Evaluating Active Learning Strategies for Automated Classification of Patient Safety Event Reports in Hospitals

Author:

Islam Shehnaz1ORCID,Alfred Myrtede1,Wilson Dulaney2,Cohen Eldan1ORCID

Affiliation:

1. Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada

2. Public Health Sciences, Medical University of South Carolina, Charleston, USA

Abstract

Patient safety event (PSE) reports, which document incidents that compromise patient safety, are fundamental for improving healthcare quality. Accurate classification of these reports is crucial for analyzing trends, guiding interventions, and supporting organizational learning. However, this process is labor-intensive due to the high volume and complex taxonomy of reports. Previous work has shown that machine learning (ML) can automate PSE report classification; however, its success depends on large manually-labeled datasets. This study leverages Active Learning (AL) strategies with human expertise to streamline PSE-report labeling. We utilize pool-based AL sampling to selectively query reports for human annotation, developing a robust dataset for training ML classifiers. Our experiments demonstrate that AL significantly outperforms random sampling in accuracy across various text representations, reducing the need for labeled samples by 24% to 69%. Based on these findings, we suggest that incorporating AL strategies into PSE-report labeling can effectively reduce manual workload while maintaining high classification accuracy.

Funder

Agency for Healthcare Research and Quality

Publisher

SAGE Publications

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