The healthcare industry is undergoing a significant transformation towards Digital Quality Measurement (dQM), moving from traditional methods to leveraging structured electronic health data for more timely, granular, and actionable quality insights. This session will explore the innovative application of Clinical Knowledge Graphs (CKGs) in informing the NCQA’s de novo measure development, to accelerate the design and management of these dQMs. CKGs represent medical information as interconnected entities, integrating heterogeneous clinical data with standard terminologies, enabling powerful queries and analytics. They serve as a human curated computable knowledge base for evidence-based digital measure specification. The presenters will describe how CKGs, such as the BMJ Clinical Intelligence Knowledge Graph, can support the creation and updating of evidence-based clinical content at scale, leveraging their computable structure for defining de novo measure development, and secondarily dQMs. BMJ has expertise in translating clinical guidelines into computable evidence. The session will highlight the crucial role of standardized data models like HL7 FHIR in enabling dQM by providing a consistent framework for representing and exchanging healthcare information. Participants will gain an understanding of how Clinical Quality Language (CQL) can be integrated with knowledge graph structures to express measure logic and ensure compatibility with FHIR. At the conclusion of the pilot, NCQA aims to determine whether the BMJ Knowledge Graph/Clinical Intelligence can effectively support the development of actionable knowledge. The proof of concept will involve type 2 diabetes in adults, demonstrating its connections to the CKM population, ultimately resulting in the creation of a knowledge graph. NCQA will then evaluate this knowledge graph to ensure alignment with internal measure development strategies. Pilot Objectives: • Interrogation of the Knowledge Graph: NCQA knowledge engineers will effectively query the BMJ knowledge graph with questions that reflect the requirements needed to support the new measure development. • Indexing FHIR CPG Resources: NCQA knowledge engineers will successfully utilize the knowledge graph to index existing FHIR Clinical Practice Guidelines (CPG) resources and CQL expressions by linking them to relevant clinical recommendations and foundational guidelines. • Data Extraction for CPG and CQL Construction: NCQA knowledge engineers will extract data from the knowledge graph to construct FHIR CPG and CQL expression scaffolding or to validate existing CPG and CQL resources. • Management of FHIR CPG Resources: NCQA knowledge engineers will effectively navigate, manage, and repurpose FHIR CPG resources and CQL expressions within related quality measures that address overlapping conditions in the CKM cluster. We will present findings from this study assessing the contribution of a clinical knowledge graph to de novo dQM development. By understanding how CKGs leverage structured digital health knowledge as the ‘building blocks’ of measurement, measure developers, implementers and provider systems can unlock new efficiencies and enhance the accuracy of their dQM initiatives, contributing to a more effective and learning health system. Speakers’ Roles: Dr. Middleton will ‘set the stage’ for the discussion with a brief recap of the current state, and the development of dQMs. Dr. Chris Wroe will describe what a knowledge graph is, how it is created and maintained, and how it may be used to support evidence-based dQM development and implementation. Dr. Tricia Elliott will describe the NCQAs strategic goals in dQM, and results from the collaborative project.