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Amit Sheth , S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, K.Gallagher

Active Semantic Electronic Medical Records an Application of Active Semantic Documents in Health Care. Amit Sheth , S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, K.Gallagher Athens Heart Center & LSDIS Lab, University of Georgia http://lsdis.cs.uga.edu.

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Amit Sheth , S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, K.Gallagher

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  1. Active Semantic Electronic Medical Recordsan Application of Active Semantic Documents in Health Care Amit Sheth , S. Agrawal, J. Lathem, N. Oldham, H. Wingate, P. Yadav, K.Gallagher Athens Heart Center & LSDIS Lab, University of Georgia http://lsdis.cs.uga.edu

  2. Semantic Web application in use In daily use at Athens Heart Center • 28 person staff • Interventional Cardiologists • Electrophysiology Cardiologists • Deployed since January 2006 • 40-60 patients seen daily • 3000+ active patients • Serves a population of 250,000 people

  3. Information Overload • New drugs added to market • Adds interactions with current drugs • Changes possible procedures to treat an illness • Insurance Coverage's Change • Insurance may pay for drug X but not drug Y even though drug X and Y are equivalent • Patient may need a certain diagnosis before some expensive test are run • Physicians need a system to keep track of ever changing landscape

  4. System though out the practice

  5. System though out the practice

  6. System though out the practice

  7. System though out the practice

  8. Active Semantic Document (ASD) A document (typically in XML) with the following features: • Semantic annotations • Linking entities found in a document to ontology • Linking terms to a specialized lexicon • Actionable information • Rules over semantic annotations • Violated rules can modify the appearance of the document (Show an alert)

  9. Active Semantic Patient Record • An application of ASD • Three Ontologies • Practice Information about practice such as patient/physician data • Drug Information about drugs, interaction, formularies, etc. • ICD/CPT Describes the relationships between CPT and ICD codes • Medical Records in XML created from database

  10. insurance_carrier encounter ancillary event insurance facility patient person practitioner owl:thing ambularory_episode insurance_policy insurance_plan Practice Ontology Hierarchy(showing is-a relationships)

  11. non_drug_ reactant owl:thing prescription_drug_ brand_name brandname_undeclared brandname_composite prescription_drug monograph_ix_class cpnum_ group prescription_drug_ property indication_ property formulary_ property interaction_with_monograph_ix_class interaction_property property formulary brandname_individual interaction_with_prescription_drug interaction indication generic_ individual prescription_drug_ generic generic_ composite Drug Ontology Hierarchy(showing is-a relationships) interaction_ with_non_ drug_reactant

  12. Drug Ontology showing neighborhood of PrescriptionDrug concept

  13. Part of Procedure/Diagnosis/ICD9/CPT Ontology specificity maps_to_diagnosis procedure diagnosis maps_to_procedure

  14. Local Medical Review Policy (LMRP) support Example – a partial list of ICD9CM codes that support medical necessity for an EKG (CPT 93000) Data extracted from the Centers for Medicare and Medicaid Services

  15. Technology - now • Semantic Web: OWL, RDF/RDQL, Jena • OWL (constraints useful for data consistency), RDF • Rules are expressed as RDQL • REST Based Web Services: from server side • Web 2.0: client makes AJAX calls to ontology, also auto complete Problem: • Jena main memory- large memory footprint, future scalability challenge • Using Jena’s persistent model (MySQL) noticeably slower

  16. Design and Implementation Issues • Schema design • Population (knowledge sources) • Freshness • Scalability though client side processing • Rules: “Starting at instance A is it possible to get to instance B going through these certain relationships, if so what are the properties of the relationship” (e.g., “Does nitrates or a super class of nitrates interact with Viagra or one of its super classes, if so what is the interaction level” )

  17. Architecture & Technology

  18. Demo On-line demo of Active Semantic Electronic Medical Record deployed and in use at Athens Heart Center

  19. Evaluation and ROI • Given that this work was done in a live, operational environment, it is nearly impossible to evaluate this system in a “clean room” fashion, with completely controlled environment – no doctors’ office has resources or inclination to subject to such an intrusive, controlled and multistage trial. Evaluation of an operational system also presents many complexities, such as perturbations due to change in medical personnel and associated training.

  20. Athens Heart Center Practice Growth

  21. Chart Completion before the preliminary deployment of the ASMER

  22. Chart Completion after the preliminary deployment of the ASMER

  23. Benefits of current system • Error prevention (drug interactions, allergy) • Patient care • insurance • Decision Support (formulary, billing) • Patient satisfaction • Reimbursement • Efficiency/time • Real-time chart completion • “semantic” and automated linking with billing

  24. Benefits of current system • Biggest benefit is that decisions are now in the hands of physicians not insurance companies or coders.

  25. Technology - Future • BRAHMS (with SPARQL support and path computation*) for high performance main memory based computation • SWRL for better rule representation • Support for example user specified rules, possibly for integration with clinical pathways: • If patients blood pressure is > than 150/70 prescribe this medicine automatically. • If patients weight is > 350 disallow a nuclear scan in the office because our scanning bed cannot handle such weight. • If patient has diagnoses X alert, the user to suggest a doctor to refer patient to Y. * Semantic Discovery http://lsdis.cs.uga.edu/projects/semdis/

  26. Value propositions & Next steps • Increasing the value of content, and content in context – highly customized using one of the ontologies (not just CTP/ICD9, but also specialty specific), at the point of use; no separate search, no wading through delivered content • Actionable rules • Possible trial involving alert services: “When a physician scrolls down on the list of drugs and clicks on the drug that he wants to prescribe, any study / clinical trial / news item about the drug and other related drugs in the same category will be displayed. “

  27. Comments on EvaluationQuestions?More? See Active Semantic Document Project (http://lsdis.cs.uga.edu/projects/asdoc/)at the LSDIS labOr resources (example ontologies, Web services, tools, applications):Google: LSDIS resources, orhttp://lsdis.cs.uga.edu/library/resources/

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