Text Mining Method for Studying Medication Administration Incidents and Nurse-Staffing Contributing Factors: A Pilot Study

Abstract

Incident reporting systems are being implemented globally, thus increasing the profile and prevalence of incidents, but the analysis of free-text descriptions remains largely hidden. The aims of the study were to explore the extent to which incident reports recorded staffing issues as contributors to medication administration incidents. Incident reports related to medication administration (N = 1012) were collected from two hospitals in Finland between January 1, 2013, and December 31, 2014. The SAS Enterprise Miner 13.2 and its Text Miner tool were used to excavate terms and descriptors and to uncover themes and concepts in the free-text descriptions of incidents with (n = 194) and without (n = 818) nurse staffing–related contributing factors. Text mining included (1) text parsing, (2) text filtering, and (3) modeling text clusters and text topics. The term “rush/hurry” was the sixth most common term used in incidents where nurse-staffing was identified as a contributing factor. Nurse-staffing factors, however, were not pronounced in clusters or in text topics of either data set. Text mining offers the opportunity to analyze large free-text mass and holds promise for providing insight into the antecedents of medication administration incidents.