1. 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

2. 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. 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. 4 System though out the practice

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8. 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. 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. 10 Practice Ontology Hierarchy (showing is-a relationships) encounterancillaryeventinsurance_ carrier insurancefacilityinsurance_ plan patientpersonpractitionerinsurance_ policy owl:thingambularory _episode

11. 11 Drug Ontology Hierarchy (showing is-a relationships) owl:thingprescription _drug_ brand_name brandname_ undeclared brandname_ composite prescription _drug monograph _ix_class cpnum_ group prescription _drug_ property indication_ property formulary_ property non_drug_ reactant interaction_ property propertyformularybrandname_ individual interaction_ with_prescri ption_drug interactionindicationgeneric_ individual prescription _drug_ generic generic_ composite interaction_ with_non_ drug_reactant interaction_ with_mono graph_ix_cl ass

12. 12 Drug Ontology showing neighborhood of PrescriptionDrug concept

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

14. 14 Extraction and Annotation using an ontology

15. 15 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 ICD9CMDiagnosis Name 244.9Hypothyrodism 250.00Diabetes mellitus Type II 250.01Diabetes Mellitus Type I 272.2Mixed Hyperlipidemia 414.01CAD – Native 780.2- 780.4 Syncope and Collapse Dizziness and Giddiness 780.79Other Malaise and Fatigue 785.0- 785.3 Tachycardia Unspecified - Other Abnormal Heart Sounds 786.50- 786.51 Unspecified Chest Pain – Precordial 786.59Other Chest Pain

16. 16 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

17. 17 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” )

18. 18 Architecture & Technology

19. 19 Demo On-line demo of Active Semantic Electronic Medical Record deployed and in use at Athens Heart Center

20. 20 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.

21. 21 Athens Heart Center Practice Growth

22. 22 Chart Completion before the preliminary deployment of the ASMER

23. 23 Chart Completion after the preliminary deployment of the ASMER

24. 24 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

25. 25 Benefits of current system Biggest benefit is that decisions are now in the hands of physicians not insurance companies or coders.

26. 26 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/

27. 27 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. “

28. 28 Comments on Evaluation Questions? More? See Active Semantic Document Project ( http://lsdis.cs.uga.edu/projects/asdoc/) at the LSDIS lab Or resources (example ontologies, Web services, tools, applications): Google: LSDIS resources, or http://lsdis.cs.uga.edu/library/resources/ http://lsdis.cs.uga.edu/projects/asdoc/

29. 29 Annotate ICD9s Annotate Doctors Lexical Annotation Level 3 Drug Interaction Insurance Formulary Drug Allergy Active Semantic Doc with 3 Ontologies

30. 30 Explore neighborhood for drug Tasmar E xplore: Drug Tasmar

31. 31 Explore neighborhood for drug Tasmar belongs to group brand / generic classification interaction Semantic browsing and querying-- perform decision support (how many patients are using this class of drug, …)