A dynamic reaction picklist for improving allergy reaction documentation in the electronic health record Article Swipe
Liqin Wang
,
Suzanne V. Blackley
,
Kimberly G. Blumenthal
,
Sharmitha Yerneni
,
Foster Goss
,
Ying-Chih Lo
,
Sonam N. Shah
,
Carlos A. Ortega
,
Zfania Tom Korach
,
Diane L. Seger
,
Li Zhou
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocaa042
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocaa042
Objective Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, “dynamic” reaction picklist to improve allergy documentation in the electronic health record (EHR). Materials and Methods We split 3 decades of allergy entries in the EHR of a large Massachusetts healthcare system into development and validation datasets. We consolidated duplicate allergens and those with the same ingredients or allergen groups. We created a reaction value set via expert review of a previously developed value set and then applied natural language processing to reconcile reactions from structured and free-text entries. Three association rule-mining measures were used to develop a comprehensive reaction picklist dynamically ranked by allergen. The dynamic picklist was assessed using recall at top k suggested reactions, comparing performance to the static picklist. Results The modified reaction value set contained 490 reaction concepts. Among 4 234 327 allergy entries collected, 7463 unique consolidated allergens and 469 unique reactions were identified. Of the 3 dynamic reaction picklists developed, the 1 with the optimal ranking achieved recalls of 0.632, 0.763, and 0.822 at the top 5, 10, and 15, respectively, significantly outperforming the static reaction picklist ranked by reaction frequency. Conclusion The dynamic reaction picklist developed using EHR data and a statistical measure was superior to the static picklist and suggested proper reactions for allergy documentation. Further studies might evaluate the usability and impact on allergy documentation in the EHR.
Related Topics
Concepts
Documentation
Computer science
Usability
Set (abstract data type)
Ranking (information retrieval)
Electronic health record
Value (mathematics)
Drug reaction
Data mining
Medicine
Information retrieval
Machine learning
Health care
Programming language
Economic growth
Economics
Psychiatry
Drug
Human–computer interaction
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocaa042
- OA Status
- green
- Cited By
- 22
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3025835236
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3025835236Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jamia/ocaa042Digital Object Identifier
- Title
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A dynamic reaction picklist for improving allergy reaction documentation in the electronic health recordWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-03-25Full publication date if available
- Authors
-
Liqin Wang, Suzanne V. Blackley, Kimberly G. Blumenthal, Sharmitha Yerneni, Foster Goss, Ying-Chih Lo, Sonam N. Shah, Carlos A. Ortega, Zfania Tom Korach, Diane L. Seger, Li ZhouList of authors in order
- Landing page
-
https://doi.org/10.1093/jamia/ocaa042Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/7309236Direct OA link when available
- Concepts
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Documentation, Computer science, Usability, Set (abstract data type), Ranking (information retrieval), Electronic health record, Value (mathematics), Drug reaction, Data mining, Medicine, Information retrieval, Machine learning, Health care, Programming language, Economic growth, Economics, Psychiatry, Drug, Human–computer interactionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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22Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 3, 2022: 11, 2021: 4Per-year citation counts (last 5 years)
- References (count)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2022909531, https://openalex.org/W2295039456, https://openalex.org/W2404369708, https://openalex.org/W6715434201, https://openalex.org/W1155385988, https://openalex.org/W2008174801, https://openalex.org/W1952748834, https://openalex.org/W2412704047, https://openalex.org/W2101345720, https://openalex.org/W6674865229, https://openalex.org/W2303732155, https://openalex.org/W6712013915, https://openalex.org/W2262116908, https://openalex.org/W2414463764, https://openalex.org/W2092436690, https://openalex.org/W2772817631, https://openalex.org/W2034626816, https://openalex.org/W6713634263, https://openalex.org/W1527557040, https://openalex.org/W6631632166, https://openalex.org/W6715690665, https://openalex.org/W2550783702, https://openalex.org/W2070533721, https://openalex.org/W1565746575, https://openalex.org/W2396349321 |
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