Patients that are on many medications are often non-compliant due to the complexity of the medication regimen; consequently, a patient that is non-compliant can have poor medical outcomes. Providers are not always aware of the complexity of their patient's prescriptions. Methods have been developed to calculate the complexity for a patient's regimen but there are no widely available automated tools that will do this for a provider. Given that ontologies are known to provide well-principled, sharable, setting-independent and machine-interpretable declarative specification frameworks for modeling and reasoning on biomedical problems, we have explored their use in the context of reducing medication complexity. Previously we proposed an Ontology for modeling drug-related knowledge and a repository for complexity scoring. Here we tested the Ontology with patient data from the University of California San Diego Epic database, and we built a decision aide that computes the complexity and recommends changes to reduce the complexity, if needed.
|Original language||English (US)|
|Number of pages||10|
|Journal||AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium|
|State||Published - 2013|
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