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

– – – – – – – –

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

Healthcare and Mayo Clinic.

with representatives from CDISC, Intermountain  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)