Ontological approach to reduce complexity in polypharmacy.

Susan Farrish, Maria Grando

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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 languageEnglish (US)
Pages (from-to)398-407
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2013
StatePublished - 2013
Externally publishedYes

Fingerprint

Polypharmacy
Drug Design
Prescriptions
Databases

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Ontological approach to reduce complexity in polypharmacy. / Farrish, Susan; Grando, Maria.

In: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, Vol. 2013, 2013, p. 398-407.

Research output: Contribution to journalArticle

@article{3d9bb104c46342d4ad3ecaac2f046222,
title = "Ontological approach to reduce complexity in polypharmacy.",
abstract = "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.",
author = "Susan Farrish and Maria Grando",
year = "2013",
language = "English (US)",
volume = "2013",
pages = "398--407",
journal = "AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium",
issn = "1559-4076",
publisher = "American Medical Informatics Association",

}

TY - JOUR

T1 - Ontological approach to reduce complexity in polypharmacy.

AU - Farrish, Susan

AU - Grando, Maria

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84901251289&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901251289&partnerID=8YFLogxK

M3 - Article

C2 - 24551346

AN - SCOPUS:84901251289

VL - 2013

SP - 398

EP - 407

JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

SN - 1559-4076

ER -