Artificial Intelligence for Identifying the Prevention of Medication Incidents Causing Serious or Moderate Harm: An Analysis Using Incident Reporters' Views

Overview

Journal Int J Environ Res Public Health

Publisher MDPI

Specialties Environmental Health
Public Health

Date 2021 Sep 10

PMID 34501795

Citations 1

Authors

Marja Harkanen

Kaisa Haatainen

Katri Vehvilainen-Julkunen

Merja Miettinen

Affiliations

Soon will be listed here.

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.

Citing Articles

Evaluation of Global trigger tool as a medication safety tool for adverse drug event detection-a cross-sectional study in a tertiary hospital.

Valkonen V, Haatainen K, Saano S, Tiihonen M Eur J Clin Pharmacol. 2023; 79(5):617-625.

PMID: 36905428 PMC: 10110725. DOI: 10.1007/s00228-023-03469-5.

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