Abstract
The purpose of this study was to describe incident reporters’ views identified by artificial intelligence concerning the prevention of medication incidents that were assessed, causing serious or moderate harm to patients. The information identified the most important risk management areas in these medication incidents. This was a retrospective record review using medication-related incident reports from one university hospital in Finland between January 2017 and December 2019 (n = 3496). Of these, incidents that caused serious or moderate harm to patients (n = 137) were analysed using artificial intelligence. Artificial intelligence classified reporters’ views on preventing incidents under the following main categories: (1) treatment, (2) working, (3) practices, and (4) setting and multiple sub-categories. The following risk management areas were identified: (1) verification, documentation and up-to-date drug doses, drug lists and other medication information, (2) carefulness and accuracy in managing medications, (3) ensuring the flow of information and communication regarding medication information and safeguarding continuity of patient care, (4) availability, update and compliance with instructions and guidelines, (5) multi-professional cooperation, and (6) adequate human resources, competence and suitable workload. Artificial intelligence was found to be useful and effective to classifying text-based data, such as the free text of incident reports.
Highlights
Based on the definition by the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP), a medication error (ME) is: ‘any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient, or consumer’ [2]
This study described incident reporters’ views concerning the prevention of medication incidents assessed causing serious or moderate harm to patients by using Artificial intelligence (AI) for data analysis
Our analysis indicated that AI could classify incidents meaningfully since the sub-categories were similar in both studies, even though the main categories were different in the manual and AI analysis
Summary
Based on the definition by the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP), a medication error (ME) is: ‘any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient, or consumer’ [2]. We concentrate only on MEs made and reported by health care professionals. The majority of MEs do not cause serious harm to patients, but some are serious and can even cause death [3]. MEs are a leading cause of harm in health care globally, with an annual estimated cost of 42 billion USD, at least one death per day and injuries to
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