# US 2017/0255753 A1 · Computerized System and Method of Open Account Processing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0255753 A1 - Document ID: US20170255753A1 - Status: Published application - Kind: A1 - Application number: 15/450,419 - Filed: 2017-03-06 - Published: 2017-09-07 - Earliest claimed: 2016-03-07 - Assignees: Crowe LLP - Applicants: Crowe Horwath, LLP - Inventors: Kevin Jarrett; Eric Boggs; Cory Herendeen - Web page: https://patentgrep.com/patent/US20170255753A1 ## CPC - G06N 5/04: Inference or reasoning models - G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A computerized system and method for health care facilities to reduce manual handling of at least some open account issues. The system provides healthcare facilities with the ability to resolve current open patient account issues by utilizing the data patterns from a facility's historical patient account transaction activity, to create a machine learning model that can predict resolutions to the open accounts. These patterns are then applied to a facility's current transaction data providing next step resolution to each patient account. ## Claims ### Claim 1 An apparatus comprising: a storage device; and at least one processor coupled to the storage device, wherein the storage device stores a program for controlling the at least one processor, and wherein the at least one processor, being operative with the program, is configured to: create a machine learning 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; analyzing the training data to create the machine learning model configured to make predictions representative of resolutions of open account transactions; applying the machine learning model to predict resolutions of a plurality of open account transactions by: making predictions on a level of human interaction needed to resolve the plurality of open account transactions using the machine learning model; and electronically resolving at least a portion of the open accounts based on predictions made by the machine learning model. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein in the program, the machine learning model is configured to categorize at least a portion of the open account transactions into a category of at least two categories based on a level of human interaction needed to resolve each respective open account transaction. ### Claim 3 (depends on 2) The apparatus of claim 2, wherein in the program, the machine learning model is configured to categorize at least a portion of the open account transactions into: (1) a first category if an exception can be resolved by an automated solution without any human involvement, (2) a second category if an exception can be resolved by an automated repeatable work list with some human involvement, and (3) a third category if an exception needs to be resolved with a higher degree of human involvement. ### Claim 4 (depends on 3) The apparatus of claim 3, wherein in the program, the machine learning model is configured to categorize into the first, second and third categories based on a predicted confidence level on an amount of human involvement needed to resolve an exception. ### Claim 5 (depends on 4) The apparatus of claim 4, wherein in the program, the machine learning model is configured to categorize into the first category if the predicted confidence level is above a first threshold confidence level, categorize into the second category if the predicted confidence level is above a second threshold confidence level, categorize into the third category if the predicted confidence level is above a third threshold confidence level. ### Claim 6 (depends on 5) The apparatus of claim 5, wherein in the program, the third threshold confidence level is less than the first threshold confidence level and the second threshold confidence level, wherein the second threshold confidence level is less than the first threshold confidence level. ### Claim 7 (depends on 4) The apparatus of claim 4, wherein in the program, the machine learning model is configured to categorize an exception in the first category if the predicted confidence level is above ninety percent. ### Claim 8 (depends on 4) The apparatus of claim 4, wherein in the program, the machine learning model is configured to categorize an exception in the second category if the predicted confidence level is less than ninety percent, but above fifty percent. ### Claim 9 (depends on 4) The apparatus of claim 4, wherein in the program, the machine learning model is configured to categorize an exception in the third category if the predicted confidence level is less than fifty percent. ### Claim 10 (depends on 9) The apparatus of claim 9, wherein in the program, the machine learning model is configured to flag any exceptions categorized in the third category for a holding area for additional attention for resolution. ### Claim 11 (depends on 10) The apparatus of claim 10, wherein in the program, the machine learning model is periodically updated by obtaining current transactional data and analyzing both the training data and the current transactional data to update the machine learning model on how to make predictions representative of resolutions of open account transactions. ### Claim 12 (depends on 10) The apparatus of claim 10, wherein in the program, the machine learning model is periodically updated with at least a portion of the transactions in the holding area for which the machine learning model predicted a different outcome from a human decision. ### Claim 13 (depends on 12) The apparatus of claim 12, wherein in the program, the machine learning model is periodically updated with the portion of the transactions in the holding area for which the machine learning model predicted a different outcome from a human decision and a sample of the training to update the machine learning model on how to make predictions representative of resolutions of open account transactions. ### Claim 14 (depends on 13) The apparatus of claim 13, wherein in the program, upon creating an updated machine learning model based on the transactions from the holding area and sample of training data, a comparison is made whether the updated machine learning model makes more accurate predictions on resolutions of open account transactions than an existing machine learning model. ### Claim 15 (depends on 14) The apparatus of claim 14, wherein in the program, if the updated machine learning model is determined to make more accurate predictions on resolutions of open account transactions than the existing machine learning model, the updated machine learning model replaces the existing machine learning model. ### Claim 16 (depends on 14) The apparatus of claim 14, wherein in the program, a determination of whether the updated machine learning model makes more accurate predictions than the existing machine learning model is based on a score from how each respective machine learning model performs against a validation data set. ### Claim 17 (depends on 16) The apparatus of claim 16, wherein in the program, the scoring of how the respective machine learning models performs includes a weight for determining performance based on one or more of a maximum time allowed for the machine learning model to analyze the validation data set and/or an accuracy of predicting resolutions of open account transactions. ### Claim 18 A method of creating a machine learning model for resolving open account issues, the method comprising the steps of: obtaining training data representative of historical account transactions between a plurality of patients and a healthcare facility; and analyzing the training data to create the machine learning model configured to make predictions representative of resolutions of open account transactions. ### Claim 19 (depends on 18) The method of claim 18, further comprising periodically updating the machine learning model by obtaining current transactional data and analyzing both the training data and the current transactional data to update the machine learning model on how to make predictions representative of resolutions of open account transactions. ### Claim 20 (depends on 18) The method of claim 18, further comprising periodically updating the machine learning model with the portion of the transactions in a holding area for which the machine learning model predicted a different outcome from a human decision and a sample of the training to update the machine learning model on how to make predictions representative of resolutions of open account transactions.