Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.

Objective: To evaluate the transferability of BERT (Bidirectional Encoder Representations from Transformers) to patient safety, we use it to classify incident reports characterised by limited data and encompassing multiple imbalanced classes.

Methods: BERT was applied to classify 10 incident types and 4 severity levels by (1) fine-tuning and (2) extracting word embeddings for feature representation. Training datasets were collected from a state-wide incident reporting system in Australia (n_type/severity=2860/1160). Transferability was evaluated using three datasets: a balanced dataset (type/severity: n_benchmark=286/116); a real-world imbalanced dataset (n_original=444/4837, rare types/severity<=1%); and an independent hospital-level reporting system (n_independent=6000/5950, imbalanced). Model performance was evaluated by F-score, precision and recall, then compared with convolutional neural networks (CNNs) using BERT embeddings and local embeddings from incident reports.

Results: Fine-tuned BERT outperformed small CNNs trained with BERT embedding and static word embeddings developed from scratch. The default parameters of BERT were found to be the most optimal configuration. For incident type, fine-tuned BERT achieved high F-scores above 89% across all test datasets (CNNs=81%). It effectively generalised to real-world settings, including rare incident types (eg, clinical handover with 11.1% and 30.3% improvement). For ambiguous medium and low severity levels, the F-score improvements ranged from 3.6% to 19.7% across all test datasets.

Discussion: Fine-tuned BERT led to improved performance, particularly in identifying rare classes and generalising effectively to unseen data, compared with small CNNs.

Conclusion: Fine-tuned BERT may be useful for classification tasks in patient safety where data privacy, scarcity and imbalance are common challenges.