Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language
AbstractObjective: 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%). View Full-Text
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Gracia, A.; Cheirif, J.; Veliz, J.; Reyna, M.; Vecchio, M.; Aryal, S. Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language. Int. J. Environ. Res. Public Health 2016, 13, 45.
Gracia A, Cheirif J, Veliz J, Reyna M, Vecchio M, Aryal S. Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language. International Journal of Environmental Research and Public Health. 2016; 13(1):45.Chicago/Turabian Style
Gracia, Amber; Cheirif, Jorge; Veliz, Juana; Reyna, Melissa; Vecchio, Mara; Aryal, Subhash. 2016. "Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language." Int. J. Environ. Res. Public Health 13, no. 1: 45.
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