Improved Service Efficiency Improves Racial Disparity in Diabetic Care

Background: Racial disparities in diabetes care have been documented. Disparities also have been shown to affect service quality and outcome of diabetic care. Analysis of our internal medicine residency clinic diabetic care management performance across REAL-G (race, ethnicity, age, preferred language and/or gender) showed race-based disparity on two outcome measures: 1) measurement of glycohemoglobin (A1C) at least twice a year; and 2) target blood pressure of \u3c 140/90. Purpose: Develop interventions to decrease racial disparities in diabetes care among patients managed by an internal medicine residency clinic, as part of the Alliance of Independent Academic Medical Center’s National Initiative V project. Methods: Interventions were developed following analysis of clinic performance data by REAL-G categories, workflow analysis and multidisciplinary clinic team meetings. A point-of-care A1C machine was procured and workflow developed using the Plan-Do-Study-Act cycle. Staff training was conducted. A rolling 12 months data set was obtained from electronic health records. Baseline data range was December 2014 to November 2015, while endline data were from January 2016 to December 2016. The interventions were launched in July 2016. Percentage difference between baseline and endline outcome indicators was calculated and Z-score test assessed. Statistical significance was set at P \u3c 0.05. Results: At baseline, 62.9% (401 of 638) of patients who self-identified as African American/black (AA) had A1C measured at least twice a year compared to 74.3% (107 of 144) of patients who self-identified as white/Caucasian (WC), a percentage difference of 11.4% (P = 0.01). For goal blood pressure in diabetics, 71.0% (453 of 638) of AA met the target as compared to 80.6% (116 of 144) of WC, a percentage difference of 9.6% (P = 0.003). Following the intervention, a higher percentage of AA patients (71.4% [381 of 534]) had at least two A1C measured during project period. The outcome also showed improvement for WC (79.8% [95 of 119]). The percentage difference between races narrowed to 8.5% (P = 0.06). For goal blood pressure, 75.1% of AA achieved the target compared to 81.5% of WC, with percentage difference narrowing to 6.4% (P = 0.14). Conclusion: Racial disparities in diabetes were confirmed, even for a clinic setting in which black patients are predominant. Racial disparity can be improved by implementing interventions that improve service for all patients

Three Residency Programs’ Lessons Learned about Disparities from a Deep Dive into Our Population Data

Introduction/Background To deliver person-centric, best-in class health care we must transition to value-based care. As part of managing this transition, we must identify at risk populations – those with disparities in clinical measures - by leveraging our existing data sets to provide actionable data to inform how we manage these populations. Currently our health care system provides clinical quality metrics to support providers’ ability to engage in continuous improvement. This data is complimented by provider’s knowledge of the literature, which consistently identifies certain populations, often using the REAL-G categories, as at risk. For example, hypertension has well established risk factors including age, gender, and race: HTN increases through early middle age; women are more likely to develop HTN \u3e 65; HTN is more common among blacks. However, our current clinical quality data does not normally provide detailed clinical/service level population specific metrics (e.g., REAL-G specific data) limiting providers’ ability to understand the clinical quality disparities in their patient populations. Hypothesis/Aim Statement To identify actionable disparity gaps for quality improvement through detailed analysis of selected clinic level quality metrics by REAL-G Categories (Race, Ethnicity, Age, Language, Gender). Methods Three residency programs participating in the Alliance of Independent Academic Medical Center’s National Initiative V (AIAMC-NIV) identified a current system-wide quality metric that was not at/above system goal: Family Medicine - colorectal cancer (CRC) screening; Internal Medicine – diabetes; and Ob/Gyn - postpartum readmission for hypertension. Through a partnership between Graduate Medical Education (GME) and Service Quality leaders, a retrospective analysis of selected quality metrics was undertaken to determine if there were disparities using REAL-G categories over a 12-month period (12.2014-11.2015). Each residency team then reviewed the data to identify the largest disparities by REAL-G category for quality improvement. Results The largest disparities in our clinics/service areas were sometimes consistent with the literature (e.g., 65% of African American DM Patients \u3e HbA1cs compared to 70% of White-Hispanic and 71% White-Non Hispanic) but not always! For example the largest CRC screening disparity was not race, ethnicity or gender ( Conclusions Diving into our clinical quality metrics using REAL-G categories, provided the actionable data needed in each of our three residency programs to plan disparity targeted improvement cycles