Improving Hospital Reporting of Patient Race and Ethnicity-Approaches to Data Auditing (original) (raw)
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Enhancing Public Hospitals' Reporting of Data on Racial and Ethnic Disparities in Care
To assess the ability of hospitals with large minority populations to use existing quality-of-care measures to reduce racial/ethnic disparities, the researchers analyzed quality-related data on acute myocardial infarction, heart failure, and pneumonia by patients' race and ethnicity from five major public hospitals. Senior clinical and administrative leaders were interviewed about their use of quality data and views on disparities and public data reporting. These hospitals exceeded national norms on most measures, and high performance was mostly consistent across racial and ethnic groups. While the findings should be interpreted cautiously, the data indicated some disparities in performance measures related to patient communication. The study also revealed limitations in use of commonly accepted quality measures for detecting disparities. None of the study hospitals had previously looked at these measures by race or ethnicity, and hospital leaders were not in agreement as to wh...
Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language
International Journal of Environmental Research and Public Health, 2015
Objective: Determine any disparities in care based on race, ethnicity and language (REaL) by utilizing inpatient (IP) core measures at Texas Health Resources, a large, faith-based, non-profit health care delivery system located in a large, ethnically diverse metropolitan area in Texas. These measures, which were established by the U.S. Centers for Medicare and Medicaid Services (CMS) and The Joint Commission (TJC), help to ensure better accountability for patient outcomes throughout the U.S. health care system. Methods: Sample analysis to understand the architecture of race, ethnicity and language (REaL) variables within the Texas Health clinical database, followed by development of the logic, method and framework for isolating populations and evaluating disparities by race (non-Hispanic White, non-Hispanic Black, Native American/Native Hawaiian/Pacific Islander, Asian and Other); ethnicity (Hispanic and non-Hispanic); and preferred language (English and Spanish). The study is based on use of existing clinical data for four inpatient (IP) core measures: Acute Myocardial Infarction (AMI), Congestive Heart Failure (CHF), Pneumonia (PN) and Surgical Care (SCIP), representing 100% of the sample population. These comprise a high number of cases presenting in our acute care facilities. Findings are based on a sample of clinical data (N = 19,873 cases) for the four inpatient (IP) core measures derived from 13 of Texas Health's wholly-owned facilities, formulating a set of baseline data. Results: Based on applied method, Texas Health facilities consistently scored high with no discernable race, ethnicity and language (REaL) disparities as evidenced by a low percentage difference to the reference point (non-Hispanic White) on IP core measures, including: AMI (0.3%-1.2%), CHF (0.7%-3.0%), PN (0.5%-3.7%), and SCIP (0-0.7%).
BMC Health Services Research, 2010
Background: To understand racial and ethnic disparities in health care utilization and their potential underlying causes, valid information on race and ethnicity is necessary. However, the validity of pediatric race and ethnicity information in administrative records from large integrated health care systems using electronic medical records is largely unknown. Methods: Information on race and ethnicity of 325,810 children born between 1998-2008 was extracted from health plan administrative records and compared to birth certificate records. Positive predictive values (PPV) were calculated for correct classification of race and ethnicity in administrative records compared to birth certificate records. Results: Misclassification of ethnicity and race in administrative records occurred in 23.1% and 33.6% children, respectively; the majority due to missing ethnicity (48.3%) and race (40.9%) information. Misclassification was most common in children of minority groups. PPV for White, Black, Asian/Pacific Islander, American Indian/Alaskan Native, multiple and other was 89.3%, 86.6%, 73.8%, 18.2%, 51.8% and 1.2%, respectively. PPV for Hispanic ethnicity was 95.6%. Racial and ethnic information improved with increasing number of medical visits. Subgroup analyses comparing racial classification between non-Hispanics and Hispanics showed White, Black and Asian race was more accurate among non-Hispanics than Hispanics. Conclusions: In children, race and ethnicity information from administrative records has significant limitations in accurately identifying small minority groups. These results suggest that the quality of racial information obtained from administrative records may benefit from additional supplementation by birth certificate data.
Race and Ethnicity: Comparing Medical Records to Self-Reports
Journal of the National Cancer Institute Monographs, 2005
Understanding and eliminating health disparities requires accurate data on race/ethnicity. To assess the quality of race/ethnicity data, we compared medical record classifi cations to self-report of a study of prophylactic mastectomy. A total of 788 women had race/ethnicity from both sources; 69.9% were 55 years of age or older, 38.3% were at least college graduates, and 67.8% were married or living with someone. There were 817 race/ thnicity classifi cations for the 788 women, of which 758 (92.3%) were identical in the medical record and self-report. Sensitivity and positive predictive value were high (86.7% -97.2%) for whites, Asians, and blacks and moderate (64.0% and 68.1%) for Latinas. However, only one of 18 Native Americans was correctly identifi ed in her medical record. Our results indicate that even if the overall accuracy of medical record classifi cations for race/ethnicity is high, such a fi nding may obscure substantial inaccuracies in the recording for racial/ ethnic minorities, especially Latinas and Native Americans. [J Natl Cancer Inst Monogr 2005;35:72 -4]
Perspectives in health information management, 2016
Well-designed electronic health records (EHRs) must integrate a variety of accurate information to support efforts to improve quality of care, particularly equity-in-care initiatives. This case study provides insight into the challenges those initiatives may face in collecting accurate race, ethnicity, and language (REAL) information in the EHR. We present the experience of an academic medical center strengthening its EHR for better collection of REAL data with funding from the EHR Incentive Programs for meaningful use of health information technology and the Texas Medicaid 1115 Waiver program. We also present a plan to address some of the challenges that arose during the course of the project. Our experience at an academic medical center can provide guidance about the likely challenges similar institutions may expect when they implement new initiatives to collect REAL data, particularly challenges regarding scope, personnel, and other resource needs.
Journal of Racial and Ethnic Health Disparities, 2016
Background Data collection on race and ethnicity is critical in the assessment of racial disparities related to health. Studies comparing clinical and administrative data show discrepancies in race documentation and attribution. Methods Self-reported data from two studies were compared to demographics in the electronic health record (EHR) extracted from the Biomedical Translational Research Information System (BTRIS) repository. McNemar and Bhapkar analyses were conducted to quantify the agreement of ethnicity and race between self-reported and EHR data. Pearson's chisquare tests were used to explore the relationship between acculturation, length of time in the USA, country of residence, and how individuals self-reported their race. Results The sample (n = 280) was predominantly female (52.1 %), with a mean age of 47 (SD ± 13.74), mean years in the USA were 12.8 (SD ± 11.67) and the majority were born outside of the USA. (55.6 %). Those who self-identified as Hispanic (n = 208) scored a mean of 5.5 (SD ± 3.07) on the short acculturation scale (SAS) that ranges 4 to 20; lower scores indicate less acculturation. A significant difference was found between the way race is reported in the electronic medical record and self-reported data among those people who identified as Hispanic, with significant differences in the white (p < 0.0001) and other (p < 0.0001) categories. Conclusions The misclassification of race is most frequent in those individuals who self-identified as Hispanic. As the Hispanic population in the USA continues to grow, understanding the factors that affect the way that individuals from this heterogeneous population self-report race may provide important guidance in tailoring care to address health disparities.
The Legality of Collecting and Disclosing Patient Race and Ethnicity Data
2006
This Policy Brief, prepared for the Robert Wood Johnson Foundation, analyzes the following question: Whether the collection of patient data by race or ethnicity, as part of a program of quality improvement, violates the law. 74 Id. at Section 2, adopting new Public Health Service Act § 921(4). 75 Id. at § 921(7)(A). 76 Id. at § 921(5). 77 Id. at § 922(a). 78 Id. at § 922(d).
Subjectively-Assigned versus self-reported race and ethnicity in US healthcare
Social Medicine, 2014
Documenting patient "race" descriptors in clinical medicine, epidemiology, and public health data and analysis has been routine in the US. However, patient race has historically been and is still most often subjectively-assigned rather than selfidentified. Even when self-identification is allowed, persons must often self-deny parts of their ancestry by adhering to restrictive race categories. In contrast, most other countries ignore so-called race and may use other ancestral background information including family and geographical histories, language(s) and/or ethnic group(s) membership. We performed two studies involving 160 patients to investigate subjectively-assigned versus selfreported race using a verbal questionnaire in a New Orleans medical clinic. Results revealed that the subjectively-assigned race recorded by the hospital administration/physician was incomplete and therefore inaccurate. Clinicians and researchers must make more accurate and respectful ancestral inquiries in order to derive useful information about individual and population health risks and disease conditions, while also being mindful of potentially erroneous race data previously gathered and conclusions inferred in healthcare literature.