Transcript Paper I: CSHARE CEMs Harmonization Slides

Harmonization of SHARPn Clinical
Element Models with CDISC SHARE
Clinical Study Data Standards
Guoqian Jiang, MD, PhD
Mayo Clinic
On behalf of CDISC CEMs
Harmonization Working Group
Acknowledgement
CDISC CEMs Harmonization WG
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Julie Evans, CDISC
Tom Oniki, IHC
Joey Coyle, IHC
Landen Bain, CDISC
Guoqian Jiang, Mayo Clinic
Stan Huff, IHC
Rebecca Kush, CDISC
Christopher Chute, Mayo Clinic
Introduction
The Intermountain Healthcare/GE Healthcare
Clinical Element Models (CEMs) have been
adopted by the SHARPn project for normalizing
patient data from electronic medical records
(EMRs).
To maximize the reusability of the CEMs in a
variety of use cases across both clinical study
and secondary use, it is necessary to build
interoperability between the CEMs and existing
data standards (e.g. CDISC and ISO 11179
standards).
Clinical Element Models
(CEMs)
The Clinical Element
Model presents a model
for describing and
representing detailed
clinical information.
CEM defines standard
data structure to capture
patient data. E.g.
BloodPressurePanel
CEM.
CDISC Standards
 CDASH - Clinical Data Acquisition Standards Harmonization
 SDTM - Study Data Tabulation Model
Objective
To harmonize the SHARPn CEMs with
CDISC SHARE clinical study data
standards.
As the starting point, we were focused on
three generic domains:
– Demographics
– Lab Tests
– Medications
Materials
CDISC contributed templates in the three
domains in Excel spreadsheets
– Demographics (DM)
– Lab Tests (LB)
– Concomitant Medication (CM)
And the SHARPn project provided three CEM
models:
– SecondaryUsePatient,
– SecondaryUseLabObs
– and SecondaryUseNotedDrug in XML Schema.
Methods
We formed a CSHARE CEMs
Harmonization Working Group with
representatives from CDISC, Intermountain
Healthcare and Mayo Clinic.
We performed a panel review on each data
element extracted from the CDISC templates
and SHARPn CEMs.
When a consensus is achieved, a data
element is classified into one of the following
three context categories: Common, Clinical
Study or Secondary Use.
Results
In total, we reviewed 127 data elements
from the CDISC SHARE templates and
1130 data elements extracted from the
SHARPn CEMs.
We identified 4 common data elements
(CDEs) from the Demographics domain,
20 CDEs from the Lab Tests domain and
15 CDEs from the Medications domain.
Demographics
Lab Tests
Medications
Outstanding Issues
 Differences in implementation
– Dose Form (--DOSFRM)
– Formulation.data.code
 Data types
– CDISC data types with mappings to ISO21090 (HL7?)
– CEM data types are a subset of HL7 data types with
extension
 Value set definition mechanism
– CDISC terminology defines standard codelists
– CEM value sets rely on external terminology services
(e.g. CTS2 value set definition services)
Conclusion
In conclusion, we have identified a set of
data elements that are common to the
context of both clinical study and
secondary use.
We consider that the outcomes produced
by this Working Group would be useful for
facilitating the semantic interoperability
between systems for both clinical study
and secondary use.
Future works
To discuss and analyze outstanding issues
– What do we do when CDISC has something we don’t
have? Do we automatically add it to the core? If not,
what are our criteria for adding/not adding?
– How do we harmonize value sets? Is it ok if one or
the other of us has a subset of the other? Do we
create “core” value sets that are supersets of what all
use cases need, just like we’re creating core models?
– What do we do about those “differences in
implementation”?
– How do we see this mapping being used now?
Future works
To expand harmonization efforts to more
other domains
To foster requirements on building a
collaborative platform for supporting the
harmonization
To author the CDISC clinical study data
models using the CEM formalisms (e.g.
CDL or ADL)
References
http://informatics.mayo.edu/sharp/opence
m/index.php/Main_Page
(csharecems/sharpn)