Abstract
BackgroundMedication errors have been identified as the most common preventable cause of adverse events. The lack of granularity in medication error terminology has led pharmacovigilance experts to rely on information in individual case safety reports’ (ICSRs) codes and narratives for signal detection, which is both time consuming and labour intensive. Thus, there is a need for complementary methods for the detection of medication errors from ICSRs. The aim of this study is to evaluate the utility of two natural language processing text mining methods as complementary tools to the traditional approach followed by pharmacovigilance experts for medication error signal detection.MethodsThe safety surveillance advisor (SSA) method, I2E text mining and University of Copenhagen Center for Protein Research (CPR) text mining, were evaluated for their ability to extract cases containing a type of medication error where patients extracted insulin from a prefilled pen or cartridge by a syringe. A total of 154,209 ICSRs were retrieved from Novo Nordisk’s safety database from January 1987 to February 2018. Each method was evaluated by recall (sensitivity) and precision (positive predictive value).ResultsWe manually annotated 2533 ICSRs to investigate whether these contained the sought medication error. All these ICSRs were then analysed using the three methods. The recall was 90.4, 88.1 and 78.5% for the CPR text mining, the SSA method and the I2E text mining, respectively. Precision was low for all three methods ranging from 3.4% for the SSA method to 1.9 and 1.6% for the CPR and I2E text mining methods, respectively.ConclusionsText mining methods can, with advantage, be used for the detection of complex signals relying on information found in unstructured text (e.g., ICSR narratives) as standardised and both less labour-intensive and time-consuming methods compared to traditional pharmacovigilance methods. The employment of text mining in pharmacovigilance need not be limited to the surveillance of potential medication errors but can be used for the ongoing regulatory requests, e.g., obligations in risk management plans and may thus be utilised broadly for signal detection and ongoing surveillance activities.
Highlights
Medication errors have been identified as the most common preventable cause of adverse events
Text mining methods can, with advantage, be used for the detection of complex signals relying on information found in unstructured text (e.g., Individual case safety report (ICSR) narratives) as standardised and both less labour-intensive and time-consuming methods compared to traditional pharmacovigilance methods
The employment of text mining in pharmacovigilance need not be limited to the surveillance of potential medication errors but can be used for the ongoing regulatory requests, e.g., obligations in risk management plans and may be utilised broadly for signal detection and ongoing surveillance activities
Summary
Medication errors have been identified as the most common preventable cause of adverse events. In an effort to prevent patient harm due to medication errors, various stakeholders such as the World Health Organization (WHO), the European Medicines Agency (EMA) and the Food and Drug Administration (FDA) have created guidelines and imposed legislations aiming at improving the reporting and detection of medication errors [3,4,5,6,7]. One of these mitigation efforts is the EU pharmacovigilance legislation from 2012. The legislation requires that all suspected serious and non-serious adverse reactions associated with medication errors must be reported by national competent authorities and marketing authorisation holders to the EudraVigilance system, which is the EMA’s database for safety monitoring of medicinal products [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
