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Insights into measuring health disparities using electronic health records from a statewide network of health systems: A case study.
Smith, Maureen A; Gigot, Matthew; Harburn, Abbey; Bednarz, Lauren; Curtis, Katherine; Mathew, Jomol; Farrar-Edwards, Dorothy.
Afiliação
  • Smith MA; Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Gigot M; Department of Family Medicine and Community Health, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Harburn A; Health Innovation Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Bednarz L; Wisconsin Collaborative for Healthcare Quality, Madison, WI, USA.
  • Curtis K; Wisconsin Collaborative for Healthcare Quality, Madison, WI, USA.
  • Mathew J; Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Farrar-Edwards D; Health Innovation Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
J Clin Transl Sci ; 7(1): e54, 2023.
Article em En | MEDLINE | ID: mdl-37008604
ABSTRACT
Within Wisconsin, our residents experience some of the worst health disparities in the nation. Public reporting on disparities in the quality of care is important to achieving accountability for reducing disparities over time and has been associated with improvements in care. Disparities reporting using statewide electronic health records (EHR) data would allow efficient and regular reporting, but there are significant challenges with missing data and data harmonization. We report our experience in creating a statewide, centralized EHR data repository to support health systems in reducing health disparities through public reporting. We partnered with the Wisconsin Collaborative for Healthcare Quality (the "Collaborative"), which houses patient-level EHR data from 25 health systems including validated metrics of healthcare quality. We undertook a detailed assessment of potential disparity indicators (race and ethnicity, insurance status and type, and geographic disparity). Challenges for each indicator are described, with solutions encompassing internal (health system) harmonization, central (Collaborative) harmonization, and centralized data processing. Key lessons include engaging health systems in identifying disparity indicators, aligning with system priorities, measuring indicators already collected in the EHR to minimize burden, and facilitating workgroups with health systems to build relationships, improve data collection, and develop initiatives to address disparities in healthcare.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos