Preventing Postpartum Readmissions for Hypertension

Molly K Lepic; Sara M O’Meara; Carla J Kelly; Rebecca Eberhardt; Deborah Simpson; Jeffrey Stearns

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PROJECT MANAGEMENT PLAN – From Population Data to Patient: Analyzing Clinical Quality Data Using REAL-G Categories to Design Clinical Unit–Based Strategies for Intervention

Vision Statement	Aurora Health Care (AHC) aspires to provide ALL people better healthcare than they can get anywhere else. Our vision is to improve healthcare through engaging residents and faculty in identifying and addressing disparities in clinical quality metrics, creating a win-win for patients and providers.
Team Objectives	Our objectives were as follows: Use existing clinical quality data to identify healthcare disparities using REAL-G (race, ethnicity, age, language, gender) categories Design and implement evidence-based strategies to address disparity gaps in targeted clinical settings (resident clinic and postpartum obstetrics) through partnerships among residents, faculty, caregivers, Aurora data management individuals, patients, and communities Share our processes and outcomes within the AHC community to sustain/spread our initiatives
Success Factors	The most successful part of our work was addressing clinically important hospital/clinic performance needs (eg, postpartum hypertension readmissions). We also framed disparities in terms of REAL-G by making disparities clinically relevant to providers who work on a daily basis with the underserved as well as to quality/care management leaders. We were among the earliest adopters of REAL-G data analysis to inform quality improvement initiatives for patients served by our residency programs. We served as pilots for system partners to explore how to analyze REAL-G data and educate physicians on diversity and inclusion, etc. We also demonstrated the value of integrating residents with their respective healthcare teams to actively engage in process improvement, resulting in better care. We were inspired by the following: Collaboration with residents, faculty, and clinic/hospital staff to implement change to improve care for our target disparity population (and all eligible patients as a whole): “We moved a metric that had been static for years.” The passion and commitment of the healthcare team members The power of teamwork and bridging the gap between resident and clinic staff: “It’s as close as we’ve gotten of bridging our patient care and resident education–and impact.” The great projects and passion of all the projects presented by other teams at the AIAMC meetings and the passion all the teams had for making a difference: “It was really cool.”
Barriers	The largest barrier encountered was the use of REAL-G data to frame our work—from engaging data analysts and teams to implementation—which added time and complexity to the normal change processes. We worked to overcome this challenge by patience, persistence, strong and visible C-suite support for the project, new partnership, expanding involvement of the clinical care/project team(s), and humor.
Lessons Learned	Paradigm Shift: With each project, residents, faculty, and staff came to the realization that a new engagement between residency education and clinical change is needed. We are shifting the paradigm to recognize that the knowledge and skills for process change are critical to improving care of patients and populations. Protected Time: Formalize time involved in the project with acceptance by faculty/attendings (eg, protected/block time) and to be firmer with the team’s time commitments and timelines. Increase Clinic Engagement: Push harder to engage a larger group with a clear delineation of roles, expectations, and accountabilities. We also need to increase involvement of the core team to draw on residents/faculty and clinic staff (eg, the operations staff who were at the pilot clinic such as the clinic medical director and supervisor of clinical operations). Education: Increase the curricular emphasis/formal education for residents with ongoing reeducation. Data: Increase the ability to access data in a format that supports analysis at the system level with data analyst support to more agilely answer emerging questions.