Development and Validation of a Machine Learning Model to Predict Post-dispatch Cancellation of Physician-staffed Rapid Car.

Objectives: This study aimed to develop and validate a machine learning prediction model for post-dispatch cancellation of physician-staffed rapid car. Materials: Data were extracted from the physician-staffed rapid response car database at our Hospital between April 2017 and March 2019. Methods: After obtaining 2019 cases, we divided the dataset into a training set for developing the model and a test set for validation using stratified random sampling with an 8 : 2 allocation ratio. We selected random forest as the machine-learning classifier. The outcome was the post-dispatch cancellation of a rapid car. The model was trained using predictor variables, including 18 different reasons for rapid car request, age and gender of a patient, date (month), and distance from the hospital. Results: This machine learning model predicted the occurrence of post-dispatch cancellation of rapid cars with an accuracy of 75.5% [95% confidence interval (CI): 71.0-79.6], sensitivity of 81.5% (CI: 75.0-86.9), specificity of 70.8% (CI: 64.4-76.6), and an area under the receiver operating characteristic value of 0.83 (CI: 0.79-0.87). The important features were distance from the hospital to the scene, age, suspicion of non-witnessed cardiac arrest, farthest geographic area, and date (months). Conclusions: We developed a favorable machine learning model to predict post-dispatch cancellation of rapid cars in a local district. This study suggests the potential of machine-learning models in improving the efficiency of dispatching physicians outside hospitals.