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Automated classification of primary care patient safety incident report content and severity using supervised Machine Learning (ML) approaches

Evans, Huw Prosser, Anastasiou, Athanasios, Edwards, Adrian, Hibbert, Peter, Makeham, Meredith, Luz, Saturnino, Sheikh, Aziz, Donaldson, Liam and Carson-Stevens, Andrew 2019. Automated classification of primary care patient safety incident report content and severity using supervised Machine Learning (ML) approaches. Health Informatics Journal 10.1177/1460458219833102

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Abstract

Learning from patient safety incident reports is a vital part of improving healthcare. However, the volume of reports and their largely free-text nature poses a major analytic challenge. The objective of this study was to test the capability of autonomous classifying of free text within patient safety incident reports to determine incident type and the severity of harm outcome. Primary care patient safety incident reports (n=31333) previously expert-categorised by clinicians (training data) were processed using J48, SVM and Naïve Bayes. The SVM classifier was the highest scoring classifier for incident type (AUROC, 0.891) and severity of harm (AUROC, 0.708). Incident reports containing deaths were most easily classified, correctly identifying 72.82% of reports. In conclusion, supervised ML can be used to classify patient safety incident report categories. The severity classifier, whilst not accurate enough to replace manual processing, could provide a valuable screening tool for this critical aspect of patient safety.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Medicine
Publisher: SAGE Publications
ISSN: 1460-4582
Date of First Compliant Deposit: 3 December 2018
Date of Acceptance: 23 November 2018
Last Modified: 28 Jun 2019 16:20
URI: http://orca.cf.ac.uk/id/eprint/117321

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