OBJECTIVES: It is essential to engage learners in efforts aimed at dismantling racism and other contributors to health care disparities. Barriers to their involvement include limited access to data. The objective of our study was to create a data dashboard using an existing quality improvement (QI) infrastructure and provide resident access to data to facilitate exploratory analysis on disparities in emergency department (ED) patient care. METHODS: Focusing on patient populations that have previously been shown in the literature to suffer significant disparities in the ED, we extracted outcomes across a variety of metrics already collected as part of routine ED operations. Using data visualization software, we developed an interactive dashboard for visual exploratory analyses. RESULTS: We designed a dashboard for our resident learners with views that are flexible and allow user selected filters to view clinical outcomes by patient age, treatment area, and chief complaint. Learners were also allowed to select grouping and outcomes of interest to investigate questions and form new hypotheses of their choosing. Available dashboard views included summary counts view to assess ED visits over time by selectable group, a rooming and triage acuity view, time-to-event survival curve view, histogram and box plot views for continuous variables, a view to assess outcome variables by time of day of ED arrival, customizable contingency table views, and correspondence analysis. CONCLUSIONS: Utilizing an existing QI infrastructure, we developed a dashboard that provides a new perspective into commonly collected ED operations data to allow for the exploration of disparities in ED care that is accessible to learners. Future directions include using these data to refine hypotheses on ED disparities, understand root causes, develop interventions, and measure their impact.
OBJECTIVES: It is essential to engage learners in efforts aimed at dismantling racism and other contributors to health care disparities. Barriers to their involvement include limited access to data. The objective of our study was to create a data dashboard using an existing quality improvement (QI) infrastructure and provide resident access to data to facilitate exploratory analysis on disparities in emergency department (ED) patient care. METHODS: Focusing on patient populations that have previously been shown in the literature to suffer significant disparities in the ED, we extracted outcomes across a variety of metrics already collected as part of routine ED operations. Using data visualization software, we developed an interactive dashboard for visual exploratory analyses. RESULTS: We designed a dashboard for our resident learners with views that are flexible and allow user selected filters to view clinical outcomes by patient age, treatment area, and chief complaint. Learners were also allowed to select grouping and outcomes of interest to investigate questions and form new hypotheses of their choosing. Available dashboard views included summary counts view to assess ED visits over time by selectable group, a rooming and triage acuity view, time-to-event survival curve view, histogram and box plot views for continuous variables, a view to assess outcome variables by time of day of ED arrival, customizable contingency table views, and correspondence analysis. CONCLUSIONS: Utilizing an existing QI infrastructure, we developed a dashboard that provides a new perspective into commonly collected ED operations data to allow for the exploration of disparities in ED care that is accessible to learners. Future directions include using these data to refine hypotheses on ED disparities, understand root causes, develop interventions, and measure their impact.
Authors: Monika K Goyal; Nathan Kuppermann; Sean D Cleary; Stephen J Teach; James M Chamberlain Journal: JAMA Pediatr Date: 2015-11 Impact factor: 16.193
Authors: Andrew P Wilper; Steffie Woolhandler; Karen E Lasser; Danny McCormick; Sarah L Cutrona; David H Bor; David U Himmelstein Journal: Health Aff (Millwood) Date: 2008-01-15 Impact factor: 6.301