The Contribution of Staffing to Medication Administration Errors: A Text Mining Analysis of Incident Report Data

Abstract

Purpose: (a) To describe trigger terms that can be used to identify reports of inadequate staffing contributing to medication administration errors, (b) to identify such reports, (c) to compare the degree of harm within incidents with and without those triggers, and (d) to examine the association between the most commonly reported inadequate staffing trigger terms and the incidence of omission errors and “no harm” terms. Design and Setting: This was a retrospective study using descriptive statistical analysis, text mining, and manual analysis of free text descriptions of medication administration–related incident reports (N = 72,390) reported to the National Reporting and Learning System for England and Wales in 2016. Methods: Analysis included identifying terms indicating inadequate staffing (manual analysis), followed by text parsing, filtering, and concept linking (SAS Text Miner tool). IBM SPSS was used to describe the data, compare degree of harm for incidents with and without triggers, and to compare incidence of “omission errors” and “no harm” among the inadequate staffing trigger terms. Findings: The most effective trigger terms for identifying inadequate staffing were “short staffing” (n = 81), “workload” (n = 80), and “extremely busy” (n = 51). There was significant variation in omission errors across inadequate staffing trigger terms (Fisher’s exact test = 44.11, p