Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases

Large electronic databases of health care information, such as administrative claims and electronic health records, are available and are being used in a number of public health settings, including drug safety surveillance. However, because of a lack of standardization, clinical terminologies may differ across databases. With the aid of existing resources and expert coders, we have developed mapping tables to convert ICD-9-CM diagnosis codes used in some existing databases to SNOMED-CT and MedDRA. In addition, previously developed definitions for specific health outcomes of interest were mapped to the same standardized vocabularies. We evaluated how vocabulary mapping affected (1) the retention of clinical data from two test databases, (2) the semantic space of outcome definitions, (3) the prevalence of each outcome in the test databases, and (4) the reliability of analytic methods designed to detect drug-outcome associations in the test databases. Although vocabulary mapping affected the semantic space of some outcome definitions, as well as the prevalence of some outcomes in the test databases, it had only minor effects on the analysis of drug-outcome associations. Furthermore, both SNOMED-CT and MedDRA were viable for use as standardized vocabularies in systems designed to perform active medical product surveillance using disparate sources of observational data.

[1]  Gary H. Merrill,et al.  Inter-translation of Biomedical Coding Schemes Using UMLS , 2006, AAAI Fall Symposium: Semantic Web for Collaborative Knowledge Acquisition.

[2]  Don E. Detmer,et al.  White Paper: Advancing the Framework: Use of Health Data - A Report of a Working Conference of the American Medical Informatics Association , 2008, J. Am. Medical Informatics Assoc..

[3]  Richard Platt,et al.  The U.S. Food and Drug Administration's Mini‐Sentinel Program , 2012 .

[4]  Natalia Grabar,et al.  Grouping pharmacovigilance terms with semantic distance , 2011, MIE.

[5]  J. Avorn,et al.  High-dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data , 2009, Epidemiology.

[6]  Charles Safran,et al.  Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. , 2007, Journal of the American Medical Informatics Association : JAMIA.

[7]  Edward H. Shortliffe,et al.  Viewpoint: The Unified Medical Language System: Toward a Collaborative Approach for Solving Terminologic Problems , 1998, J. Am. Medical Informatics Assoc..

[8]  Patrick B. Ryan,et al.  Health Outcomes of Interest in Observational Data: Issues in Identifying Definitions in the Literature , 2012 .

[9]  M. Schuemie,et al.  Combining electronic healthcare databases in Europe to allow for large‐scale drug safety monitoring: the EU‐ADR Project , 2011, Pharmacoepidemiology and drug safety.

[10]  S. Fenton,et al.  SNOMED CT survey: an assessment of implementation in EMR/EHR applications. , 2008, Perspectives in health information management.

[11]  J. Overhage,et al.  Advancing the Science for Active Surveillance: Rationale and Design for the Observational Medical Outcomes Partnership , 2010, Annals of Internal Medicine.

[12]  Prakash M. Nadkarni,et al.  Migrating existing clinical content from ICD-9 to SNOMED , 2010, J. Am. Medical Informatics Assoc..

[13]  Robert L Davis,et al.  Real-Time Vaccine Safety Surveillance for the Early Detection of Adverse Events , 2007, Medical care.

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

[15]  Manisha Mantri,et al.  Unified Medical Language System , 2013 .

[16]  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..

[17]  Betsy L. Humphreys,et al.  Technical Milestone: The Unified Medical Language System: An Informatics Research Collaboration , 1998, J. Am. Medical Informatics Assoc..

[18]  Olivier Bodenreider Using SNOMED CT in combination with MedDRA for reporting signal detection and adverse drug reactions reporting , 2009, AMIA.