Insights into measuring health disparities using electronic health records from a statewide network of health systems: A case study

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.

[1]  Maureen A. Smith,et al.  Using Statewide Electronic Health Record and Influenza Vaccination Data to Plan and Prioritize COVID-19 Vaccine Outreach and Communications in Wisconsin Communities. , 2021, American journal of public health.

[2]  Maureen A. Smith,et al.  Identifying Substantial Racial and Ethnic Disparities in Health Outcomes and Care in Wisconsin Using Electronic Health Record Data. , 2021, WMJ : official publication of the State Medical Society of Wisconsin.

[3]  Andrea Martani,et al.  Factors influencing harmonized health data collection, sharing and linkage in Denmark and Switzerland: A systematic review , 2019, PloS one.

[4]  George Hripcsak,et al.  Challenges with quality of race and ethnicity data in observational databases , 2019, J. Am. Medical Informatics Assoc..

[5]  Stephen B. Johnson,et al.  Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation , 2019, J. Am. Medical Informatics Assoc..

[6]  William R. Buckingham,et al.  Making Neighborhood-Disadvantage Metrics Accessible - The Neighborhood Atlas. , 2018, The New England journal of medicine.

[7]  R. Kang,et al.  Lessons learned about advancing healthcare equity from the Aligning Forces for Quality initiative. , 2016, The American journal of managed care.

[8]  Julia Adler-Milstein,et al.  Using health information exchanges to calculate clinical quality measures: A study of barriers and facilitators. , 2016, Healthcare.

[9]  K. Fiscella,et al.  Racial and Ethnic Disparities in the Quality of Health Care. , 2016, Annual review of public health.

[10]  L. Casalino,et al.  US Physician Practices Spend More Than $15.4 Billion Annually To Report Quality Measures. , 2016, Health affairs.

[11]  S Leatherman,et al.  The evolution of healthcare quality measurement in the United States , 2016, Journal of internal medicine.

[12]  Menggang Yu,et al.  Neighborhood Socioeconomic Disadvantage and 30-Day Rehospitalization , 2014, Annals of Internal Medicine.

[13]  D. Gaskin,et al.  The quality of care delivered to patients within the same hospital varies by insurance type. , 2013, Health affairs.

[14]  Maureen A. Smith,et al.  Publicly reported quality-of-care measures influenced Wisconsin physician groups to improve performance. , 2013, Health affairs.

[15]  Maureen A. Smith,et al.  Public reporting helped drive quality improvement in outpatient diabetes care among Wisconsin physician groups. , 2012, Health affairs.

[16]  Jason Wang,et al.  Validity of electronic health record-derived quality measurement for performance monitoring , 2012, J. Am. Medical Informatics Assoc..

[17]  S. Rathore,et al.  Variations in the Use of an Innovative Technology by Payer: The Case of Drug-Eluting Stents , 2012, Medical care.

[18]  Mark McClellan,et al.  Measuring health care performance now, not tomorrow: essential steps to support effective health reform. , 2011, Health affairs.

[19]  Stephen B. Thomas,et al.  Toward a fourth generation of disparities research to achieve health equity. , 2011, Annual review of public health.

[20]  Richard J. T. Klein,et al.  Data and measurement issues in the analysis of health disparities. , 2010, Health Services Research.

[21]  K. Fiscella,et al.  Reducing Disparities Downstream: Prospects and Challenges , 2008, Journal of General Internal Medicine.

[22]  Jennifer Rankin THE MULTIPLE LOCATION TIME WEIGHTED INDEX: USING PATIENT ACTIVITY SPACES TO CALCULATE PRIMARY CARE SERVICE AREAS , 2008 .

[23]  Carol M. Mangione,et al.  Disparities in health and health care , 2001, Journal of General Internal Medicine.

[24]  B. Starfield,et al.  Contribution of primary care to health systems and health. , 2005, The Milbank quarterly.

[25]  M. Hatahet,et al.  Wisconsin Collaborative for Healthcare Quality (WCHQ): lessons learned. , 2004, WMJ : official publication of the State Medical Society of Wisconsin.

[26]  A. Nelson Unequal treatment: report of the Institute of Medicine on racial and ethnic disparities in healthcare. , 2003, The Annals of thoracic surgery.

[27]  R. Phillips,et al.  Receipt of preventive care among adults: insurance status and usual source of care. , 2003, American journal of public health.