The hidden dangers in routine medical complaints: uncovering patient harm

Patient harm incidents (PHI) significantly impact healthcare quality and outcomes. Despite technological advances, predicting and managing these incidents remains challenging. This study aims to develop a predictive model to distinguish the genuine PHI from medical complaints and non-harmful events.

A retrospective study was conducted using data collected from January 2014 to December 2023, encompassing patient interactions, treatments, and complaints in the authors' institution. Variables considered included demographic details, clinical factors, and complaint characteristics. The predictive model was developed using least absolute shrinkage and selection operator (LASSO) regression and validated via a split-sample method. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, the Hosmer-Lemeshow test, and decision curve analysis.

The study included 987 medical complaints, of which 361 involved PHI. Using LASSO and logistic regression analyses, the model identified key predictors of PHI including the choice of treatment methods, healthcare providers' professional behavior, and the nature of the complaints. The model achieved an AUC of 0.917 (95% CI: 0.895-0.939) in the training set and 0.904 (95% CI: 0.870-0.938) in the test set. Model performance was further supported by calibration and decision curve analysis results.

The predictive model shows promise in identifying PHI from service complaints within our hospital. Key predictors, such as treatment decisions and healthcare providers' professional conduct, appear to play a notable role in patient safety. By utilizing this model, healthcare facilities may enhance their ability to identify and address factors that could contribute to PHI.