Race, Ethnicity, and Language Data Standardization for Health Care Quality Improvement (2009) / Chapter Skim
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5 Improving Data Collection Across the Health Care System
5 Improving Data Collection Across the Health Care System
Pages 127-146
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From page 127... ...
, physician practices, health plans, and local, state, and federal agencies can all play key roles by incorporating race, ethnicity, and language data into existing data collection and quality reporting efforts, each faces opportunities and challenges in attempting to achieve this objective. To identify the next steps toward improving data collection, it is helpful to understand these opportunities and challenges in the context of current practices.
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From page 128... ...
This is followed by a review of methods that can be used to derive race and ethnicity data through indirect estimation when obtaining data directly from many patients or enrollees is not possible. COLLECTING AND SHARING DATA ACROSS THE HEALTH CARE SYSTEM Health care involves a diverse set of public and private data collection systems, including health surveys, administrative enrollment and billing records, and medical records, used by various entities, including hospitals, CHCs, physicians, and health plans.
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. Therefore it is not surprising that more than 89 percent of hospitals report collecting race and ethnicity data, and 79 percent report collecting data on primary language (AHA, 2008)
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and therefore are good settings for implementing quality improvement strategies aimed at reducing racial and ethnic disparities in care. Yet while CHCs serve diverse patient populations and, as organizations, understand the importance of demographic data for improving the quality of care, the accuracy of the race, ethnicity, and language data they collect may be limited (Maizlish and Herrera, 2006)
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From page 131... ...
race and ethnicity data beyond the basic OMB categories included in their national Uniform Data System (HRSA, 2009) .5 Like hospitals, CHCs face challenges to collecting data, such as the need to train staff, the need to modify existing HIT systems, and the need to ensure interoperability between the practice management systems where demographic data are collected and recorded and the EHR systems where the demographic data can be linked to clinical data for quality improvement purposes.
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For example, implementation required analyzing and redesigning hundreds of clinical workflow patterns in busy CHCs and developing the right strategies for training staff. Additionally, some CHCs were collecting race and ethnicity data using paper forms and then transferring the data first into practice management systems and then into EHR systems for linkage with quality data.
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Health Plans Health plans, including Medicaid managed care and Medicare Advantage plans, have the capabilities necessary to systematically compile and manage race, ethnicity, and language data, and thus have roles to play in quality improvement (Rosenthal et al., 2009)
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8 A study conducted by America's Health Insurance Plans (AHIP) found that 54 percent of plans collected race and ethnicity data, and 56 percent collected primary language data.
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While the use of racial, ethnic, and language identifiers for coverage, benefit determination, and underwriting is prohibited, the collection of these data for improving quality and reducing health care disparities is both permit ted and encouraged. Low participation by plan members in reporting race, ethnicity, and language data may be indicative of low trust of the industry (Coltin, 2009)
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. IMPROVING DATA COLLECTION PROCESSES The above discussion of challenges faced by various health and health care entities highlights how important it is for data capture and quality to overcome HIT constraints and minimize respondent and organizational resistance.
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Implementing Staff Training Staff of hospitals, physician practices, and health plans have expressed concern about asking patients, enrollees, or members to provide information about their race, ethnicity, and language need (Hasnain-Wynia, 2007)
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12 Personal communication, O Tiutin, Contra Costa Health Plan, July 10, 2009.
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This use of predictive variables rather than direct collection of information from patients is termed "indirect estimation." A number of indirect estimation approaches can be applied to race and ethnicity data, including linking area-level population data from the Census Bureau to quality data, using names for indirect estimation, and attributing Bayesian probabilities to indirectly estimated data. Linking Area-Level Data to Quality Data One of the simplest indirect approaches is to use area-level population data derived from the Census.
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From page 140... ...
HIPAA Privacy Rule requirements for deidentifying data protect individuals but may, in some cases, raise barriers to exchanging address data, as is sometimes necessary for indirect estimation processes. Using Names for Indirect Estimation Names have been used as indicators of racial and ethnic identity.
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. Using Indirectly Collected Data Indirect race and ethnicity identifications can be used in quality improvement efforts when direct identifica tions are unavailable (see Box 5-6)
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Instead, if indirect estimation of race and ethnicity is to be used, the estimated probabilities should be stored in a system that is distinct from medical records but can be merged with medical record data to create analytic files for identification of disparities. 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 de
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From page 143... ...
When direct collection is impossible or has not been completed, however, indirect approaches can be employed. These approaches include linking area-level population data from the Census to quality data, using data like names to infer race and ethnicity, and attributing Bayesian probabilities to indirectly estimated data.
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2008. Sustaining quality improvement in community health centers: Perceptions of leaders and staff.
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2009. Needed: National standardization of race/ethnicity data to address health disparities.
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2009. Applications of indirect estimation of race/ethnicity data in health plan activities.
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