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Standard information models for representing adverse sensitivity information in clinical documents

Lookup NU author(s): Professor Sarah SlightORCiD


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© Schattauer 2016. Background: Adverse sensitivity (e.g., allergy and intolerance) information is a critical component of any electronic health record system. While several standards exist for structured entry of adverse sensitivity information, many clinicians record this data as free text. Objectives: This study aimed to 1) identify and compare the existing common adverse sensitivity information models, and 2) to evaluate the coverage of the adverse sensitivity information models for representing allergy information on a subset of inpatient and outpatient adverse sensitivity clinical notes. Methods: We compared four common adverse sensitivity information models: Health Level 7 Allergy and Intolerance Domain Analysis Model, HL7-DAM; the Fast Healthcare Interoperability Resources, FHIR; the Consolidated Continuity of Care Document, C-CDA; and OpenEHR, and evaluated their coverage on a corpus of inpatient and outpatient notes (n = 120). Results: We found that allergy specialists’ notes had the highest frequency of adverse sensitivity attributes per note, whereas emergency department notes had the fewest attributes. Overall, the models had many similarities in the central attributes which covered between 75% and 95% of adverse sensitivity information contained within the notes. However, representations of some attributes (especially the value-sets) were not well aligned between the models, which is likely to present an obstacle for achieving data interoperability. Also, adverse sensitivity exceptions were not well represented among the information models. Conclusions: Although we found that common adverse sensitivity models cover a significant portion of relevant information in the clinical notes, our results highlight areas needed to be reconciled between the standards for data interoperability.

Publication metadata

Author(s): Topaz M, Seger DL, Goss F, Lai K, Slight SP, Lau JJ, Nandigam H, Zhou L

Publication type: Article

Publication status: Published

Journal: Methods of Information in Medicine

Year: 2016

Volume: 55

Issue: 2

Pages: 151-157

Online publication date: 24/02/2016

Acceptance date: 30/01/2016

ISSN (print): 0026-1270

Publisher: Schattauer GmbH


DOI: 10.3414/ME15-01-0081

PubMed id: 26905461


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