Abstract: A method for KPI forecasting, comprising: (i) receiving an identification of one or more KPI to be forecast and a forecast horizon; (ii) extracting data received from a database for KPI forecasting; (iii) aggregating the extracted data; (iv) optionally removing one or more outliers from the aggregated data by identifying one or more possible outliers, presenting the outliers to a user, receiving information from the user comprising an identification of outliers, and removing the outliers; (v) fitting training data to a plurality of forecasting models; (vi) identifying a best fit forecasting model using test data; (vii) forecasting, using the best fit model, to generate KPI forecast data; (viii) evaluating the KPI forecast data for accuracy; and (ix) presenting the generated KPI forecast data to the user via a user interface.

Abstract: The present system is configured to display distributions of predicted health outcome information for patient populations in geographical areas. The system is configured to obtain demographic, social (e.g., including environmental), and prior health outcome information for a patient population in a geographical area. The system is configured to train a prediction model based on the demographic, social, and prior health outcome information, which outputs weighted features of the demographic and social information that are predictive of health outcomes for the patient population. The system is configured to cause display of a distribution of predicted health outcome information for the patient population in the geographical area based on the weighted features.

Abstract: In one embodiment, a computer system and method is disclosed for providing a social determinates of health model which correlates socio-economic factors with the non-adherence risk of an individual to a proscribed task such as care plan compliance, medication dosing, physical therapy or exercise routine, office visit, or test appointment.

Abstract: In one embodiment, a computer system and method is disclosed for providing an adjustable social determinates of health model, which allows for user selectable customization of the categories used to calculate the model as well as adjustment of the model itself. In addition, the system displays predictiveness metrics indicating the predictive quality of the model. The model can be used by the user to create a socio-economic index to provide the user with insights into the relevant socio-economic factors impacting healthcare of an individual patient or population.