Query Health Concept-to-Codes
(C2C) SWG
Meeting #5
January 10, 2012
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Today’s Agenda
Topic
Time Allotted
Quick Review of Updated Timeline and Future Meeting Times
2:30 – 2:35
Presentation by Subject Matter Experts
2
Victor Beraja - Ibeza
2:35 - 3:15
Rhonda Fascile – CDISC SHARE
3:15 – 4:00
Proposed Timeline
Meeting times extended from 2:30-4:00pm
TODAY
Meeting 2 –
Meeting 3 –
Meeting 4 –
Meeting 5 –
Meeting 6 –
Meeting 7 –
Meeting 8 –
Meeting 9 –
Meeting 10 –
Dec 13
Dec 20
Jan 03
Jan 10
Jan 17
Jan 24
Jan 31
Feb 7
Feb 14
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)
Presentation
•hQuery
•i2b2
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
Coordinate offline activities to summarize approaches and develop draft deliverable from presentations
3
Tasks
•Consensus
Voting
Finalized
Victor Beraja, M.D.
Ibeza LLC, 2550 S Douglas Rd., Coral Gables, FL 33134
victor@ibeza.net | Tel.: 305-357-1711 | www.ibeza.net
Copyright 2012
CONCEPT TO CODE MAPPING
IMPORTANCE OF CONTEXT QUERIES
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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.
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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.
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Concepts to Codes
• Each clinical data concept is mapped to
SNOMED and/or LOINC as available within
a structure that provides context
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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 99204 (office visit) and ICD 366.12
(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
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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
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Problem #2 w Present Queries
• Both patient groups billed
with ICD 362.07 (Diabetic
Macular Edema) Do they
have edema?
Answer: Maybe.
• The Justification for the
test was macular edema.
– Group 1: No edema
– Group 2: Edema.
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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
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CMS vs. HITSP CDA
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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..
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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
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
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Summary
• Context Searches can be accomplished by
incorporating Sections and Subsections into
HL7-CDA
• Context Searches yield accurate queries
with primary source information
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.
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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
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The End
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Query Health Concept to Codes
Teleconference
January 10, 2012
CDISC SHARE Project Overview
Rhonda Facile, CDISC
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CDISC SHARE
• Background
• Vision and Goals
• Project Plan
–Where we are today
–Next Steps
• Acknowledgements
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Global Content Standards for Clinical Research
(Protocol-driven Research; Protocol Reporting)
Harmonized through BRIDG Model**
Controlled Terminology (NCI-EVS)
FDA eSubmissions
Glossary
FDA Critical Path
Analysis and Reporting
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)
** CDISC, ISO, HL7 Standard
*Transport: CDISC ODM, SASXPT and/or HL7
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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.
25
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
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Compliance Issues – 1 example
Slide By: Ellen Pinnow, MS Health Programs Coordinator FDA,
Office of Women’s Health, Slide from 2006 CDISC US Interchange
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Current 2D World
Relationship
Relationship
Relationship
Slide By: Dave Iberson-Hurst
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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.
http://www.cdisc.org/cdisc-share
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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
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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)
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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
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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 21090 Standard – detailed data types to
facilitate machine readability and transport.
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Semantic Interoperability
Same?
Slide – Dave Iberson-Hurst
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34
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• 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 21090 compliant, HL7 alignment, RIM
alignment
http://www.cdisc.org/bridg
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Which Metadata Model?
SDTM
Intermountain
Open eHR
Slide – Dave Iberson-Hurst
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The CDISC SHARE MODEL
CDISC SHARE MODEL
BRIDG Classes
21090 Data types
SDTM Variables
CDASH Variables
Controlled Terminology
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Research Concept in Template
Spreadsheet
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Transport & Protection of the Content
SDTM
ADaM
CDASH
View
View
View
View
SHARE
Scientific Concepts
(BRIDG, Terminology, Data Types...)
BRIDG
XML V3
Message(s)
XML
Format(s)
Tabular
Form
Slide – Dave Iberson-Hurst
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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
40
Project Plan
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2009 - Present
CDASH & SDTM definitions aligned

Implementation rules extracted

Metadata model agreed

CDISC SHARE Model tested

Scientific concepts & attributes mapping
(In progress)
42
Content Mapping
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Governance Use Cases Under Development
A simple addition to a code list
Work Item
Research
Concept
Code List
Code List Item

Addition of a Scientific Concept to a Domain
Work Item
Research
Concept
Research
Research
Concept
Research
Concept
Concept
Existing Domain
New Domain
Work Item
Research
Research
Concept
Research
Concept
Research
Concept
Concept
New Domain
Slide: Dave Iberson-Hurst
44
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
45
Model Development
MD
Model
High-Level Project Plan
Model/Technology Team
Content
Content Team
LAB Team
to start soon!
Study Construction Concepts
Lab Team
Governance & User Interface
Governance Team
User Interface Team
Software Requirements
Software
R1
46
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.
47
Longer Term CDISC Share
Development Plan
Major Development Phases
Continuing SW Releases
(do not need to be aligned with Phases)
Phase 4
Continuous
Oncology, Devices,
smaller
TA (current)
increments in
SEND and new TA
content
Phase 5
Phase 3
Phase 2
CDASH
Phase 1
SDTM
ADaM and new TA
new TA
Slide – Dave Iberson-Hurst
48
CDISC Share - Conclusion
•
•
•
•
•
Precise definitions
Rich metadata
24/7 access
Linked to NCIt
Links Clinical Research to
Healthcare.
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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
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Questions about CDISC Share?
Interested in joining a team?
Contact
rfacile@cdisc.org
dave.iberson-hurst@assero.co.uk
jevans@cdisc.org
Or visit: cdisc.org
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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.
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