A Realism-Based View on Counts in OMOP's Common Data Model

Correctly counting entities is a requirement for analytics tools to function appropriately. The Observational Medical Outcomes Partnership's (OMOP) Common Data Model (CDM) specifications were examined to assess the extent to which counting in OMOP CDM compatible data repositories would work as expected. To that end, constructs (tables, fields and attributes) defined in the OMOP CDM as well as cardinality constraints and other business rules found in its documentation and related literature were compared to the types of entities and axioms proposed in realism-based ontologies. It was found that not only the model itself, but also a proposed standard algorithm for computing condition eras may lead to erroneous counting of several sorts of entities.

[1]  Werner Ceusters,et al.  Negative findings in electronic health records and biomedical ontologies: A realist approach , 2007, Int. J. Medical Informatics.

[2]  Robert Arp,et al.  Building Ontologies with Basic Formal Ontology , 2015 .

[3]  Werner Ceusters,et al.  Ontological Foundations for Tracking Data Quality through the Internet of Things. , 2016, Studies in health technology and informatics.

[4]  Robert M Califf,et al.  The Patient-Centered Outcomes Research Network , 2014, North Carolina Medical Journal.

[5]  Patrick B. Ryan,et al.  Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases , 2010, J. Am. Medical Informatics Assoc..

[6]  Hyeon-Eui Kim,et al.  Identifying Appropriate Reference Data Models for Comparative Effectiveness Research (CER) Studies Based on Data from Clinical Information Systems , 2013, Medical care.

[7]  Guilherme Del Fiol,et al.  Evaluating common data models for use with a longitudinal community registry , 2016, J. Biomed. Informatics.

[8]  James J. Cimino,et al.  Desiderata for Healthcare Integrated Data Repositories Based on Architectural Comparison of Three Public Repositories , 2013, AMIA.

[9]  Jessica A. Turner,et al.  The Ontology for Biomedical Investigations , 2016, PloS one.

[10]  Werner Ceusters,et al.  Ontological realism: A methodology for coordinated evolution of scientific ontologies , 2010, Appl. Ontology.

[11]  J. Cimino.  Desiderata for Controlled Medical Vocabularies in the Twenty-First Century , 1998, Methods of Information in Medicine.

[12]  Roy Pardee,et al.  The HMO Research Network Virtual Data Warehouse: A Public Data Model to Support Collaboration , 2014, EGEMS.

[13]  Patrick B. Ryan,et al.  Validation of a common data model for active safety surveillance research , 2012, J. Am. Medical Informatics Assoc..

[14]  M E Matheny,et al.  Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership , 2015, Applied Clinical Informatics.

[15]  George Hripcsak,et al.  Caveats for the use of operational electronic health record data in comparative effectiveness research. , 2013, Medical care.

[16]  Peter R. Rijnbeek,et al.  Converting to a Common Data Model: What is Lost in Translation? , 2014, Drug Safety.

[17]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[18]  A. Rector,et al.  Relations in biomedical ontologies , 2005, Genome Biology.

[19]  Martijn J. Schuemie,et al.  Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research , 2016, Healthcare informatics research.

[20]  Werner Ceusters,et al.  Strategies for referent tracking in electronic health records , 2006, J. Biomed. Informatics.