Standard Information Models for Representing Adverse Sensitivity Information in Clinical Documents
| Autor: | Li Zhou, Diane L. Seger, Sarah P. Slight, Maxim Topaz, H. Nandigam, K. Lai, Foster R. Goss, J. J. Lau |
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| Rok vydání: | 2016 |
| Předmět: |
020205 medical informatics
Interoperability Health Informatics Documentation 02 engineering and technology computer.software_genre Health informatics 03 medical and health sciences 0302 clinical medicine Health Information Management Continuity of Care Document Health care 0202 electrical engineering electronic engineering information engineering medicine Electronic Health Records 030212 general & internal medicine Advanced and Specialized Nursing business.industry Emergency department computer.file_format Models Theoretical Reference Standards medicine.disease openEHR Information model Medical emergency Data mining business computer |
| Zdroj: | Methods of Information in Medicine. 55:151-157 |
| ISSN: | 2511-705X 0026-1270 |
| Popis: | SummaryBackground: 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. |
| Databáze: | OpenAIRE |
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