One or more non-transitory, computer-readable storage media comprising a plurality of instructions stored thereon that, in response to being executed, cause a computing device to:
- create a plurality of machine learning models comprising at least a credit model and a denial model for predicting resolutions to open accounts by:
- obtaining training data representative of historical account transactions between a plurality of patients and a healthcare facility, wherein at least a portion of the training data is targeted specifically for each of the credit model and the denial model;
- analyzing the training data to create the plurality of machine learning models configured to make predictions representative of resolutions of open account transactions, wherein the credit model and the denial model are configured to make predictions based on respective open account types of credit balances in which the credit model is to predict an automated resolution based on identifying one or more of problem payors, timing issues, transaction code issues, transaction type issues, or transaction sequence issues and denial of benefits in which the denial model is to predict an automated resolution based on identifying one or more of denial rates, service line issues, healthcare provider specific issues, revenue impact, or management reports, wherein the open account transactions are transactions for accounts that have previously been billed and remain unpaid;
- training and/or retraining at least a portion of the plurality of machine learning models by making predictions regarding open account transactions,
- wherein in response to at least one of the plurality of machine learning models making a prediction at a confidence level exceeding a threshold confidence level regarding an open account transaction:
- (i) assigning the prediction to the open account transaction;
- (ii) flagging the prediction for a holding area when the prediction is a different outcome from a human decision for the open account transaction;
- and (iii) periodically retraining the plurality of machine learning models based on open account transactions associated with the holding area;
- wherein in response to the confidence level associated with the prediction being less than the threshold confidence level regarding the open account transaction, performing a deep learning process by (i) making predictions regarding the open account transaction with a plurality of different machine learning models that are different levels to each other in that a machine learning model of a subsequent level includes output from a machine learning model of a previous level;
- and (ii) retraining the machine learning model that made the prediction based on the deep learning process;
- applying the machine learning model to predict resolutions of a plurality of open account transactions as a function of open account type by:
- making predictions on a level of human interaction needed to resolve the plurality of open account transactions using the plurality of machine learning models;
- and
- electronically resolving at least a portion of the open accounts based on predictions made by the plurality of machine learning models.