Everyone needs robust analytics: Integrating medical ontologies into the Dana-Farber Pathways.

Abstract

304 Background: The majority of clinical data is unstructured. Medical ontologies standardize the language used to communicate diagnoses, symptoms, and procedures used in healthcare. Incorporating medical ontologies into the Dana-Farber Pathways (DFP) provides structure and removes ambiguity by creating definitions for each data point. This exercise was critical for the development of the DFP into a fully automated platform that optimizes clinician workflow, reduces clicks, and expands the potential for data analysis. Methods: The DFP data model was reviewed to identify value sets contained in an existing medical ontology. DFP required employing multiple ontologies, which was difficult, as many ontologies overlap (e.g., SNOMED, NCI, ICD-O-3, ICD-10) and none were developed for this intention. For example, the majority of DFP necessitate site/histology as a criterion for decision-making. In our Electronic Data Warehouse (EDW), site/histology are stored as free text, as ICD-O-3 codes from the cancer registry, and as ICD-10 codes from Epic. To obtain site/histology in a structured format, both ICD-O-3 and ICD-10 were needed. Ongoing maintenance of the linkages between ontologies and value sets is required as ontologies are updated and DFP are enhanced. Results: The relationship between the DFP value set and the SNOMED ontology was 1:1. However, other relationships were not equal or straightforward. Some ontology values were too detailed for DFP. These were either grouped together (e.g. tumor site) or just one value was chosen (e.g. drug class). Others didn’t contain all the values required by DFP (e.g. regimens). Conclusions: DFP requires structured data related to ontologies but not identical to and not limited by the scope of ontologies. Choosing medical ontologies utilized in an EDW facilitates the implementation of auto-navigation, which ultimately streamlines provider workflow. A structured data model connected to ontologies collects valid and clearly defined data to support robust analytics. [Table: see text]