System and method for machine based medical diagnostic code identification, accumulation, analysis and automatic claim process adjudication

• US 10,552,576 B2
• Filed: 10/25/2011
• Issued: 02/04/2020
• Est. Priority Date: 10/26/2010
• Status: Active Grant

• Current Assignee: Stanley Victor Campbell
• Original Assignee: Stanley Victor Campbell
• Inventors: Campbell, Stanley Victor
• Primary Examiner(s): Le, Linh Giang
• Application Number:
  US13/281,349
• Publication Number:
  US 20120166212A1
• Time in Patent Office: 3,024 Days
• Field of Search: 705 2, 705 3, 3 2, 3 3
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 70/20 relating to practices or guidelines

Abstract

A context sensitive methodology, a Structured Virtual Construct (SVC) system, data tagging techniques, and an apparatus are provided for performing Medical Code-based decision-making involving the matching of a given medical identified element against one or more of a set of known or reference medical identified elements from history or other data elements. A satisfactory decision is achieved as a function of both aggregated ranking (AR) and account adjudication (AA), where account adjudication refers to the full set of values garnered by the Medical Code accumulation process in the process of generating approval/denial/re-classification/of medical diagnosis and/or claim events.

Claims

1. A computer-based Artificial Intelligence (AI) method for performing Medical Code-based decision making comprising:

analyzing, by an AI based computer utilizing neural computational logic, medical and contextually related data, using AI in an iterative context sensitive methodology for performing Medical Code-based decision-making comprising a matching of a given medical identified element against one or more of a set of known or reference medical identified elements, wherein the AI comprises one or more of a least squares fit for linear analysis, a Monte Carlo computational sampling, a Markov chain discrete step and a Dempster-Shafer formalism; and

determining, by an AI based computer, an acceptable code decision comprising a plurality of iterative decisions including matching the given medical identified element with the one or more of a set of known or reference medical identified elements to optimize a confidence level of correctness until a non-optimal confidence threshold for an acceptable code decision is reached.

2. The computer-based AI method of claim 1, wherein the Medical Code-based decision-making further comprises an aggregated ranking to determine a correlation adjudication value.

3. The computer-based AI method of claim 2, wherein the aggregated ranking generates a threshold that defines an acceptable adjudication parameter for decision-making.

4. The computer-based AI method of claim 1, wherein the acceptable code decision comprises rendering an automatic reclassification, executing an auto-accept decision or executing an auto-deny.

5. The computer-based AI method of claim 1, wherein the context sensitive methodology comprises determining a critical decision threshold that is modified based on a set of one or more external events or parameters.

6. The computer-based AI method of claim 1, wherein the Al further comprises a user interface configured to permit human feedback to enhance automated system learning.

7. A non-transitory computer readable medium with instructions stored thereon to perform Medical Code-based decision making, the decision making comprising:

analyzing, by an Artificial Intelligence (AI) based computer utilizing neural computational logic, medical and contextually related data, using AI in a iterative context sensitive methodology for performing Medical Code-based decision-making comprising a matching of a given medical identified element against one or more of a set of known or reference medical identified elements, wherein the AI comprises one or more of a least squares fit for linear analysis, a Monte Carlo computational sampling, a Markov chain discrete step and a Dempster-Shafer formalism; and

determining, by an AI based computer, an acceptable code decision comprising a plurality of iterative decisions including matching the given medical identified element with the one or more of a set of known or reference medical identified elements to optimize a confidence level of correctness until a non-optimal confidence threshold for an acceptable code decision is reached.

8. The non-transitory computer readable medium of claim 7, wherein the Medical Code-based decision-making further comprises an aggregated ranking to determine a correlation adjudication value.

9. The non-transitory computer readable medium of claim 8, wherein the aggregated ranking generates a threshold that defines an acceptable adjudication parameter for decision-making.

10. The non-transitory computer readable medium of claim 7, wherein the acceptable code decision comprises rendering an automatic reclassification, executing an auto-accept decision or executing an auto-deny.

11. The non-transitory computer readable medium of claim 7, wherein the context sensitive methodology comprises determining a critical decision threshold that is modified based on a set of one or more external events or parameters.

12. The non-transitory computer readable medium of claim 7, wherein the AI further comprises a user interface configured to permit human feedback to enhance automated system learning.