System and method for detecting billing errors using predictive modeling
First Claim
1. A system for detecting billing errors using artificial intelligence comprising:
- a computer system in communication with a billing client, said computer system electronically receiving and processing billing information electronically gathered by the billing client over a pre-defined period of time, said computer system configured to include an artificial neural network having an input layer, a plurality of processing elements, and an output layer;
a billing history database in communication with the computer system and storing the billing information, the computer system processing the billing information to select one or more data fields of the billing information; and
a billing error detection engine executed by the computer system, said detection engine processing the one or more data fields using one or more predictive models to detect, score, and flag potential billing errors in the billing information, the billing error detection engine executing the following steps;
a feedback model so that the computer system learns relationships between billing codes present in the billing information,an inpatient model that targets low charges and high charges in inpatient data by filtering for each Diagnosis Related Groups (DRG) the number of visits within a pre-defined threshold and then applying a Principal Component Analysis (PCA) Module to calculate and compare a department-hospital level average with a reconstructed value for new visits, the inpatient model utilizing said artificial neural network of said computer system, said artificial neural network reconstructing charge values and flagging actual charge values for review if a difference between the reconstructed charge value and the actual charge value is above a thresholdan outpatient model that detects missing codes in outpatient data by applying a supervised learning model to learn the probability of a presence of a code using a logistic regression (LR) model for each code to be evaluated, and applying a Decision Tree (DT) model to capture non-linearity between data and their codes and to take into account multiple hospitals,applying a joint-density learning model to learn interdependencies between visit data using a Restricted Boltzmann Machine (RBM) model to compute whether a code should be present and a probability of missing charges, and applying a Gaussian Missing Data (GMD) model to suggest other codes that should be present; and
executing a cascade model to capture relationship between codes and improve prediction accuracy and performance by (i) applying a normalization model to pre-process outputs of the LR model, the DT model, and the RBM model to calibrate the outputs for consistency, (ii) applying an ensemble model to combine the LR model, the DT model, the RBM model, and the GMD model to generate an ensemble score, and (iii) applying a feedback model to further refine results by receiving as input a predicted code and an ensemble score to generate a probability of code acceptance;
wherein the computer system transmits the flagged potential billing errors to the billing client for review.
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Accused Products
Abstract
A system and method for detecting billing errors using predictive models is provided. The system includes a computer system and a billing error detection engine capable of detecting billing errors using predictive modeling techniques. The system receives and pre-processes billing information. The system then applies one or more predictive models to the information to identify billing errors. The results could be optionally sent to, and reviewed by, third party auditors, whereby their feedback could be incorporated into the results. A final report is generated by the system which indicates billing errors that require correction, thereby allowing an entity to correct such errors and prevent revenue leakage.
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Citations
21 Claims
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1. A system for detecting billing errors using artificial intelligence comprising:
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a computer system in communication with a billing client, said computer system electronically receiving and processing billing information electronically gathered by the billing client over a pre-defined period of time, said computer system configured to include an artificial neural network having an input layer, a plurality of processing elements, and an output layer; a billing history database in communication with the computer system and storing the billing information, the computer system processing the billing information to select one or more data fields of the billing information; and a billing error detection engine executed by the computer system, said detection engine processing the one or more data fields using one or more predictive models to detect, score, and flag potential billing errors in the billing information, the billing error detection engine executing the following steps; a feedback model so that the computer system learns relationships between billing codes present in the billing information, an inpatient model that targets low charges and high charges in inpatient data by filtering for each Diagnosis Related Groups (DRG) the number of visits within a pre-defined threshold and then applying a Principal Component Analysis (PCA) Module to calculate and compare a department-hospital level average with a reconstructed value for new visits, the inpatient model utilizing said artificial neural network of said computer system, said artificial neural network reconstructing charge values and flagging actual charge values for review if a difference between the reconstructed charge value and the actual charge value is above a threshold an outpatient model that detects missing codes in outpatient data by applying a supervised learning model to learn the probability of a presence of a code using a logistic regression (LR) model for each code to be evaluated, and applying a Decision Tree (DT) model to capture non-linearity between data and their codes and to take into account multiple hospitals, applying a joint-density learning model to learn interdependencies between visit data using a Restricted Boltzmann Machine (RBM) model to compute whether a code should be present and a probability of missing charges, and applying a Gaussian Missing Data (GMD) model to suggest other codes that should be present; and executing a cascade model to capture relationship between codes and improve prediction accuracy and performance by (i) applying a normalization model to pre-process outputs of the LR model, the DT model, and the RBM model to calibrate the outputs for consistency, (ii) applying an ensemble model to combine the LR model, the DT model, the RBM model, and the GMD model to generate an ensemble score, and (iii) applying a feedback model to further refine results by receiving as input a predicted code and an ensemble score to generate a probability of code acceptance; wherein the computer system transmits the flagged potential billing errors to the billing client for review. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for detecting billing errors using artificial intelligence comprising:
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electronically receiving and processing billing information by a computer system in communication with a billing client, said billing information electronically gathered by the billing client over a pre-defined period of time, said computer system configured to include an artificial neural network having an input layer, a plurality of processing elements, and an output layer; processing the billing information by the computer system to select one or more data fields of the billing information; storing the billing information in a billing history database in communication with the computer system; executing by the computer system a billing error detection engine to process the one or more data fields using one or more predictive models of the billing error detection engine to detect, score, and flag potential billing errors in the billing information; executing, by the billing error detection engine, a feedback model so that the computer system learns relationships between billing codes present in the billing information; executing, by the billing error detection engine, an inpatient model that targets low charges and high charges in inpatient data by filtering for each Diagnosis Related Groups (DRG) the number of visits within a pre-defined threshold and then applying a Principal Component Analysis (PCA) Module to calculate and compare a department-hospital level average with a reconstructed value for new visits, the inpatient model utilizing said artificial neural network of said computer system, said artificial neural network reconstructing charge values and flagging actual charge values for review if a difference between the reconstructed charge value and the actual charge value is above a threshold; executing, by the billing error detection engine, an outpatient model that detects missing codes in outpatient data by applying a supervised learning model to learn the probability of a presence of a code using a logistic regression (LR) model for each code to be evaluated, and applying a Decision Tree (DT) model to capture non-linearity between data and their codes and to take into account multiple hospitals; applying, by the billing error detection engine, a joint-density learning model to learn interdependencies between visit data using a Restricted Boltzmann Machine (RBM) model to compute whether a code should be present and a probability of missing charges, and applying a Gaussian Missing Data (GMD) model to suggest other codes that should be present; and executing, by the billing error detection engine, a cascade model to capture relationship between codes and improve prediction accuracy and performance by (i) applying a normalization model to pre-process outputs of the LR model, the DT model, and the RBM model to calibrate the outputs for consistency, (ii) applying an ensemble model to combine the LR model, the DT model, the RBM model, and the GMD model to generate an ensemble score, and (iii) applying a feedback model to further refine results by receiving as input a predicted code and an ensemble score to generate a probability of code acceptance; transmitting the flagged potential billing errors to the billing client for review. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable medium having computer-readable instructions stored thereon which, when executed by a computer system, cause the computer system to detect billing errors using artificial intelligence by performing the steps of:
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electronically receiving and processing billing information by a computer system in communication with a billing client, said billing information electronically gathered by the billing client over a pre-defined period of time, said computer system configured to include an artificial neural network having an input layer, a plurality of processing elements, and an output layer; processing the billing information by the computer system to select one or more data fields of the billing information; storing the billing information in a billing history database in communication with the computer system; executing by the computer system a billing error detection engine to process the one or more data fields using one or more predictive models of the billing error detection engine to detect, score, and flag potential billing errors in the billing information; executing, by the billing error detection engine, a feedback model so that the computer system learns relationships between billing codes present in the billing information; executing, by the billing error detection engine, an inpatient model that targets low charges and high charges in inpatient data by filtering for each Diagnosis Related Groups (DRG) the number of visits within a pre-defined threshold and then applying a Principal Component Analysis (PCA) Module to calculate and compare a department-hospital level average with a reconstructed value for new visits, the inpatient model utilizing said artificial neural network of said computer system, said artificial neural network reconstructing charge values and flagging actual charge values for review if a difference between the reconstructed charge value and the actual charge value is above a threshold; executing, by the billing error detection engine, an outpatient model that detects missing codes in outpatient data by applying a supervised learning model to learn the probability of a presence of a code using a logistic regression (LR) model for each code to be evaluated, and applying a Decision Tree (DT) model to capture non-linearity between data and their codes and to take into account multiple hospitals; applying, by the billing error detection engine, a joint-density learning model to learn interdependencies between visit data using a Restricted Boltzmann Machine (RBM) model to compute whether a code should be present and a probability of missing charges, and applying a Gaussian Missing Data (GMD) model to suggest other codes that should be present; and executing, by the billing error detection engine, a cascade model to capture relationship between codes and improve prediction accuracy and performance by (i) applying a normalization model to pre-process outputs of the LR model, the DT model, and the RBM model to calibrate the outputs for consistency, (ii) applying an ensemble model to combine the LR model, the DT model, the RBM model, and the GMD model to generate an ensemble score, and (iii) applying a feedback model to further refine results by receiving as input a predicted code and an ensemble score to generate a probability of code acceptance; transmitting the flagged potential billing errors to the billing client for review. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification