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Methods Inf Med 2016; 55(02): 151-157
DOI: 10.3414/ME15-01-0081
Original Articles
Schattauer GmbH

Standard Information Models for Representing Adverse Sensitivity Information in Clinical Documents

M. Topaz
1   General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
2   Harvard Medical School, Boston, MA, USA
,
D. L. Seger
1   General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
3   Clinical and Quality Analysis, Partners Healthcare System, Wellesley, MA, USA
,
F. Goss
4   Department of Emergency Medicine, University of Colorado, Aurora, CO, USA
,
K. Lai
3   Clinical and Quality Analysis, Partners Healthcare System, Wellesley, MA, USA
,
S. P. Slight
5   Division of Pharmacy, School of Medicines, Pharmacy and Health, Durham University, Durham, UK
,
J. J. Lau
1   General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
,
H. Nandigam
1   General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
2   Harvard Medical School, Boston, MA, USA
3   Clinical and Quality Analysis, Partners Healthcare System, Wellesley, MA, USA
,
L. Zhou
1   General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA, USA
2   Harvard Medical School, Boston, MA, USA
3   Clinical and Quality Analysis, Partners Healthcare System, Wellesley, MA, USA
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Publikationsverlauf

received: 02. Mai 2015

accepted: 30. Januar 2016

Publikationsdatum:
08. Januar 2018 (online)

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Summary

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 Health-care Interoperability Resources, FHIR; the Consolidated Continuity of Care Document, C-CDA; and OpenEHR, and evaluated their coverage on a corpus of inpatient and out-patient 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 stand -ards for data interoperability.

Keywords

Allergy - hypersensitivity - electronic health records - health information systems - Health Level 7