JMIR Publications

ABSTRACT

Background:

Medication inappropriateness in older patients with multimorbidity leads to a greater risk of adverse drug events. Clinical decision support systems (CDSS) are intended to improve medication appropriateness. One approach to improve CDSS is to use ontologies instead of relational databases. Previously, we developed OntoPharma, an ontology based CDSS to reduce medication prescribing errors.

Objective:

The primary aim was to model a domain to improve medication appropriateness in older patients (chronic patient domain). Secondary aim was to implement OntoPharma containing chronic patient domain in a hospital setting.

Methods:

A four-step process was proposed. 1) Defining the domain scope. Chronic patient domain focused on improving medication appropriateness in older patients. A group of experts selected three uses cases: medication regimen complexity; anticholinergic and/or sedative drug burden and presence of triggers to identify possible adverse events. 2) Domain model representation. The implementation was conducted by Medical Informatics specialists and Clinical Pharmacists using Protégé-OWL. 3) OntoPharma-driven alert module adaptation. We reused the existing framework based on SPARQL to query ontologies. 4) Implementing OntoPharma containing the chronic patient domain in a hospital setting. Alerts generated between July to September 2022 were analysed.

Results:

We proposed six new classes and five new properties introducing the necessary changes in the ontologies previously created. An alert is shown if: Medication Regimen Complexity Index ≥40 and/or Drug Burden Index ≥1 and/or there is a trigger based on abnormal laboratory value. 364 alerts were generated in 107 patients. 154 (42.3%) alerts were accepted.

Conclusions:

We propose an ontology-based approach to provide support for improving medication appropriateness in older patients with multimorbidity in a scalable, sustainable and reusable way. The chronic-patient domain was built on our previous research reusing the existing framework. OntoPharma is implemented in clinical practice and generates alerts considering the following use cases: medication regimen complexity; anticholinergic and/or sedative drug burden and presence of triggers to identify possible adverse events. Clinical Trial: Not applicable