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Current applications and future directions for the CDISC Operational Data Model standard: A methodological review.

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This review categorizes Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) research, revealing its broad application beyond initial use cases in clinical research data management and informatics.

Keywords:
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Area of Science:

  • Clinical Informatics
  • Health Data Standards
  • Research Methodology

Background:

  • Advancing Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) research requires understanding existing literature.
  • This paper presents a methodological review of ODM literature, developing a classification schema for its application across the clinical research data lifecycle.
  • The review identifies areas for future research and development to address ODM limitations and leverage its strengths in clinical research informatics.

Purpose of the Study:

  • To systematically review and classify existing research on the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM).
  • To develop a classification schema categorizing ODM applications within the clinical research data lifecycle.
  • To identify limitations, strengths, and future research directions for ODM in clinical research informatics.

Main Methods:

  • Systematic literature scan of multiple databases (ABI/Inform, ACM Digital, AIS eLibrary, Europe Central PubMed, Google Scholar, IEEE Xplore, PubMed, ScienceDirect).
  • Web of Science citation analysis performed using the search term "CDISC ODM."
  • Inclusion criteria focused on research examining ODM as an information system component or critically evaluating its usage scenarios, resulting in 69 articles after rigorous review.

Main Results:

  • ODM demonstrates flexibility, extending beyond original use cases to cover diverse data and metadata requirements in clinical research.
  • A classification schema was created, categorizing ODM literature into: EDC/EHR infrastructure, planning, data collection, data analysis, and study archival.
  • Identified opportunities for future ODM development include semantic alignment, end-to-end CDISC standard representation, real-time data exchange, EHR integration, and complete study design.

Conclusions:

  • ODM is widely used across the clinical research data lifecycle in ways not originally anticipated, serving as both a data exchange and metadata standard.
  • Significant literature addresses integrating Electronic Health Record (EHR) and clinical research data using ODM.
  • ODM's simplicity and readability have facilitated its broad adoption; maintaining a focused core model with extensions for edge cases enhances usability for developers.