A dynamic reaction picklist for improving allergy reaction documentation in the electronic health record. (17th May 2020)

• Record Type: Journal Article
• Authors: Wang, Liqin; Blackley, Suzanne V; Blumenthal, Kimberly G; Yerneni, Sharmitha; Goss, Foster R; Lo, Ying-Chih; Shah, Sonam N; Ortega, Carlos A; Korach, Zfania Tom; Seger, Diane L; Zhou, Li
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 6(2020)
• Page Start: 917
• Page End: 923
• Publication Date: 2020-05-17
• Subjects: Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa042
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: 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.