Using a Clinical Knowledge Base to Assess Comorbidity Interrelatedness Among Patients with Multiple Chronic Conditions.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
PMID: 26958279

Abstract

Decision support tools increasingly integrate clinical knowledge such as medication indications and contraindications with electronic health record (EHR) data to support clinical care and patient safety. The availability of this encoded information and patient data provides an opportunity to develop measures of clinical decision complexity that may be of value for quality improvement and research efforts. We investigated the feasibility of using encoded clinical knowledge and EHR data to develop a measure of comorbidity interrelatedness (the degree to which patients' co-occurring conditions interact to generate clinical complexity). Using a common clinical scenario-decisions about blood pressure medications in patients with hypertension-we quantified comorbidity interrelatedness by calculating the number of indications and contraindications to blood pressure medications that are generated by patients' comorbidities (e.g., diabetes, gout, depression). We examined properties of comorbidity interrelatedness using data from a decision support system for hypertension in the Veterans Affairs Health Care System.

Authors

  • Donna M Zulman
    Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA; Division of General Medical Disciplines, Stanford University, Stanford, CA.
  • Susana B Martins
    Geriatrics Research Education and Clinical Center (GRECC), VA Palo Alto Health Care System, Palo Alto, CA.
  • Yan Liu
    Department of Clinical Microbiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.
  • Samson W Tu
    Biomedical Informatics Research (BMIR), Stanford University School of Medicine, Stanford, CA.
  • Brian B Hoffman
    VA Boston Health Care System, West Roxbury Division, Boston, MA; Harvard Medical School, Boston, MA.
  • Steven M Asch
    Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA; Division of General Medical Disciplines, Stanford University, Stanford, CA.
  • Mary K Goldstein
    VA Palo Alto Health Care System, Palo Alto, CA, and Stanford University, Stanford, CA, USA.

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