FEATURESText Mining Method for Studying Medication Administration Incidents and Nurse-Staffing Contributing Factors A Pilot StudyHärkänen, Marja PhD, RN; Vehviläinen-Julkunen, Katri PhD, RN, RM; Murrells, Trevor MSc, BSc; Paananen, Jussi PhD; Rafferty, Anne Marie PhD, RNAuthor Information Author Affiliations: Department of Nursing Science, University of Eastern Finland, Kuopio (Drs Härkänen and Vehviläinen-Julkunen); and Kuopio University Hospital (Dr Vehviläinen-Julkunen), Finland; Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King's College London, United Kingdom (Mr Murrells and Dr Rafferty); and Institute of Biomedicine, University of Eastern Finland, Kuopio (Dr Paananen). The research has been financially supported by Finnish Cultural Foundation, Academy of Finland, University of Eastern Finland, King's College London, and by the National Institute for Health Research (NIHR) Imperial Patient Safety Translational Research Centre. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Marja Härkänen, PhD, RN, Department of Nursing Science, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland (firstname.lastname@example.org). Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.cinjournal.com). Online date: March 12, 2019 CIN: Computers, Informatics, Nursing: July 2019 - Volume 37 - Issue 7 - p 357-365 doi: 10.1097/CIN.0000000000000518 Buy SDC Metrics 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. Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.