Using Statewide Electronic Health Record and Influenza Vaccination Data to Plan and Prioritize COVID-19 Vaccine Outreach and Communications in Wisconsin Communities

Abstract

The University of Wisconsin Neighborhood Health Partnerships Program used electronic health record and influenza vaccination data to estimate COVID-19 relative mortality risk and potential barriers to vaccination in Wisconsin ZIP Code Tabulation Areas. Data visualization revealed four groupings to use in planning and prioritizing vaccine outreach and communication based on ZIP Code Tabulation Area characteristics. The program provided data, visualization, and guidance to health systems, health departments, nonprofits, and others to support planning targeted outreach approaches to increase COVID-19 vaccination uptake. (Am J Public Health. 2021;111(12):2111–2114. https://doi.org/10.2105/AJPH.2021.306524 )

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

“…All of the health systems submit ZIP code information to the Collaborative data repository but none currently submit residential address information. As a result, the Collaborative has initially focused on rural-urban geodisparity based on ZIP codes, 21 with a longer-term plan to extract residential address and calculate neighborhood disadvantage.…”

“…Geography including rural-urban residence and neighborhood disadvantage has been linked to disparities in the quality of care [19,20]. Our prior work in building a rural-urban geodisparity model has shown that disparities exist within Wisconsin in both rural and urban underserved areas [21], and that, at a national level, neighborhood disadvantage is strongly related to rehospitalization rates [22]. Both ZIP code (needed for rural-urban geodisparity) and address (needed for neighborhood disadvantage) are collected by the Collaborative health systems in their EHRs, but only ZIP code was available in the Collaborative data repository.…”

“…All of the health systems submit ZIP code information to the Collaborative data repository but none currently submit residential address information. As a result, the Collaborative has initially focused on rural-urban geodisparity based on ZIP codes [21], with a longer-term plan to extract residential address and calculate neighborhood disadvantage. The Collaborative staff worked with health systems to submit complete five-digit ZIP codes to geocode to rural-urban geodisparity categories (available at https://www.hipxchange.org/ RuralUrbanGroups).…”

Assessing Primary Care Clinic Operational Strategies via EMR Data Mining

“…Likewise, using EMR data for health care performance management is a familiar research topic. The central theme in this area centers on using EMR data as a useful data source for analysis to measure and improve the quality of care, patient conditions, and even disease spread, as evidenced during the recent pandemic (Barbazza et al, 2021; Bonham-Werling et al, 2021). Jaakkimainen et al (2014) investigate another theme is using EMR data to study patient wait time between primary care and specialty care visits to analyze health care access issues.…”

Effect of health system on the association of rurality and level of disadvantage with receipt of diabetic eye screening

“…However, binary classification of areas as either rural or urban may be overly simplistic and obscure variable levels of health insurance, healthcare access, education, income, and health status that contribute significantly to patient outcomes. Previous reports have demonstrated the need for more nuanced classification of rurality informed by social determinants of and barriers to healthcare access 22 23. Thus, the Wisconsin Collaborative for Healthcare Quality (WCHQ), in collaboration with the University of Wisconsin Health Innovation Program, developed six groups or geolocations to better describe the healthcare access and socioeconomic characteristics of rural and urban zip codes across the state of Wisconsin: (1) urban advantaged, (2) urban, (3) urban underserved, (4) rural advantaged, (5) rural, and (6) rural underserved 22 24.…”