Specification of a Cardiovascular Metadata Model: A Consensus StandardRebecca Wilgus, RN MSN1; Dana Pinchotti2; Salvatore Mungal3; David F Kong, MD AM1; James E Tcheng, MD1; Brian McCourt1
1Duke Clinical Research Institute, Durham NC2American College of Cardiology Foundation, Washington DC3Duke Cancer Center, Durham NC
Current Landscape
Clinical Data
Research
Clinical
Decision
Support Public & Population
Health
Quality
Measurement & Patient
Safety
Business,
Operations/
Administration
Don Mon, AHIMA
Where we need to be…
Patient
Clinician
Healthcare Data
SystemsPatient care
Quality Improvement
Research
Reimbursement
Post Marketing Safety
Decision Support
Administration & Mgt.
Public Health Reporting
…
Data Uses
Single Source Multiple Uses
McCourt, B. 26SEP2011
CV DAM Project Team
Data Standards Development Methodology
1. Agree on fundamental principles2. Explicitly define scope3. Identify, prioritize adoption of prior work, ID
gaps4. Prepare materials for review
Aggregate, filter, normalize5. Clinical expert review
Adopt, harmonize, then author6. Metadata annotation7. Consensus & Publication8. Stewardship & Maintenance
Nahm, M. & McCourt, B. 26SEP2011
Methodology Rationale
Strong project management Structured, coordinated processes Proven track-record
Nahm, M. & McCourt, B. 26SEP2011
Fundamental Principles for Cardiology Data Standards
Each release will expand the content or extend the applicable use cases of prior efforts
Clinical data elements with harmonized, consensus definitions
Technical representation for multiple standards CDISC SDTM HL7 RIM Terminologies (i.e., SNOMED-CT)
Published metadata Vocabularies (NCI EVS, caDSR)
Scope of the CV DAMCardiovascular Data
Car
diov
erte
rdef
ibpr
oced
ures
(ICD
Reg
istr
y)C
onge
nita
l Hea
rt C
ondi
tions
(IMPA
CT)
Con
com
itant
Med
icat
ions
Dem
ogra
phic
s
Vita
l Sig
ns
Adv
erse
Eve
nts
STEM
I/NST
EMI
(AC
TIO
N)
Top
100
EHR
dat
a el
emen
ts
Car
diac
Cat
han
d PC
I(C
athP
CI
Car
otid
Art
ery
Sten
ting
and
Enda
rter
ecto
my
(CA
RE)
Non-specialty data Common cardiovascular clinical observations - Sub-specialty domains
18 to
tal d
omai
ns a
nd g
row
ing
Cardiac ImagingACC /AHA/STS
*NCRI GrantACC/AHA/STS registriesCDISC
Car
diac
CT
Car
diac
MR
Echo
card
iogr
aphy
Nuc
lear
Car
diol
ogy
AC
S H
isto
ry &
Sym
ptom
s
CTNBP FDA
CV Clinical- Data Elements- Event definitions- Clinical terminology and
data definitions
CDISC- SDTM standard for FDA submission- Controlled Terminology alignment- CRF templates- Stds adoption by researchers
HL7- Mappings to HL7 standards- Adoption support for EHR’s- CCHIT EHR Certification (future)
CV DAM R1CV DAM R2CV DAM R3
Future
CV
Out
com
es
TIA
/ Str
oke
*National Cardiovascular Research Infrastructure
Clinical Data Element Development Process
3. Select or author definitions (incl. valid values)
1. Identify DE Sources 2. Aggregate, align
for review
Data Standards Workgroup
4. Annotate with vocabulary, relationships and mapping to technical representations
6. Public comment & ballot
5. Iterate until clean
7. Publish8. Maintain
Domain Experts Informatics Experts
Anatomy of a Data Element
Tagged Values Name: HL7 RIM Value:RIM Mapping: observation.value Condition: Where observation.code = "heart failure class"
Tagged Values Name: CDISC SDTM CDISC SDTM: FA.FATESTCD = HFCLASS, FA.FATEST = Heart Failure Class, FA.OBJ = Heart Failure WHERE MH.TERM = Heart Failure
Tagged Values Name: CADSR Local Value Domain Value: NYHAClassType
Tagged Values: Name: Property Concept Code Value C-66909
Tagged Value: PropertyConceptPreferredName Value NYHA Class I (C66904)
Tagged Value: Name: PropertyQualifierConceptCode Value NYHA Class II (C66905)
Tagged Value: Name PropertyConceptCide
Heart Failure \ NYHA ClassClass
Attribute Name
Alias
Citation
Coding Instructions
Permissible values
Definitions
Representation Maps
Nahm, M. & McCourt, B. 26SEP2011
Data Type
CV DAM
Components of the CV DAM:
Use Cases Activity Diagrams UML model Context Document
Project description Workflows & processes Conventions utilized &
rationale Guidelines for interpretation
Use Cases
Use cases
Activity Diagrams
Activity Diagrams
UML Models
UML Model
Leverages the UML formalism for representation ofdata elements
Provides great flexibility and extensibility for metadata to be captured for each data element CDISC SDTM HL7 RIM Select SNOMED-CT mappings Concept codes from NCI EVS & caDSR
CV DAM UML Model Requirements
1. Intuitive for domain experts (clinicians) & technologists 2. Easy to update and maintain 3. Supports multiple diagrams reusing the same data elements4. Supports metadata for multiple implementations5. Supports complex relationships that exist among clinical data elements6. Supports the capture of metadata at the class and attribute level7. Complies with the NCI’s modeling guidelines
Data Element Library
Advantages of the Atomic Class Model:
Characteristics of a ‘Well Modeled’ Data Element: Data element is essential to the collection or evaluation of data within
the use cases
Data element is broadly applicable or is so critical that it’s absence will bring into question the integrity of the standard
Data element has a consensus definition. Consensus means that most WG members have researched, discussed and agreed on the recommended definition, data type, and permissible values. Notes from these discussions are documented.
Data type is specified
Permissible values (response options) are identified and defined
Bibliographic citations are provided or the WG noted they authored the definition
Characteristics of a ‘Well Modeled’ Data Element: Data elements (and permissible values) have been decomposed into
atomic, clinical concepts
Atomic concepts have been matched with terms in NCI EVS or new concepts with draft definitions are provided.
Technical representations for standards of interest are specified (i.e., HL7 RIM or CDISC SDTM
A minimal set of associations of importance to the domain are identified. Associations provide context for the data element (i.e., …is an ‘indication for’, a ‘finding about’, a ‘complication of’). Tells others how or where the domain experts think the data element ‘fits’ in.
Data elements that are derived from other data elements (i.e., max SBP) are identified and the data elements needed for the derivation are fully developed
349 Attributes 166 Atomic Classes 18 Parent Classes 4 Packages
Structure of the UML Model
Details of the UML Model
CV DAM Reports
Limitations of the DAM
Tools to develop and represent content have limitations
Competing philosophies regarding representation of data standards
Terminologies often lack precise clinical definitions that are applicable across use cases
Functional models exist for each system – a consistent set of requirements that are common across all models are lacking
Advantages of the DAM
Precisely defined, consensus, clinical content Common platform of clinical requirements Machine readable links Includes metadata and technical representations
that inform multiple implementations Publically reviewed and vetted
Future Uses of CV DAM
McCourt, B. 10MAY2011
HL7:
CV EHR Functional Profile
RIM-derived Messages
Structured Reports
CDISC SDTM:
FDA Clinical Trials Data Warehouse
Future Content Development Areas
CV Imaging (in progress) FDA CV Endpoints Women’s Heart Disease
Thank-you!
NCRI Principal InvestigatorsRobert Harrington, MD, FACC DukeEric Peterson, MD, FACC DukeJohn Rumsfeld, MD, FACC ACCF
National Heart, Lung, and Blood Institute grant: 1RC2HL101512-01
NCRI Principal InvestigatorsRobert Harrington, MD, FACC DukeEric Peterson, MD, FACC DukeJohn Rumsfeld, MD, FACC ACCF
National Heart, Lung, and Blood Institute grant: 1RC2HL101512-01
ACC Governance Work Group H. Vernon Anderson, MD, FACCMark Kremers, MD, FACCMartha Radford, MD, FACCMatthew Roe, MD, FACCRichard Shaw, PhD, FACCJames Tcheng, MD, FACCWilliam Weintraub, MD, FACC
ACC Governance Work Group H. Vernon Anderson, MD, FACCMark Kremers, MD, FACCMartha Radford, MD, FACCMatthew Roe, MD, FACCRichard Shaw, PhD, FACCJames Tcheng, MD, FACCWilliam Weintraub, MD, FACC
Thank-you!
Questions?
Rebecca Wilgus: [email protected] Brian McCourt: [email protected] Dana Pinchotti: [email protected]