Identifying adverse drug reactions from free-text electronic hospital health record notes

Arthur Wasylewicz, Britt van de Burgt, Aniek Weterings, Naomi Jessurun, Erik Korsten, Toine Egberts, Arthur Bouwman, Marieke Kerskes, René Grouls, Carolien van der Linden

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

BACKGROUND: Adverse drug reactions (ADRs) are estimated to be the fifth cause of hospital death. Up to 50% are potentially preventable and a significant number are recurrent (reADRs). Clinical decision support systems have been used to prevent reADRs using structured reporting concerning the patient's ADR experience, which in current clinical practice is poorly performed. Identifying ADRs directly from free text in electronic health records (EHRs) could circumvent this.

AIM: To develop strategies to identify ADRs from free-text notes in electronic hospital health records.

METHODS: In stage I, the EHRs of 10 patients were reviewed to establish strategies for identifying ADRs. In stage II, complete EHR histories of 45 patients were reviewed for ADRs and compared to the strategies programmed into a rule-based model. ADRs were classified using MedDRA and included in the study if the Naranjo causality score was ≥1. Seriousness was assessed using the European Medicine Agency's important medical event list.

RESULTS: In stage I, two main search strategies were identified: keywords indicating an ADR and specific prepositions followed by medication names. In stage II, the EHRs contained a median of 7.4 (range 0.01-18) years of medical history covering over 35 000 notes. A total of 318 unique ADRs were identified of which 63 were potentially serious and 179 (sensitivity 57%) were identified by the rule. The method falsely identified 377 ADRs (positive predictive value 32%). However, it also identified an additional eight ADRs.

CONCLUSION: Two key strategies were developed to identify ADRs from hospital EHRs using free-text notes. The results appear promising and warrant further study.

Original languageEnglish
Pages (from-to)1235-1245
Number of pages11
JournalBritish Journal of Clinical Pharmacology
Volume88
Issue number3
Early online date1 Sept 2021
DOIs
Publication statusPublished - Mar 2022

Keywords

  • adverse drug event
  • adverse drug reaction
  • clinical decision support
  • clinical decision support system
  • drug allergy
  • free-text
  • natural language processing
  • text-mining

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