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Integration of Health Information Resources into Electronic Health Records Using HL7

Integration of Health Information Resources into Electronic Health Records Using HL7 . Guilherme Del Fiol, MD, MS Biomedical Informatics Department, University of Utah

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Integration of Health Information Resources into Electronic Health Records Using HL7

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  1. Integration of Health Information Resources into Electronic Health Records Using HL7 Guilherme Del Fiol, MD, MS Biomedical Informatics Department, University of Utah Intermountain Healthcare, Salt Lake City, UTJames J. Cimino, MD Department of Biomedical Informatics, Columbia University, New York, NYSaverio Maviglia, MD, MScPartners Healthcare System, Boston, MA

  2. Outline • Background • HL7 infobutton standard • Demonstration participants • Infobutton Managers • Information resource providers • Live demonstration

  3. Infobuttons Background

  4. Information for Decision-Making ? MRSA

  5. Addressing Information Needs with Infobuttons Clinical information systems evoke information needs Clinician’s computer has access to resources Context can be used to predict need Context can be used to automate retrieval

  6. ! ? Age Sex Role Training Task Data Institution Context Context-Dependent Information Needs

  7. Resource s Clinical System Infobutton Context Query Knowledge Base Infobutton Manager Page of Hyperlinks Infobuttons vs. Infobutton Manager

  8. Infobutton standard Overview

  9. Why do we need a standard? • There is not a common integration language • Parameter names • Terminologies used for content searchretrieval • Hundreds of resources available • Not designed for infobutton integration: suboptimal results • Labor intensive integration: just a few are actually used

  10. Multiple ways of “asking” the same question What is the dose of azithromycin ? i http://resource1.com/search = “azithromycin AND dose” http://resource2.com/ query = "azithromycin"[MeSH Terms] AND dose[All Fields] http://resource3.com/ searchConcept = 3333 ^ azithromycinfilter = 11 ^ dosage

  11. No standard in place Resource 1 API http://resource1.com/search.cgi?search = “azithromycin AND dose” http://resource2.com/ query = "azithromycin"[MeSH Terms] AND dose[All Fields] Clinical Information System Infobutton Manager Resource 2 i API API http://www.resource3.com/search.cgi?searchConcept = 3333^ azithromycinfilter = 11 ^ dosage Resource 3 API

  12. Standard-based integration Resource 1 Columbia HL7 HL7 ElectronicHealthRecord Intermountain Resource 2 i HL7 HL7 HL7 Partners HL7 Resource 3 HL7

  13. Key points • XMLand URL-basedsyntax • Recommends adoption of a set of standard terminologies (e.g., RxNorm, LOINC, SNOMED-CT, MeSH) • Aligned with national initiatives • Flexible requirements to allow faster adoption

  14. Example • The user is looking at a problem listof a female, 94 years-oldpatient with Heart Failure. The user clicks on an infobutton that presents a series of questions. The user selects “How do I treatHeart Failure?”

  15. <gendercode=“F"displayName=“Female"/> <agevalue=“94" unit=“a"/> <taskContextcode=“PROBLISTREV"/> <mainSearchCriteria code="428“ codeSystem="2.16.840.1.113883.6.103"displayName=“Heart Failure"/> <mainSearchCriteriacode="428“ codeSystem="2.16.840.1.113883.6.103" displayName=“Heart Failure"/> • <informationRecipient> • <patient> • <languagecode=“eng"/> • <patient> • <informationRecipient> <subTopiccode="Q000628"codeSystem="2.16.840.1.113883.6.177"displayName="therapy"/>

  16. <patientContext> <gendercode=“F"displayName=“Female"/> <agevalue=“94" unit=“a"/> <taskContextcode=“PROBLISTREV"/> <mainSearchCriteriacode="428"codeSystem="2.16.840.1.113883.6.103"displayName=“Heart Failure"/> <subTopiccode="Q000628"codeSystem="2.16.840.1.113883.6.177"displayName="therapy"/>

  17. URL-based message • Simpler implementation • Support industry backwards compatibility • Faster adoption • Rules for automated conversion • URL can be automatically derived from XML model

  18. age.v=56 age.u=a administrativeGenderCode.c=F mainSearchCriteria.c.c=2823-3mainSearchCriteria.c.cs=2.16.840.1.113883.6.1mainSearchCriteria.c.dn=Serum potassium mainSearchCriteria.c.ot=K taskContext.c.c=LABRREV interpretationCode.c.c=L

  19. http://www.e-resource.com/api? patientPerson.administrativeGenderCode.c=F age.v=56&age.u=a taskContext.c=LABRREV mainSearchCriteria.c=2823-3 mainSearchCriteria.cs=2.16.840.1.113883.6.1 mainSearchCriteria.dn=Serum potassium mainSearchCriteria.c.ot=K

  20. Demonstration Participants

  21. Content providers • ACP PIER • Clin-eguide (Wolters Kluwer Health) • Dynamed (Ebsco) • Lexicomp • Micromedex (Thomson Healthcare) • UpToDate

  22. Infobutton Managers • Intermountain Healthcare • First production version in 2001 • Infobutton Manager since 2005 • Medication order entry, problem list,lab results • 1,000+ users per month • Knowledge base: resources and questions configured in XML files

  23. Infobutton Managers • Columbia University • Concept of interest translated into controlled terminology • Related concepts identified • Topics/questions matched to concept classes and other context parameters • XML table of topics (along with javascript) returned to the user • Links are initiated from user’s browser

  24. Infobutton Managers • Columbia University – usage • Infobuttons available since 1996 • Infobutton manager version 1 2002 • Available in: • WebCIS: lab results, micro results, sensitivity results, inpatient drugs, outpatient drugs, problem list • Eclipsys: lab orders, drug orders, nursing orders • Regenstrief Medical Records System: drug orders • NY State Psych Institute: drug orders • NextGen: lab results • 700+ users per month • 2100+ uses per month

  25. Infobutton Managers • Columbia University – benefits • Easy to use: 92% • Question on list >50% of time: 89% • Answered question: 69% • Useful: 77% • Helpful >50% of time: 90% • Positive effect on care: 74%

  26. Infobutton Managers • Partners Healthcare • Live since 2002 • Medication order entry, problem list,lab results (8 clinical apps) • Federated search engine for 2 library portals • 50K sessions by 5K unique users per month • 60% RN, 15% MD, 11% PharmD • 1-50% of patient encounters • 90% medication queries • Median session duration under 15 seconds! • 85-90% success rate • Resources and context triggers configured in SQL/Access – no terminology or lexical analysis

  27. http://www.hl7.org/v3ballot/html/welcome/environment/index.htmhttp://www.hl7.org/v3ballot/html/welcome/environment/index.htm

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