{"doi":"10.1093/jamia/ocaa042","title":"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.","journal":"Journal of the American Medical Informatics Association","year":2020,"id":103131,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":25,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6376,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":503877,"name":"Suzanne V. Blackley","orcid":null,"position":1,"is_corresponding":false},{"id":227967,"name":"Kimberly G. Blumenthal","orcid":"0000-0003-4773-9817","position":2,"is_corresponding":false},{"id":474255,"name":"Sharmitha Yerneni","orcid":null,"position":3,"is_corresponding":false},{"id":502869,"name":"Foster Goss","orcid":"0000-0002-7939-7885","position":4,"is_corresponding":false},{"id":502870,"name":"Ying-Chih Lo","orcid":"0000-0001-6538-842X","position":5,"is_corresponding":false},{"id":502871,"name":"Sonam N. Shah","orcid":"0000-0002-2222-895X","position":6,"is_corresponding":false},{"id":502872,"name":"Carlos A. Ortega","orcid":"0000-0002-2394-8975","position":7,"is_corresponding":false},{"id":503878,"name":"Zfania Tom Korach","orcid":null,"position":8,"is_corresponding":false},{"id":502873,"name":"Diane L. Seger","orcid":"0000-0001-9924-378X","position":9,"is_corresponding":false},{"id":284373,"name":"Li Zhou","orcid":"0000-0003-3874-4833","position":10,"is_corresponding":false},{"id":86645,"name":"Liqin Wang","orcid":"0000-0002-8280-8892","position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":null,"created_at":"2026-07-18T22:42:17.920911Z","pmid":"32417930","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}