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

Huw Prosser Evans, Athanasios Anastasiou, Adrian Edwards, Peter Hibbert, Meredith Makeham, Saturnino Luz, Aziz Sheikh, Liam Donaldson, Andrew Carson-Stevens*

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
JournalHealth Informatics Journal
DOIs
Publication statusE-pub ahead of print - 7 Mar 2019

Keywords

  • incident reporting
  • machine learning
  • natural language processing
  • patient safety
  • quality improvement

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