Abstract: A method and system for automated computer-based medical malpractice insurance underwriting using value-based care data is disclosed. A machine-learning based predictive model is trained to predict a risk of a medical malpractice claim from a provider data set including value-based care data and social factor data. A provider data set including value-based care data and social factor data for a provider is retrieved. The provider data set is input into the trained machine-learning based predictive model. A risk score indicating a risk of a medical malpractice claim for the provider is predicted based on the input provider data set using the trained machine learning based predictive model. A premium for medical malpractice insurance is determined for the provider based on the predicted risk score. The predictive modeling method can also be used to predict stop loss risk and determine a combined premium for medical malpractice and stop loss insurance.

Abstract: A method and system for automated computer-based medical malpractice insurance underwriting using value-based care data is disclosed. A machine-learning based predictive model is trained to predict a risk of a medical malpractice claim from a provider data set including value-based care data and social factor data. A provider data set including value-based care data and social factor data for a provider is retrieved. The provider data set is input into the trained machine-learning based predictive model. A risk score indicating a risk of a medical malpractice claim for the provider is predicted based on the input provider data set using the trained machine-learning based predictive model. A premium for medical malpractice insurance is determined for the provider based on the predicted risk score. The predictive modeling method can also be used to predict stop loss risk and determine a combined premium for medical malpractice and stop loss insurance.