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Query Health Concept-to-Codes (C2C) SWG Meeting #5 January 10, 2012 1.

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Presentation on theme: "Query Health Concept-to-Codes (C2C) SWG Meeting #5 January 10, 2012 1."— Presentation transcript:

1 Query Health Concept-to-Codes (C2C) SWG Meeting #5 January 10,

2 Today’s Agenda TopicTime Allotted Quick Review of Updated Timeline and Future Meeting Times 2:30 – 2:35 Presentation by Subject Matter Experts Victor Beraja - Ibeza 2:35 - 3:15 Rhonda Fascile – CDISC SHARE 3:15 – 4:00 2

3 Proposed Timeline 3 TODAY Coordinate offline activities to summarize approaches and develop draft deliverable from presentations Meeting 1 – Dec 6 Meeting 2 – Dec 13 Meeting 3 – Dec 20 Meeting 4 – Jan 03 Meeting 5 – Jan 10 Meeting 6 – Jan 17 Meeting 7 – Jan 24 Meeting 8 – Jan 31 Meeting 9 – Feb 7 Meeting 10 – Feb 14 Tasks Preliminary review of presentation summaries and Draft Deliverable Presentation I2b2 (Cont.) Intermountain Health DOQS (Data Warehousing / Mapping) Tasks Introductions Scope Proposed Approach Identify SME and presentation timeline for next few meetings Meeting times extended from 2:30-4:00pm Presentation hQuery i2b2 Presentation DOQS (Data Warehousing / Mapping) Cont. PopMedNet NLM Presentation Ibeza CDISC SHARE Tasks Review of presented concept mapping frameworks to select a proposed approach Begin Consensus Voting process Presentation 3M NY Presbyterian Hospital Vocab Team RELMA (LOINC) Tasks NQF AHIMA LexEVS and CTS2 Tasks Consensus Voting Finalized

4 CONCEPT TO CODE MAPPING IMPORTANCE OF CONTEXT QUERIES Victor Beraja, M.D. 4 Ibeza LLC, 2550 S Douglas Rd., Coral Gables, FL | Tel.: | Copyright 2012

5 Ibeza Mission Simplify 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. 5

6 Clinical 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. 6

7 Concepts to Codes Each clinical data concept is mapped to SNOMED and/or LOINC as available within a structure that provides context 7

8 Problem #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 positive –Group 2: Retina Exam: DME negative –Group 3: Retina Exam: Not done Would miss 66% of positive exams 8

9 Solution for Problem #1 Context Search –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 9

10 Problem #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 edema –Group 2: Edema. 10

11 Solution for Problem #2 Context Search –Search for the concepts in the context of Fluorescein Angiogram Findings. –“Macula edema present” or “Macula edema not present” Result 100% accurate result 11

12 CMS vs. HITSP CDA 12

13 Clinical Data Today CMS Evaluation and Management Guidelines of 1997 Patient encounter Procedures and Tests Review of Systems ROS Past Family/Soc. History Chief Complaint Physical Exam –Eyes, Head & Neck, etc..

14 14

15 Impact of this XML Schema HQuery results have 100% accuracy The context gives source of information EHR vendors can then communicate Clinical Data among each other and with HIE with the same ease with which they currently communicate labs in real time

16 Why is this So Important? Public Health use is to identify “Hot Spots” We run rules in real time to –Detect individual cases prevent them from becoming a “Hot Spot” statistic. –Improve quality of care –Reduce fraud, waste, abuse, and –Maintain proper medical care 16

17 17

18 Summary Context Searches can be accomplished by incorporating Sections and Subsections into HL7-CDA Context Searches yield accurate queries with primary source information

19 Standards 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. 19

20 Standards 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 20

21 The End 21

22 22 Query Health Concept to Codes Teleconference January 10, 2012 CDISC SHARE Project Overview Rhonda Facile, CDISC

23 23 CDISC SHARE Background Vision and Goals Project Plan – Where we are today – Next Steps Acknowledgements

24 24 FDA Critical Path Initiative FDA eSubmissions Analysis and Reporting * *Transport: CDISC ODM, SASXPT and/or HL7 Global Content Standards for Clinical Research (Protocol-driven Research; Protocol  Reporting) Protocol Study Design Eligibility Registration Schedule (PR Model) Lab Data (LAB and PGx) Analysis Datasets (ADaM) Tabulated CRF data (SDTM) Study Data Lab Data Study Design Case Report Forms (CRF) (CDASH) Study Data Harmonized through BRIDG Model** Controlled Terminology (NCI-EVS) Glossary ** CDISC, ISO, HL7 Standard

25 25 CDISC SHARE CDISC 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.

26 26 The Need for Better Metadata To enhance Data Quality and Compliance To decrease the time needed to aggregate and review results Machine readable standards to improve “compliance” Illustrate inherent relationships between metadata Speed up standards development

27 27 Compliance Issues – 1 example Slide By: Ellen Pinnow, MS Health Programs Coordinator FDA, Office of Women’s Health, Slide from 2006 CDISC US Interchange

28 28 Current 2D World Relationship Slide By: Dave Iberson-Hurst

29 29 A 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. CDISC SHARE VISION

30 30 CDISC SHARE Library Contents Metadata (SDTM and CDASH) – Trial Design Metadata – Definitions – Datatypes Links to controlled terminology (CT) dictionaries via the NCIt (which links to CDISC CT, SNOMED, ICD9, ICD10, UMLS, etc.) Implementation instructions CDASH CRF metadata and instructions

31 31 CDISC SHARE Goals (1) Create an environment where existing content is consistently and easily maintained Provide a consistent approach to standard definition Speed up new clinical research content development Improve access to standards Encourage the widest possible participation in new clinical research content development (asynchronous contribution - 24/7)

32 32 CDISC 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 improvement Deliver all of CDISC’s existing and all new content in both human and machine-readable forms Enable better automated handling of clinical research data through the use of machine-readable content Facilitate alignment of Clinical Research and Healthcare Standards

33 33 How do we achieve this? Semantic Interoperability - Focus on developing rigorous and unambiguous definitions. BRIDG the Foundation of CDISC SHARE, ensure the link to healthcare CDISC SHARE Model – link all CDISC Standards ISO Standard – detailed data types to facilitate machine readability and transport.

34 34 Semantic Interoperability 34 CDASH SDTM Same? Slide – Dave Iberson-Hurst

35 35 A 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

36 36 Which Metadata Model? SDTM Intermountain Open eHR Slide – Dave Iberson-Hurst

37 37 The CDISC SHARE MODEL SDTM Variables CDASH Variables Controlled Terminology BRIDG Classes Data types CDISC SHARE MODEL

38 38 Research Concept in Template Spreadsheet

39 39 Transport & Protection of the Content SHARE Scientific Concepts (BRIDG, Terminology, Data Types...) XML V3 Message(s) XML V3 Message(s) SDTM ADaM CDASH XML Format(s) XML Format(s) Tabular Form Tabular Form View BRIDG View Slide – Dave Iberson-Hurst

40 40 CDISC SHARE Model Benefits Summary Richer content Machine readable Layered / structured One definition used many times Linked together CDISC Standards Structured using BRIDG constructs to reflect the nature of the data

41 41 Project Plan

42 Present CDASH & SDTM definitions aligned  Implementation rules extracted  Metadata model agreed  CDISC SHARE Model tested  Scientific concepts & attributes mapping (In progress)

43 43 Content Mapping

44 44 Work Item Existing Domain New Domain Governance Use Cases Under Development Work Item Research Concept A simple addition to a code list Code List Addition of a Scientific Concept to a Domain Research Concept New Domain Code List Item  Slide: Dave Iberson-Hurst

45 45 CDISC Share Phase 1 – Functionality Requirements Users should be able to: – import & export content – manipulate metadata – access an electronic equivalent of a subset in PDF of SDTMIG v3.1.2, CDASH v 1.1, Controlled Terminology

46 46 Model Development Content Governance & User Interface Software Requirements Software MD Model Content Team Study Construction Concepts Governance Team User Interface Team Lab Team Model/Technology Team R1 High-Level Project Plan LAB Team to start soon!

47 47 Project Plan 6 subteams – Content – Governance – User Interface – Study Construction Concepts – Model/technology – Lab – to start soon One more team to be initiated to evaluate potential software tools soon.

48 48 Longer Term CDISC Share Development Plan Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 SDTM CDASH Oncology, Devices, TA (current) SEND and new TA ADaM and new TA new TA Major Development Phases Continuous smaller increments in content Continuing SW Releases (do not need to be aligned with Phases) Slide – Dave Iberson-Hurst

49 49 CDISC Share - Conclusion Precise definitions Rich metadata 24/7 access Linked to NCIt Links Clinical Research to Healthcare.

50 50 Active Participants Clyde Ulmer - FDA Erin Muhlbradt - NCI-EVS Fred Wood - Octagon Gary Walker - Quintiles Hanming Tu - Octagon Madhavi Vemuri – J & J Melissa Cook - Octagon Mike Riben – MD Anderson Diane Wold - GSK Simon Bishop - GSK Terry Hardin - Parexel Tsai Yiying - FDA Michael Morozewicz Barry Cohen - Octagon Dave Iberson-Hurst - Assero Rhonda Facile – NCI-EVS Chris Tolk - CDISC Dianne Reeves – NCI-CBIIT Julie Evans - CDISC Jian Chen – Edetek Carlo Radovsky – Etera Solutions Geoff Lowe – MEDIDATA Solutions Frederick Malfait – Roche Kerstin Forsberg – Astra Zeneca Kevin Burges – Formedix CDISC acknowledges all volunteers, their affiliated companies and the NCI-EVS for support of the CDISC Share project. Bold = team leaders

51 51 Questions about CDISC Share? Interested in joining a team? Contact Or visit: cdisc.org

52 52 Strength through collaboration. As a catalyst for productive collaboration, CDISC brings together individuals spanning the healthcare continuum to develop global, open, consensus-based medical research data standards....and sharing


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