Identifying health information technology related safety event reports from patient safety event report databases

The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model features. A feature-constraint model was developed and evaluated to support the analysis of PSE reports.

5287 PSE reports manually coded as likely or unlikely related to HIT were used to train unigram, bigram, and combined unigram-bigram logistic regression and support vector machine models using five-fold cross validation. A difference-based scoring approach was used to prioritize and select unigram and bigram features by their relative importance to likely and unlikely HIT reports. A held-out set of 2000 manually coded reports were used for testing.

Unigram models tended to perform better than bigram and combined models. A 300-unigram logistic regression had comparable classification performance to a 4030-unigram SVM model but with a faster relative run-time. The 300-unigram logistic regression model evaluated with the testing data had an AUC of 0.931 and a F1-score of 0.765.

A difference-based scoring, prioritization, and feature selection approach can be used to generate simplified models with high performance. A feature-constraint model may be more easily shared across healthcare organizations seeking to analyze their respective datasets and customized for local variations in PSE reporting practices.

The feature-constraint model provides a method to identify HIT-related patient safety hazards using a method that is applicable across healthcare systems with variability in their PSE report structures.