Implementation and comparison of two text mining methods with a standard pharmacovigilance method for signal detection of medication errors.

BACKGROUND: Medication 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. METHODS: The 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). RESULTS: We 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. CONCLUSIONS: Text 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.

[The National List of recommendations-- what is it based on?]. / National Rekommandationsliste--hvad hviler den på?

The National List of recommendations is a scientific survey of the most used drugs. It separates the drugs into three groups: 1) recommended drugs, 2) drugs only to be used in special circumstances, and 3) drugs which should not be used in normal circumstances because of too limited efficacy or serious adverse reactions. The list is made by specialists chosen by the scientific bodies. The Institute of Rational Pharmacotherapy acts as secretariat. The primary selection criterion is the relationship between effect and adverse reactions, while price issues are not considered. Special consideration has been given to how the effect is expressed, composite measurements of effect and to extrapolation. The list, which is updated annually, is available at the IRF's homepage. The list of recommendations is primarily an instrument for general practitioners and drug committees; in future it should be integrated into the regional drug lists.