Objectives: Several recently published cases of preventable adverse drug reactions were associated with flaws in drug application. However, current clinical decision support (CDS) systems do not properly consider drug application issues and thus do not support effective prevention of such medication errors. With the aim to improve CDS in this respect, we developed a comprehensive model precisely describing all aspects of drug application.
Methods: The model consists of 1) a schema comprising all relevant attributes of drug application and 2) an ontology providing a hierarchically structured vocabulary of terms that describe the possible values of the schema's attributes. Finally, medical products were annotated by a semi-automatic term assignment process. For evaluation, we developed an algorithm that uses our model to compute a meaningful similarity between medicinal products with respect to their drug application characteristics.
Results: Our schema consists of 22 attributes. The ontology contains 248 terms, textual descriptions, and synonym lists. More than 58,700 medicinal products were automatically annotated with >386,600 terms. 2,450 drugs were manually reviewed by experts, adding >4500 terms. The annotation and similarity measure allow for (similarity) searches, clustering, and proper discrimination of drugs with different drug application characteristics. We demonstrated the value of our approach by means of a set of case studies.
Conclusion: Our model enables a detailed description of drug application, allowing for semantically meaningful comparisons of drugs. This is an important prerequisite for improving the ability of CDS systems to prevent prescription errors.