Leveraging implementation science and quality improvement to ensure high-quality cancer care: An analysis of quality certification projects at an academic medical center.

459 Background: Quality improvement (QI) addresses local quality gaps to optimize care delivery, while implementation science (IS) seeks to increase adoption of evidence-based practices (EBPs) to close research-practice gaps and improve population health. Identifying opportunities for QI-IS synergy can foster collaboration and ensure attention to rapid and sustainable improvements in evidence-based care. Our objectives were to 1) assess how QI projects submitted for silver or gold certification in our academic medical center addressed evidence-based gaps in cancer care and 2) consider how their methods align with IS approaches. Methods: The QI project database was keyword searched to identify proposals in cancer. Data including detailed description of the quality gap and methods to identify and evaluate interventions were abstracted and entered in a database organized by the DMAIC framework (Define, Measure, Analyze, Improve, and Control). Two investigators reviewed data to identify EBPs and describe evidence using a typology for IS (Type 1 etiology and burden, Type 2 intervention effectiveness, and Type 3 dissemination and implementation in context). Data in DMAIC domains were mapped to relevant IS constructs for side-by-side comparison. Results: The search identified 81 proposals submitted between May 2017 and April 2025, of which 62 targeted cancer-related care gaps (5.2% of applications). Fourteen referenced published EBPs, including American Society for Clinical Oncology or National Comprehensive Cancer Network guidelines and state or federal quality metrics on cancer screening or evaluations for diagnosis and treatment. An additional 17 addressed processes relevant to EBPs, e.g., screening documentation. QI tools like cause-and-effect diagrams identified multilevel causes, but few proposals deployed multilevel interventions (strategies in IS), and none referenced theory. Reported “lessons learned” further detailed unanticipated patient, care team, and system-level challenges. Most (n=24, 77%) of EBP-related proposals addressed Type 2 evidence (e.g., performance, quality, efficiency) but could have also been framed as Type 3 evidence on implementation context/strategies targeting implementation outcomes (e.g., clinician adoption, patient reach, implementation fidelity). Conclusions: Although focused on local improvement, about 1 in every 20 QI proposals addressed implementation of cancer EBPs. Framing QI purpose as addressing Type 3 evidence could help link local QI projects to IS theory and methods to inform strategy selection and evaluation of implementation outcomes that precede service-level outcomes. Organizations that combine rapid-cycle QI improvements and rigorous IS methodologies may more effectively close care gaps and tackle persistent healthcare challenges.