal identification information.

In addition, although the need for data to track these disparities and develop effective programs to reduce and eliminate them is evident to policymakers, researchers, and healthcare advocates; the need may not be as evident to those who deliver healthcare, nor to those who receive it.

Challenges for Hospitals

Securing Organizational Buy-In
In light of federal and state regulations mandating the collection and reporting of R/E/L data, many hospitals across the country are engaged in a variety of efforts to improve their collection of these data. However, hospital leadership may question whether committing resources to improved R/E/L collection will be worth the effort. To ensure the success of these efforts, there needs to be organizational buy-in from hospital leadership, specifically regarding data collection efforts. Implementing new data collection systems or modifying current ones takes time and resources, and requires a strong commitment from hospital leaders to moving the process forward in the face of competing priorities.

Research indicates that racial and ethnic disparities in healthcare delivery have an impact on cost, quality, patient safety, and risk management. Hospitals that collect R/E/L data accurately and in a standard manner can then use these data to stratify quality measures, develop and expand culturally competent interventions, and improve care delivery to the communities they serve.

The following tools explain why hospitals should consider undertaking a data collection improvement project:

Understanding Drivers of Health Care Disparities and Developing Targeted Interventions (PDF file, 522 KB; HTML)
A presentation that discusses why disparities in healthcare matter, the intersection of quality and disparities, R/E/L data collection, and interventions. (Developed by the California grantee)

Why Should Hospitals Collect R/E/L Data? (PDF file, 26 KB; HTML)
Targeted at hospital executives and upper and middle managers, this document outlines the purposes and legal justification for collecting patient R/E/L information, and details initiatives and legislation (both enacted and proposed) that aim to promote and standardize data collection. (Developed by the California grantee)

Securing Staff Buy-In
Registration and admitting departments who must already stretch scarce resources and deal with staff turnover may feel overburdened by a new data collection requirement. IT departments must address the technical hurdles of accommodating expanded data definitions and possibly providing electronic scripts and data collection screens to facilitate the collection of the data.

The following overviews of grantees’ projects provide hospital staff with the clear understanding of the purpose and importance of R/E/L data collection they need in order to commit to full participation:

Race, Ethnicity, and Language Data Collection: Nuts and Bolts (PDF file, 180 KB; HTML)
A presentation on training staff and informing leadership about the importance of effectively collecting high quality R/E/L data that meet state requirements and can be used to improve quality in hospitals.(Developed by the California grantee)

Collection and Use of Race and Ethnicity Data for Discharge Data Reporting Systems: The New Mexico Race/Ethnicity Data Project (PDF file, 1.4 MB; HTML)

A presentation describing the purpose of the project, the NM Hospital Inpatient Discharge Database (HIDD), data collection guidelines, changes to NMAC policy, hospital training and evaluation, and data collection, analysis, and reporting. (Developed by the New Mexico grantee)

Challenges for Patients

Many patients may be reluctant to provide identifying information about their race, ethnicity, or primary language without a clear understanding of how this information will be used to enhance their overall healthcare experience. Studies have shown that patients are more willing to share information on R/E/L if they understand why these data are being collected and how the information will be used.

Educational outreach materials explaining the rationale behind R/E/L data collection should be distributed to patients at the time of admission. Registration and admitting staff, as well as other healthcare personnel should be trained to answer patients’ questions about the data collection process, and to reassure patients that their R/E/L information will not be used for any other purpose besides improving healthcare services and programs for patients.

Educating Patients
FAQs about the Collection of Patient Race, Ethnicity, and Language—For Patients (PDF file, 122 KB; HTML)
Provides answers to questions that are frequently asked by patients during the admission/registration process. Hospitals can attach this list to registration forms, offer it as a handout to patients, or post it in the waiting area. (Developed by the California grantee)

Help Us Know Our Patients Better (PDF file, 95 KB; HTML)
A one-page flyer to educate patients about the importance of R/E/L data collection and to clarify the vocabulary for race, ethnicity, and tribal classification. (Developed by the New Mexico grantee)


Internet Citation: Race and Ethnicity Data Improvement Toolkit. Healthcare Cost and Utilization Project (HCUP). July 2016. Agency for Healthcare Research and Quality, Rockville, MD.
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Last modified 7/28/16