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

2. Today’s Agenda Topic Time 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

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

4. concept to code mapping importance of Context queries
Victor Beraja, M.D.
Ibeza LLC, 2550 S Douglas Rd., Coral Gables, FL 33134
| Tel.: |
Copyright 2012
concept to code mapping importance of Context queries

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.

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.

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

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

9. Solution 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

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.

11. Solution 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

12. CMS vs. HITSP CDA

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..

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
Size of eRx in the US healthcare market
Size of APA in the US healthcare market .

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

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.

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

21. The End

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

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

24. Harmonized 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)
Glossary
FDA eSubmissions
Analysis and Reporting
FDA Critical Path
Initiative
Protocol
Study Design
Eligibility
Registration
Schedule
(PR Model)
Case Report Forms (CRF)
(CDASH)
Study Data
Lab Data
(LAB
and PGx)
Tabulated
CRF data
(SDTM)
Study Data
Lab Data
Study Design
Analysis Datasets
(ADaM) *
*Transport: CDISC ODM, SASXPT and/or HL7
CDISC 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

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.
2D to 3D world

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

28. Current 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.”
Relationship
Slide By: Dave Iberson-Hurst

29. CDISC SHARE VISION
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.
SHARE metadata is envisioned to help find, understand and use clinical data efficiently.

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
CDISC Metadata standards, extensions to those in the future

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. 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. 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. Semantic Interoperability
CDASH
Same?
SDTM
Ensure 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-Hurst 34

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. Which 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

37. Controlled Terminology
The CDISC SHARE MODEL
CDISC SHARE MODEL
SDTM Variables
CDASH Variables
Controlled Terminology
BRIDG Classes
21090 Data types
Here’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

38. Research Concept in Template Spreadsheet
The model template spreadsheets like this.

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

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. Project Plan

42. 2009 - 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 aligned
Implementation rules extracted from published standards
Metadata model developed and agreed
Model tested on SDTM observation classes
Scientific concepts and their attributes identified and (mind) mapped for each type of SDTM domain

43. Content Mapping
Current process
Transforming SDTM and CDASH into scientific concept structured metadata
All SDTM and CDASH domains and variables
BRIDG definitions
Controlled terminology
Data types
Also 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.

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

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

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. Longer Term CDISC Share Development Plan
Continuing SW Releases (do not need to be aligned with Phases)
Major Development Phases
Continuous smaller increments in content
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
Slide – Dave Iberson-Hurst

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

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

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

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