Achieving Large-Scale Quality Improvement in Primary Care Annual Wellness Visits and Hierarchical Condition Coding.

BACKGROUND: Completion of Medicare Annual Wellness Visits (AWV) and documentation of Hierarchical Condition Categories (HCC) are important metrics in accountable care organizations (ACO) with quality and financial implications. To improve performance in large healthcare organizations, quality improvement (QI) efforts need to be scaled up in a way that is feasible within available system-wide resources. OBJECTIVE: We describe a 3-year effort using a multifaceted QI framework called the fractal management system for AWV and HCC performance. DESIGN: Pre-post evaluation of a multi-level, health system-wide QI management system intervention between 2018 and 2020. The system provided project management, coaching, communications, feedback of performance, and health informatics. PARTICIPANTS: The intervention was delivered to all 97 primary care practices within an Ohio-based accountable care organization, comprising 72,603 attributed Medicare and Medicare Advantage patients as of 2018. Eighty-nine of these practices were included in the analysis. APPROACH: AWV completion was defined as percent of eligible patients with a documented AWV during the calendar year. HCC completion was defined as documented reassessment of all prior-year HCC conditions. KEY RESULTS: AWV completion at the practice level increased from 23.7% (SD .14) in 2018 to 34.9% (SD .18) in 2019, and 59.8% (SD .17) in 2020. This was a statistically significant effect of time on AWV completion rates overall (F[2, 87] = 164.43, p < .000). More than half (56.2%) of practices met or exceeded the 60% goal in 2020. Practice-level HCC completion tracking started in 2019 (M = 75.9%, SD 7.4%) and increased in 2020 (M = 79.7%, SD 7.1%); t(172) = 2.0, p < .001. CONCLUSIONS: AWV and HCC performance goals were met in 2020, despite service disruptions due to COVID-19. The QI approach we used is applicable to other problems and other large healthcare systems.

Redesigning Kidney Disease Care to Improve Value Delivery.

This article describes the articulation, development, and deployment of a machine learning (ML) model-driven value solution for chronic kidney disease (CKD) in a health system. The ML model activated an electronic medical record (EMR) trigger that alerted CKD patients to seek primary care. Simultaneously, primary care physicians (PCPs) received an alert that a CKD patient needed an appointment. Using structured checklists, PCPs addressed and controlled comorbid conditions, reconciled drug dosing and choice to CKD stage, and ordered prespecified laboratory and imaging tests pertinent to CKD. After completion of checklist prescribed tasks, PCPs referred patients to nephrology. CKD patients had multiple comorbidities and ML recognition of CKD provided a facile insight into comorbid burden. Operational results of this program have exceeded expectations and the program is being expanded to the entire health system. This paradigm of ML-driven, checklist-enabled care can be used agnostic of EMR platform to deliver value in CKD through structured engagement of complexity in health systems.

Leading with love: learning and shared accountability.

PURPOSE: To describe peer learning and shared accountability and their use within our management system to improve the rate of patient annual wellness visits completed by primary care physicians. DESIGN/METHODOLOGY/APPROACH: Our management system implements programs to improve performance on a measure, initially declaring the goal, roles and responsibilities. In the illustrative case in this article, primary care physicians are assigned the goal of completing annual wellness visits for 65% of their patients by the end of 2021. To support physicians, peer learning networks are established, connecting teams, physicians and others to broadly share best practices and support better performance. Shared accountability means higher-level leaders in the organization need to first set lower-level leaders up to succeed before holding lower-level leaders accountable for achieving the declared goal. Our shared accountability model describes processes of the higher-level leader to ensure lower-leader success. The accountability process if a lower-level leader does not improve performance involves 3 steps: (1) a letter; (2) meeting with hospital executives for peer review; (3) review for sanctions/disciplinary action. FINDINGS: In quarter 1 of calendar year 2021, we identified 30 physicians that were behind pace for reaching the 65% goal of AWVs with patients for 2021 and also had not achieved the 2020 60% goal. After step 1, 22 of 30 (73%) physicians were on target for the goal. After step 2, 3 of 8 physicians were on target for the goal. ORIGINALITY/VALUE: Peer learning and shared accountability are underdeveloped in health care, and often viewed as at odds with each other. In our framework we integrate them. Thus, we formed learning networks, connecting every level of the organization and branching out across the health system to share ideas and build capability. Our shared accountability model removes the punitive connotation often connected to accountability by aligning higher and lower-level leaders to work together as a team. This model is improving personal performance among primary care physicians, and now being used for all quality and value efforts in our health system. We believe if broadly applied, this model could help improve value in health care.