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

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.

Keywords: adverse drug event; adverse drug reaction; clinical decision support; clinical decision support system; drug allergy; free-text; natural language processing; text-mining.

FIGURE 1

On the left is a graphical representation of the EHR including the different modules. The free‐text notes included from the different modules are marked grey. On the right is an example of a free‐text EHR note with two potential ADRs

FIGURE 2

Inclusion and exclusion of potential ADRs. pADRs, potential adverse drug reactions; reADRs, recurrent adverse drug reacions

FIGURE 3

Venn diagram presenting unique adverse drug reactions (ADRs). The blue circle (n = 318), including the green portion, represents the total number of unique ADRs identified by the manual electronic health record (EHR) review. The red circle (n = 556) including the green and yellow portions represents the total number of unique ADRs identified by the rule‐based EHR review (true positives + false positives). The red section (n = 377) represents the false positives. The green section (n = 179) represents the number of true positives. The yellow circle represents ADRs found only by the rule‐based EHR review