Mapping computerized clinical guidelines to electronic medical records: Knowledge-data ontological mapper (KDOM)

Abstract

Clinical guidelines recommend quality standards for patient care. Encoding guidelines in a computer-interpretable format and integrating them with an Electronic Medical Record (EMR) can enable delivery of patient-specific recommendations when and where needed. GLIF3 is a language for representing computer-interpretable guidelines (CIGs) and sharing them among healthcare institutions. Sharing a CIG necessitates mapping its data items to the institutional EMRs. We developed a framework called Knowledge-Data Ontological Mapper (KDOM) that enables bridging the gap from abstractions used in CIGs to specific EMRs. Briding the gap involves: (1) using an ontology of mappings, and an optional reference information model, to map an abstraction gradually into EMR codes, and (2) automatically creating SQL queries to retrieve the EMR data. We evaluated the KDOM framework by mapping a GLIF3-encoded guideline into two different EMR schemas and by using the mapping ontology to define mappings from 15 GLIF3 CIGs and one SAGE CIG into our reference information model.