Objective: This study examined whether health insurance stability was associated with improved type 2 diabetes mellitus (DM) control and reduced racial/ethnic health disparities.Methods: We utilized electronic medical record data (2005-2013) from two large, urban academic health systems with a racially/ethnically diverse patient popula­tion to examine insurance coverage, and three DM outcomes (poor diabetes control, A1c ≥8.0%; very poor diabetes control A1c >9.0%; and poor BP control, ≥ 130/80 mm Hg) and one DM management outcome (A1c monitoring). We used generalized estimating equations adjusting for age, sex, comorbidities, site of care, education, and income. Additional analysis examined if insurance stability (stable public or private insurance over the six-month internal) moderates the impact of race/ethnicity on DM outcomes.Results: Nearly 50% of non-Hispanic (NH) Whites had private insurance cover­age, compared with 33.5% of NH Blacks, 31.5% of Asians, and 31.1% of Hispanics. Overall, and within most racial/ ethnic groups, insurance stability was associated with better glycemic control compared with those with insurance switches or always being uninsured, with uninsured NH Blacks having significantly worse BP control. More NH Black and Hispanic patients had poorly controlled (A1c≥8%) and very poorly controlled (A1c>9%) diabetes across all insurance stability types than NH Whites or Asians. The interaction between insurance instability and race/ethnic groups was statis­tically significant for A1c monitoring and BP control, but not for glycemic control.Conclusion: Stable insurance coverage was associated with improved DM outcomes for all racial / ethnic groups, but did not eliminate racial ethnic disparitiesEthn Dis. 2021;31(1):149-158; doi:10.18865/ed.31.1.149

and the Translating Research Into Practice (TRIP) Consortium BACKGROUND: Implementing city-wide patient navigation processes that support patients across the continuum of cancer care is impeded by a lack of standardized tools to integrate workflows and reduce gaps in care. The authors present an actionable workflow process mapping protocol for navigation process planning and improvement based on methods developed for the Translating Research Into Practice study. METHODS: Key stakeholders at each study site were identified through existing community partnerships, and data on each site's navigation processes were collected using mixed methods through a series of team meetings. The authors used Health Quality Ontario's Quality Improvement Guide, service design principles, and key stakeholder input to map the collected data onto a template structured according to the case-management model. RESULTS: Data collection and process mapping exercises resulted in a 10-step protocol that includes: 1) workflow mapping procedures to guide data collection on the series of activities performed by health care personnel that comprise a patient's navigation experience, 2) a site survey to assess program characteristics, 3) a semistructured interview guide to assess care coordination workflows, 4) a site-level swim lane workflow process mapping template, and 5) a regional high-level process mapping template to aggregate data from multiple site-level process maps. CONCLUSIONS: This iterative, participatory approach to data collection and process mapping can be used by improvement teams to streamline care coordination, ultimately improving the design and delivery of an evidence-based navigation model that spans multiple treatment modalities and multiple health systems in a metropolitan area. This protocol is presented as an actionable toolkit so the work may be replicated to support other qualityimprovement initiatives and efforts to design truly patient-centered breast cancer treatment experiences.

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