Presentation on theme: "Query Health Concept-to-Codes (C2C) SWG Meeting #5"— Presentation transcript:
1Query Health Concept-to-Codes (C2C) SWG Meeting #5 January 10, 2012
2Today’s Agenda Topic Time Allotted Quick Review of Updated Timeline and Future Meeting Times2:30 – 2:35Presentation by Subject Matter ExpertsVictor Beraja - Ibeza2:35 - 3:15Rhonda Fascile – CDISC SHARE3:15 – 4:00
3Meeting times extended from 2:30-4:00pm Proposed TimelineMeeting times extended from 2:30-4:00pmTODAYMeeting 1 –Dec 6Meeting 2 –Dec 13Meeting 3 –Dec 20Meeting 4 –Jan 03Meeting 5 –Jan 10Meeting 6 –Jan 17Meeting 7 –Jan 24Meeting 8 –Jan 31Meeting 9 –Feb 7Meeting 10 –Feb 14TasksIntroductionsScopeProposed ApproachIdentify SME and presentation timeline for next few meetingsPresentationhQueryi2b2PresentationI2b2 (Cont.)Intermountain HealthDOQS (Data Warehousing / Mapping)PresentationDOQS (Data Warehousing / Mapping) Cont.PopMedNetNLMPresentationIbezaCDISC SHAREPresentation3MNY Presbyterian Hospital Vocab TeamRELMA (LOINC)TasksNQFAHIMALexEVS and CTS2TasksPreliminary review of presentation summaries and Draft DeliverableTasksReview of presented concept mapping frameworks to select a proposed approachBegin Consensus Voting processTasksConsensus Voting FinalizedCoordinate offline activities to summarize approaches and develop draft deliverable from presentations
4concept to code mapping importance of Context queries Victor Beraja, M.D.Ibeza LLC, 2550 S Douglas Rd., Coral Gables, FL 33134| Tel.: |Copyright 2012concept to code mapping importance of Context queries
5Ibeza MissionSimplify healthcare through concept coding and a medical concept glossary.Run medical rules to inform patients and doctors at the point of care about clinical guidelines and insurance benefits so that both can have a frank discussion about what's best for the patient and what is covered by insurance.
6Clinical Data Architecture Today Doctors capture clinical data according to well defined History and Physical Exam Sections and Subsections.CMS - E&M Guidelines of 1997 are used by private and public programs.Current EHR’s store Clinical Data using E&M Guidelines to determine level of care.Queries using this architecture will make it simple to find accurate information.
7Concepts to CodesEach clinical data concept is mapped to SNOMED and/or LOINC as available within a structure that provides context
8Problem #1 w Present Queries Was a dilated fundus exam of the macula done in these patients groups with diabetes Type II?All had CPT (office visit) and ICD (cataract)Group 1: Retina Exam: DME positiveGroup 2: Retina Exam: DME negativeGroup 3: Retina Exam: Not doneWould miss 66% of positive exams
9Solution for Problem #1 Context Search Result 100% accurate result Search for the concepts in the context of Retina Exam of the Office Visit.“Dilated fundus exam”“Macula edema present” or “Macula edema not present”Result 100% accurate result
10Problem #2 w Present Queries Both patient groups billed with ICD (Diabetic Macular Edema) Do they have edema? Answer: Maybe.The Justification for the test was macular edema.Group 1: No edemaGroup 2: Edema.
11Solution for Problem #2 Context Search Result 100% accurate result Search for the concepts in the context of Fluorescein Angiogram Findings.“Macula edema present” or “Macula edema not present”Result 100% accurate result
13Clinical Data Today CMS Evaluation and Management Guidelines of 1997 Patient encounterProcedures and TestsReview of Systems ROSPast Family/Soc. HistoryChief ComplaintPhysical ExamEyes, Head & Neck, etc..
15Impact of this XML Schema HQuery results have 100% accuracyThe context gives source of informationEHR vendors can then communicate Clinical Data among each other and with HIE with the same ease with which they currently communicate labs in real timeSize of eRx in the US healthcare marketSize of APA in the US healthcare market.
16Why is this So Important? Public Health use is to identify “Hot Spots”We run rules in real time toDetect individual cases prevent them from becoming a “Hot Spot” statistic.Improve quality of careReduce fraud, waste, abuse, andMaintain proper medical care
18SummaryContext Searches can be accomplished by incorporating Sections and Subsections into HL7-CDAContext Searches yield accurate queries with primary source information
19Standards Overview and Current Status • How do your standards relate to concept mapping?Each clinical data concept is mapped to SNOMED and/or LOINC• Are you able to maintain the integrity of the original data in its native form (i.e. data as collected and not modified)?The integrity of the original data is preserved by creating a dictionary of clinical terms offered to the public so everyone can use the same terms in their clinical forms. New terminology submitted is then revised by a team of experts. These determine if the “new” term is added as “new” or as an “alternate wording” of an existing clinical term.
20Standards Integration and Infrastructure • How do you see your standard integrating with the QH Reference Implementation solution?Our standards allow Context Queries of specific Clinical Data. For example you will be able to query number of patients who had a dilated fundus exam with an exam of the macula for diabetic maculopathy. Standards Alignment to Query Health• Where does the mapping occur? Is it at the Data Source level? Or at the Information Requestor level? Or Both?Both. At the creation of the glossary of concepts mapped to SNOMED and LOINC.• Can it be easily implemented elsewhere? Yes Standards Maintenance• Who maintains the development of standards?A dedicated group of medical experts and engineers oversees the integrity and development of the standard.• Who maintains the mappings and how often are they released?A dedicated group of medical experts on a quarterly basis
22Query Health Concept to Codes Teleconference January 10, 2012CDISC SHARE Project OverviewRhonda Facile, CDISC
23CDISC SHARE Background Vision and Goals Project Plan Acknowledgements Where we are todayNext StepsAcknowledgements
24Harmonized through BRIDG Model** Controlled Terminology (NCI-EVS) Global Content Standards for Clinical Research (Protocol-driven Research; Protocol Reporting)Harmonized through BRIDG Model**Controlled Terminology (NCI-EVS)GlossaryFDA eSubmissionsAnalysis and ReportingFDA Critical PathInitiativeProtocolStudy DesignEligibilityRegistrationSchedule(PR Model)Case Report Forms (CRF)(CDASH)Study DataLab Data(LABand PGx)TabulatedCRF data(SDTM)Study DataLab DataStudy DesignAnalysis Datasets(ADaM)**Transport: CDISC ODM, SASXPT and/or HL7CDISC has standards that span the entire dd process. Going forward, we need to link up the standards and create more electronic readable formats to make full spectrum** CDISC, ISO, HL7 Standard
25CDISC SHARECDISC Standards now encompass the entire drug development process.The focus of CDISC SHARE is on integrating the CDISC standards family into an aligned, linked, machine readable, easily accessible, metadata repository.2D to 3D world
26The Need for Better Metadata To enhance Data Quality and ComplianceTo decrease the time needed to aggregate and review resultsMachine readable standards to improve “compliance”Illustrate inherent relationships between metadataSpeed up standards development
27Compliance Issues – 1 example Slide By: Ellen Pinnow, MS Health Programs Coordinator FDA, Office of Women’s Health, Slide from 2006 CDISC US InterchangeThis one simple slide illustrates how some of the first users of SDTM didn’t follow the SDTMIG even for something as simple as using the prescribed codes for male and female, thus making data aggregation difficult for answering even such a simple question as the proportion of women being included in clinical trials.It shows how creative implementation can be….
28Current 2D World Relationship Relationship Relationship “Consider a very simple example from the SDTM Vital Signs domain (see Figure 1 and 2). We as humans look at this tabulation and from the layout and positional relationships, from the column headings and from the actual content immediately build a number of relationships between data items in our heads. We understand that there is a relationship between the result value and unit variables. These are intrinsically linked in that one is not useful without the other. The relationship between systolic and diastolic is less clear, and is suggested only by the juxtaposition of the two rows. The fact that blood pressure is comprised of systolic and diastolic is not represented at all. While we can manipulate the data by writing some code, we have failed to tell the computer, the machine, about all of these relationships. Because these relationships are not explicitly represented, we need to do significant manual work. If we can build metadata to describe the internal relationships in our data, then manipulation engines can be built to undertake this work in a more automated fashion.”RelationshipSlide By: Dave Iberson-Hurst
29CDISC SHARE VISIONA global, accessible electronic library, which through advanced technology, enables precise and standardized data element definitions and richer metadata that can be used in applications and studies to improve biomedical research and its link with healthcare.SHARE metadata is envisioned to help find, understand and use clinical data efficiently.
30CDISC SHARE Library Contents Metadata (SDTM and CDASH)Trial Design MetadataDefinitionsDatatypesLinks to controlled terminology (CT) dictionaries via the NCIt (which links to CDISC CT, SNOMED, ICD9, ICD10, UMLS, etc.)Implementation instructionsCDASH CRF metadata and instructionsCDISC Metadata standards, extensions to those in the future
31CDISC SHARE Goals (1)Create an environment where existing content is consistently and easily maintainedProvide a consistent approach to standard definitionSpeed up new clinical research content developmentImprove access to standardsEncourage the widest possible participation in new clinical research content development (asynchronous contribution - 24/7)
32CDISC SHARE Goals (2)Facilitate data reuse - Data Aggregation and Mining can use legacy data to answer new questions, sometimes saving the cost of a new trial.Decrease costs - Downloadable metadata could reduce standards maintenance costs and enable process improvementDeliver all of CDISC’s existing and all new content in both human and machine-readable formsEnable better automated handling of clinical research data through the use of machine-readable contentFacilitate alignment of Clinical Research and Healthcare Standards
33How do we achieve this?Semantic Interoperability - Focus on developing rigorous and unambiguous definitions.BRIDG the Foundation of CDISC SHARE, ensure the link to healthcareCDISC SHARE Model – link all CDISC StandardsISO Standard – detailed data types to facilitate machine readability and transport.
34Semantic Interoperability CDASHSame?SDTMEnsure semantic interoperability across existing standards. This includes providing clear, unambiguous metadata definitions -“Rigorous and unambiguous definitions in SHARE would act as target standard to which disparate data could be mapped”Slide – Dave Iberson-Hurst34
35A domain analysis information model representing protocol-driven biomedical/clinical research Provides a basis for harmonization among standards within the clinical research domain and between biomedical/clinical research and healthcare.ISO compliant, HL7 alignment, RIM alignment
36Which Metadata Model? SDTM Intermountain Open eHR There is no recognized industry standard for modeling metadata and showing the relationships between data and metadata. Share seeks to provide a consistent and standardized mechanism by which data and metadata can be modeled to ensure that metadata across separate organizations is standardized and data is of the highest quality to facilitate optimum compliance.Slide – Dave Iberson-Hurst
37Controlled Terminology The CDISC SHARE MODELCDISC SHARE MODELSDTM VariablesCDASH VariablesControlled TerminologyBRIDG Classes21090 Data typesHere’s a view the show the model would be applied to show the relationships of metadata around the concept of blood pressure. We have chosen to follow BRIDG model
38Research Concept in Template Spreadsheet The model template spreadsheets like this.
39Transport & Protection of the Content SDTMADaMCDASHViewViewViewViewSHARE Scientific Concepts (BRIDG, Terminology, Data Types...)BRIDGThe content is the stable part in the middle, which should not be affected by how it is viewed (top part) or how it is transported (bottom part). XML V3Message(s)XMLFormat(s)TabularFormSlide – Dave Iberson-Hurst
40CDISC SHARE Model Benefits Summary Richer contentMachine readableLayered / structuredOne definition used many timesLinked together CDISC StandardsStructured using BRIDG constructs to reflect the nature of the data
422009 - Present CDASH & SDTM definitions aligned Implementation rules extracted Metadata model agreed CDISC SHARE Model tested Scientific concepts & attributes mapping (In progress)CDASH & SDTM reviewed and definitions alignedImplementation rules extracted from published standardsMetadata model developed and agreedModel tested on SDTM observation classesScientific concepts and their attributes identified and (mind) mapped for each type of SDTM domain
43Content MappingCurrent processTransforming SDTM and CDASH into scientific concept structured metadataAll SDTM and CDASH domains and variablesBRIDG definitionsControlled terminologyData typesAlso includes all of the SDTM and CDASH ‘rules’1 sheet per domain, tests in sub tabs.SDTM domains that have a CDASH component first since these are well defined and recognized, then the remainder of the SDTM.These excel spreadsheets will form the basis for the first batch load into the Share software.
44Governance Use Cases Under Development Work ItemA simple addition to a code listResearch ConceptCode ListCode List ItemWork ItemAddition of a Scientific Concept to a DomainResearch ConceptExisting DomainResearch ConceptResearch ConceptResearch ConceptDistinction between content objects and work items, both of which have to be managed. A work item can be of any size, and may involve work on one to very many content objects.Work ItemNew DomainNew DomainResearch ConceptResearch ConceptResearch ConceptResearch ConceptSlide: Dave Iberson-Hurst
45CDISC Share Phase 1 – Functionality Requirements Users should be able to:import & export contentmanipulate metadataaccess an electronic equivalent of a subset in PDF of SDTMIG v3.1.2, CDASH v 1.1, Controlled Terminology
46High-Level Project Plan MD ModelModel DevelopmentModel/Technology TeamContentContent TeamLAB Teamto start soon!Study Construction ConceptsLab TeamGovernance & User InterfaceGovernance TeamHigh Level Project PlanUser Interface TeamSoftware RequirementsSoftwareR1
47Project Plan6 subteamsContentGovernanceUser InterfaceStudy Construction ConceptsModel/technologyLab – to start soonOne more team to be initiated to evaluate potential software tools soon.
48Longer Term CDISC Share Development Plan Continuing SW Releases (do not need to be aligned with Phases)Major Development PhasesContinuous smaller increments in contentPhase 1Phase 2Phase 3Phase 4Phase 5SDTMCDASHOncology, Devices, TA (current)SEND and new TAADaM and new TAnew TASlide – Dave Iberson-Hurst
49CDISC Share - Conclusion Precise definitionsRich metadata24/7 accessLinked to NCItLinks Clinical Research to Healthcare.
50Active Participants Clyde Ulmer - FDA Dave Iberson-Hurst - Assero Erin Muhlbradt - NCI-EVSRhonda Facile – NCI-EVSFred Wood - OctagonChris Tolk - CDISCGary Walker - QuintilesDianne Reeves – NCI-CBIITHanming Tu - OctagonJulie Evans - CDISCMadhavi Vemuri – J & JJian Chen – EdetekMelissa Cook - OctagonCarlo Radovsky – Etera SolutionsMike Riben – MD AndersonGeoff Lowe – MEDIDATA SolutionsDiane Wold - GSKFrederick Malfait – RocheSimon Bishop - GSKKerstin Forsberg – Astra ZenecaTerry Hardin - ParexelKevin Burges – FormedixTsai Yiying - FDAMichael MorozewiczBarry Cohen - OctagonCDISC acknowledges all volunteers,their affiliated companies and theNCI-EVS for support of the CDISCShare project.Bold = team leaders
51Questions about CDISC Share. Interested in joining a team Questions about CDISC Share? Interested in joining a team? Contact Or visit: cdisc.org
52Strength through collaboration. ...and sharingAs a catalyst for productive collaboration, CDISC brings togetherindividuals spanning the healthcare continuum to developglobal, open, consensus-based medical research data standards.