A system including a confidence assessment module that implements a neural network to assess the likelihood that codes associated with a patient's encounter with a healthcare organization are accurate. The confidence assessment module may also be incrementally trained.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of incrementally training a confidence assessment module that calculates a confidence value indicative of the extent to which a code associated with a patient's encounter with a healthcare organization is proper, the computer-implemented method comprising: assessing, with the confidence assessment module, a training corpus comprised of a plurality of coded encounters, to produce resultant confidence values associated with each encounter; and, comparing the resultant confidence values to a target confidence value; adjusting variables within the confidence assessment module to produce resultant confidence values closer to the target confidence value. 2. The computer-implemented method of claim 1, wherein each of the plurality of coded encounters comprises documentation associated with a patient's encounter with a healthcare provider, and codes associated with that encounter. 3. The computer-implemented method of claim 1, wherein the confidence assessment module is a neural network-based confidence assessment module. 4. The computer-implemented method of claim 3, wherein the neural network-based confidence assessment module comprises at least one layer of binary input nodes, one layer of intermediate nodes, and one layer of output nodes, and wherein nodes between layers are weighted with edge weight values. 5. The computer-implemented method of claim 4, wherein the output node comprises the confidence value. 6. The computer-implemented method of claim 4, wherein adjusting variables comprises: applying an error function to the variables to determine an error of the resultant confidence value as compared with the target confidence value; and, calculating the derivative of the error function to determine how the edge weights values should be modified to decrease the error; and, modifying the edge weights. 7. The computer-implemented method of claim 6, further comprising: iteratively repeating the assessing, comparing, and adjusting step. 8. The computer-implemented method of claim 1, wherein the confidence value is indicative of the likelihood that the code associated with the patient's encounter with the healthcare organization is accurate. 9. The computer-implemented method of claim 6, wherein accurate means a human coder would have associated the same code with the encounter. 10. An incremental learning confidence assessment system that evaluates coded encounters between a patient and a healthcare organization and computes a confidence value indicative of the likelihood that codes associated with the coded encounters are proper, the system comprising: a confidence assessment module operative to receive coded encounters, the coded encounters comprising both (1) documentation associated with a patient's encounter with a healthcare organization, and (2) codes associated with that encounter, and using a neural network, compute the confidence value for the coded encounter; and, a training module that invokes the confidence assessment module to evaluate a corpus of training notes, then adjusts variables within the confidence assessment module based on the evaluation. 11. The system of claim 10, wherein the neural network comprises a binary neural network having a layer of binary input nodes, a layer of intermediate nodes, and an output node, the output node indicative of the confidence value. 12. The system of claim 10, wherein nodes between layers are represented as edges, and edges are associated with an edge weight variable that defines the relative weight given to an edge. 13. The system of claim 12, wherein the training module evaluates the resulting confidence values associated with the corpus of training notes against a target confidence value. 14. The system of claim 13, wherein the training module applies an error function to the edge weights to determine an error associated with at least some of the edge weights, and then calculates the derivatives of the error function to determine how the variables should be adjusted so the confidence values associated with the corpus are closer to the target confidence value. 15. The system of claim 14, wherein the training module causes the edge weights to be adjusted. 16. A computer-implemented method of calculating a confidence value indicative of the likelihood that a code associated with a patient's encounter with a healthcare organization is proper, the computer-implemented method comprising: analyzing, using a processor of the computer, patient-related healthcare records that relate to the patient's encounter with a healthcare organization, to extract attributes; representing at least some of the extracted attributes as binary input nodes associated with a neural network model that has an output node associated with the confidence value; and, applying the neural network model to the binary input nodes, to compute the confidence value associated with the output node. 17. The computer-implemented method of claim 16, further comprising: comparing the confidence value to a threshold value, and based on the comparison, determining if the code and patient-related healthcare records should be further reviewed by a human reviewer. 18. The computer-implemented method of claim 17, further comprising: providing the patient-related health records to a human reviewer. 19. The computer-implemented method of claim 16, wherein the neural network model comprises a trained neural network model. 20. The computer-implemented method of claim 16, wherein the neural network model has a plurality of intermediate nodes and wherein weight values are associated with network edges that connect various nodes, and further comprising: comparing the confidence value associated with the output node to an expected value; and adjusting the weight values associated with the edges between the intermediate nodes such that the confidence value associated with the output node is within an acceptable deviance of the expected value.