Principal Findings
The developed prediction model can stratify medical workers into different risk groups when encountering medical disputes. This provides a valuable tool for identifying high-risk individuals and implementing preventive strategies. In addition, the model was externally validated using data from medical workers in Beijing, further enhancing its reliability and generalizability. Notably, this study’s impact extends beyond research, as it created a web-based app for medical dispute prediction. This user-friendly tool can be accessed by the public, thereby facilitating its practical app in clinical settings. By inputting relevant characteristics, health care workers and stakeholders can evaluate the risk of medical disputes for individual cases.A nationwide survey conducted in China in 2021 revealed that 31.06% of physicians had dealt with medical disputes involving patients [Preventive strategies are urgently needed to address medical disputes. To implement personalized preventive strategies, we developed a novel prediction model to classify medical workers into different risk categories based on their likelihood of experiencing medical disputes in the upcoming year. We proposed several models using ML techniques and introduced a wide selection of ML techniques for the analysis. GBDT demonstrated superior performance, achieving the highest score in the internal validation set and ranking second in the external validation set. A single indicator alone has limited predictive value for medical disputes. However, by combining multiple indicators, the predictive performance is greatly improved. In this study, we identified 18 features that were significantly associated with medical disputes and used them as the input features for the model. Through ML modeling, favorable prediction effectiveness was achieved, as demonstrated by calibration and discrimination metrics. ML prediction models offer several advantages over traditional models because they can effectively handle large and complex data sets, identify nonlinear relationships, and improve prediction accuracy. Furthermore, ML models can be trained on a subset of data and validated on an independent subset, thereby reducing the risk of overfitting. Consequently, the GBDT model outperformed the traditional LR model in terms of prediction performance in this study.Although previous studies have established prediction models such as using medical claims data to predict suicide risk [To determine the best-performing model among the 6 models, we compared their predictive performance using a scoring system to rank the techniques in terms of predictive effectiveness. The prediction performance mainly assessed discrimination and calibration, both of which are crucial aspects of prediction performance [This study demonstrated a significant association between the 18 characteristics and medical disputes. Specifically, working in a public hospital, general hospital, or class A tertiary hospital; being a physician; having a senior technical title; being male and of older age; lacking concern for the duty of the hospital law department or legal counselor; not having a clear understanding of the contents of hospital charters; considering it less important to conduct clinical practice in accordance with the law; having a strong need for legal training in hospitals; and having previous legal issues outside of medical work were identified as predictors of medical disputes. Conversely, legal training organized by hospitals, having publicity of the rule of law for hospital staff, participating in legal training organized by hospitals, being willing to participate in law training organized by hospitals, and having a well-established rule of law in the hospital were found to be protective factors. These findings are consistent with the current literature, which also elucidated that conducting standardized high-level hospitals [This study primarily focused on investigating the relationship between medical disputes and hospital legal construction, providing valuable supplementary findings to the current literature. We demonstrated that possessing good knowledge about medical law was conducive to reducing medical disputes among medical staff. Publicizing the rule of law for hospital staff and encouraging their participation in legal training courses were identified as greatly beneficial in preventing medical disputes. In addition, up to 52.51% (19,983/38,053) of the medical workers in the Hunan data set reported having previously experienced legal issues outside of medical work in the Hunan data set. This finding suggests that hospitals should prioritize cultivating legal literacy among their staff and develop strategies to prevent and address legal problems even if these legal problems were not related to medical work.Other studies have already proposed helpful suggestions for the prevention of medical disputes. For instance, an integrative review conducted by Jack et al [