Structuring Clinical Pathways: Creating Common Language Where There Is None.

303 Background: Clinical pathways (PW) consist of Decision Criteria (DC) such as patient and disease characteristics, prior therapy, and genetic tests. Historical Dana-Farber Pathways (DFP) content was unstructured and maintained in static documents. This could lead to inconsistencies across and within PW, limits to the scope of DFP analytics, and potential discrepancies in clinical content. Methods: To transform DFP content into a digitally innovative structure the DFP team created a hierarchical Data Model (DM). The team compiled all unstructured DC in historical PW, organized them into parameters and attributes, and connected them to external ontologies (e.g., ICDO3) where appropriate. The team then applied the structured DC to historical PW to test comprehensiveness; and addressed any gaps identified in PW and the DM. Results: The DPF DM contains 32 parameters (e.g., Diagnosis) and 218 attributes (e.g., Group Stage) that can be combined to represent all 600+ pathway branch points. The comprehensiveness and nuance of the DM improves DFP’s specificity and clinical flexibility: The DM ensures that DFP gathers actionable data across all PW and creates common language that allows for disease-specific nuances; The DM rectifies gaps in historical PW, such as DC that were not mutually exclusive or conflated multiple clinical parameters; The DM creates complex DC to direct specific sub-groups of patients to the correct treatment path, even in cases where treatment recommendations differ within diagnostic groups. Conclusions: Structuring and standardizing PW content is a complex, time-intensive endeavor. However, this work addresses the challenges of managing clinical content and provides significant benefits for future PW development. A structured DM ensures PW are comprehensive, logical, and built on a framework of inter-operability standardization. A DM also allows DFP to connect with the EMR for auto-navigation, which streamlines provider workflow. The ongoing work required to build and maintain a structured PW DM is worth the result: rich actionable data that can illuminate and standardize practice patterns within and across diseases and institutions. It creates an insightful solution to broadly manage the cancer population.