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Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language

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 measure...

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Autores principales: Gracia, Amber, Cheirif, Jorge, Veliz, Juana, Reyna, Melissa, Vecchio, Mara, Aryal, Subhash
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4730436/
https://www.ncbi.nlm.nih.gov/pubmed/26703665
http://dx.doi.org/10.3390/ijerph13010045
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author Gracia, Amber
Cheirif, Jorge
Veliz, Juana
Reyna, Melissa
Vecchio, Mara
Aryal, Subhash
author_facet Gracia, Amber
Cheirif, Jorge
Veliz, Juana
Reyna, Melissa
Vecchio, Mara
Aryal, Subhash
author_sort Gracia, Amber
collection PubMed
description 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%).
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spelling pubmed-47304362016-02-11 Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language Gracia, Amber Cheirif, Jorge Veliz, Juana Reyna, Melissa Vecchio, Mara Aryal, Subhash Int J Environ Res Public Health Article 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%). MDPI 2015-12-22 2016-01 /pmc/articles/PMC4730436/ /pubmed/26703665 http://dx.doi.org/10.3390/ijerph13010045 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gracia, Amber
Cheirif, Jorge
Veliz, Juana
Reyna, Melissa
Vecchio, Mara
Aryal, Subhash
Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language
title Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language
title_full Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language
title_fullStr Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language
title_full_unstemmed Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language
title_short Harnessing Data to Assess Equity of Care by Race, Ethnicity and Language
title_sort harnessing data to assess equity of care by race, ethnicity and language
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4730436/
https://www.ncbi.nlm.nih.gov/pubmed/26703665
http://dx.doi.org/10.3390/ijerph13010045
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