Design of a Fine-Grained Knowledge Model for the Formalization of Clinical Practice Guidelines: Comparison with GEM

Published as textual documents, clinical practice guidelines (CPGs) didn't demonstrate to impact physician practices when disseminated in their original format. However, when computerized and embedded in clinical decision support systems, they appeared to be more effective. In order to ease the translation from textual to computerized CPGs, we have elaborated a fine-grained knowledge model of CPGs (FGKM) to be used when authoring CPGs. The work has been conducted on VIDALRecos (R) CPGs. The building of the model has followed a bottom-up iterative process starting with 15 different CPGs. The first version of the FGKM has been assessed on two new complex CPGs, and was enriched by comparison with the Guideline Elements Model (GEM). The final version of the FGKM has been tested on the 2014 Hypertension CPGs. We compared the rules automatically derived from FGKM instances to those manually extracted from textual CPGs for decision support. Results showed that difficulties such as text normalization have to be solved. The FGKM is intended to be used upstream of the process of CPGs authoring in order to ease the implementation and the update of both textual and computerized CPGs.