A dynamic reaction picklist for improving allergy reaction documentation in the electronic health record

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.

Heterogeneity of Drug Allergies and Reaction Lists in Two U.S. Health Care Systems' Electronic Health Records

“…Further, free-text comments are encouraged for complete documentation by the Allergy Clinical Consensus Group within BWH's health care system. 17 Along this line, a drug allergy practice parameter-developed by the American Academy of Allergy, Asthma and Immunology, the American College of Allergy, Asthma and Immunology, and the Joint Council of Allergy, Asthma and Immunology-advises that a relevant drug-allergy history should include ample details such as the symptoms' timing, onset, duration, relationship with medication use while also discussing the history of previous reactions, and their management. 17 Documenting these details requires documentation beyond Epic's coded fields.…”

“…also discussing history of previous reactions and their management. 17 Documenting these details requires documentation beyond Epic's coded fields. Furthermore, it is critical to consider the balance between documenting enough details to guide proper management and doing so in a means that is easy enough for patients to fill out and without contributing to clinician burnout.…”

PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes

“…Similarly, we also used modifier modules to identify experiencers other than the patient (e.g., mother, father, wife, husband, son, daughter), patient history, and allergic reactions for which we used an allergen lexicon to identify and exclude symptoms mentioned in the context of drug-induced adverse events, such as drug allergic reactions (e.g., ‘ACE Inhibitors Angioedema’, ‘Metformin GI Upset’). [33] Additionally, we used a pattern-matching approach to identify sentences in which symptoms were mentioned as part of risk factors, side effects, instructions, and survey questions. The NLP rules were iteratively added or refined during the lexicon development stage to optimize NLP performance.…”

Development of a Post-Acute Sequelae of COVID-19 (PASC) Symptom Lexicon Using Electronic Health Record Clinical Notes

“…We used an allergen lexicon to identify and exclude symptoms mentioned in the context of drug-induced adverse events, such as drug allergic reactions (e.g., 'ACE Inhibitors Angioedema', 'Metformin GI Upset'). 31 Finally, All rights reserved. No reuse allowed without permission.…”

“…We used an allergen lexicon to identify and exclude symptoms mentioned in the context of drug-induced adverse events, such as drug allergic reactions (e.g., 'ACE Inhibitors Angioedema', 'Metformin GI Upset'). 31 Finally, we used a pattern-matching approach to identify sentences in which symptoms were mentioned as part of risk factors, side effects, instructions, and survey questions. The NLP rules were iteratively added or refined during the lexicon development stage to optimize NLP performance.…”