1 / 13

Using Ontologies in Clinical Decision Support Applications

Using Ontologies in Clinical Decision Support Applications. Samson W. Tu Stanford Medical Informatics Stanford University. Main points. Information technology has the potential to advance patient care by improving clinician adherence to clinical practice guidelines

Rita
Download Presentation

Using Ontologies in Clinical Decision Support Applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using Ontologies in Clinical Decision Support Applications Samson W. Tu Stanford Medical Informatics Stanford University

  2. Main points • Information technology has the potential to advance patient care by improving clinician adherence to clinical practice guidelines • Principled architecture that separates ontologies, knowledge bases, and problem-solving components allows development and deployment of maintainable complex software systems • EON and ATHENA projects demonstrate use of ontologies in clinical decision support applications

  3. EON project • NLM-funded project at Stanford (PI: Dr. Musen) • Develop methodology, ontologies, and software components for creating decision-support system for guideline-based care • Use Protégé knowledge-acquisition methodology and tool for construction of • Domain concept ontologies • Patient information model • Guideline knowledge bases • Develop software components that assist clinicians in specific tasks • Therapy-advisory and eligibility-determination servers • Database mediator for time-oriented queries • Explanation and visualization facilities

  4. Yenta Yenta Yenta Yenta Eligibility Client Advisory Client EON architecture Patient Database Servers Clients Clients Temporal Mediator Protégé Knowledge Base Protocol Eligibility Checker EON Guideline Ontology TherapyAdvisory Server Medical Domain Ontology Patient Data Model Protégé Guidelines

  5. ATHENA project • Funded by VA Research Service HSR&D (PIs: Drs. Hoffman and Goldstein, VA clinicians and Stanford faculties) • Hypothesized that guideline-based interventions in management of hypertension can • Change physicians’ prescribing behavior • Change patient outcome • Deployed and evaluated at primary care VA clinics in 9 geographically diverse cities over a 15-month clinical trial • Results • Expert clinicians maintain hypertension knowledge base using Protégé • Clinicians interacted with the ATHENA Hypertension Advisory at 54% of all patient visits • Impact on prescribing behavior and change patient outcome being analyzed

  6. Building ATHENA system from EON components VA CPRS VA DHCP EON Servers ATHENA Clients Patient Database Temporal Mediator ATHENA Clients Event Monitor Event Monitor Guideline Interpreter Data Converter Advisory Client Advisory Client ATHENA GUI nightly data extraction Guideline Knowledge Base Protégé

  7. What the Clinician Sees…

  8. ATHENA HTN Advisory BP targets Primary recommendation Drug recommendation

  9. ATHENA HTN Advisory: More Info

  10. ATHENA HTN Advisory: Link to evidence base

  11. EON ontologies Generic data types (generalize to HL7 data types) Medical concept ontology (generalizes to standard terminologies) Patient information model (generalizes to HL7 RIM) Guideline ontology

  12. Physician-maintained hypertension knowledge base

  13. Benefits of ontology-based clinical information systems • Separation of declarative domain knowledge and procedural problem-solving knowledge allow • Content experts to maintain knowledge bases • Standardization of ontologies that leads to sharing and interoperability • Semantically rich ontologies allow sophisticated reasoning and decision support • e.g., automatic concept classification based on description logic • e.g., detailed drug recommendations based on computable model of clinical practice guidelines

More Related