Automated Healthcare Risk Management System Utilizing Real-time Predictive Models, Risk Adjusted Provider Cost Index, Edit Analytics, Strategy Management, Managed Learning Environment, Contact Management, Forensic GUI, Case Management And Reporting System For Preventing And Detecting Healthcare Fraud, Abuse, Waste And Errors

• US 20140081652A1
• Filed: 09/14/2013
• Published: 03/20/2014
• Est. Priority Date: 09/14/2012
• Status: Abandoned Application

• Current Assignee: RISK Management Solutions Incorporated
• Original Assignee: RISK Management Solutions Incorporated
• Inventors: Klindworth, Walter Allan
• Application Number:
  US14/027,193
• Publication Number:
  US 20140081652A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 20/00 Payment architectures, schemes or protocols apparatus for performing or posting payment transactions G07F7/08, G07F19/00; electronic cash registers G07G1/12
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 40/08 Insurance
• 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: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The Automated Healthcare Risk Management System is a real-time Software as a Service application which interfaces and assists investigators, law enforcement and risk management analysts by focusing their efforts on the highest risk and highest value healthcare payments. The system'"'"'s Risk Management design utilizes real-time Predictive Models, a Provider Cost Index, Edit Analytics, Strategy Management, a Managed Learning Environment, Contact Management, Forensic GUI, Case Management and Reporting System for individually targeting, identifying and preventing fraud, abuse, waste and errors prior to payment. The Automated Healthcare Risk Management System analyzes hundreds of millions of transactions and automatically takes actions such as declining or queuing a suspect payment. Claim payment risk is optimally prioritized through a Managed Learning environment, from high risk to low risk for efficient resolution by investigators.

Claims

1. A method for identifying and preventing improper healthcare payments, comprising the steps of:

a. access data on historic claims;

b. analyze the data to create a predictive scoring model;

c. access at least one current claim to process;

d. calculate at least one fraud and abuse score for the at least one current claim;

e. provide reason codes to support the calculated fraud and abuse score for the at least one current claim;

f. process the at least one claim against a Provider Cost Index;

g. process the at least one claim using Edit Analytics decision logic;

h. sort and rank the at least one claim based upon the at least one predictive model score, Provider Cost Index and Edit Analytics failures,

whereby the capability to cost-effectively identify, queue and present only the highest-risk and highest value claims to investigate.

2. The method of claim 1 wherein the predictive scoring model is based on using non-parametric statistical measures.

3. The method of claim 1 wherein the at least one fraud and abuse score can comprise one or more of a sub-claim score, a provider score or a time score.

4. The method of claim 1 wherein the at least one predictive model score which is used as part of the sorting and ranking comprises a plurality of empirically derived and statistically valid model scores generated by multi-dimensional statistical algorithms and probabilistic predictive models that identify providers, healthcare merchants, beneficiaries or claims as potentially fraudulent or abusive.

5. The method of claim 1 further including the step of creating empirical decision criteria and decision parameters in real-time, using the predictive models, scores, Provider Cost Index, Edit Analytics results or data to systematically evaluate, trigger and investigate specific claims or transactions, created by providers, healthcare merchants, beneficiaries and facilities who are determined to be risky.

6. The method of claim 1 further including the step of randomly testing new models, data, actions, treatments and contact methods against control positions and measure incremental benefits using a Managed Learning Environment.

7. The method of claim 1 further including the step of deploy dynamic real-time or batch queuing, so that immediate results can be accessed via a Forensic Graphical User Interface (GUI), with Case Management by multiple investigator levels of experience and stake holders selected from the group consisting of nurses, physicians, medical investigators, law enforcement, adjustors and risk management experts.

8. The method of claim 1 further including the step of utilizing nurses, physicians, medical investigators, law enforcement or adjustors to research and interrogate claims, providers, healthcare merchants or beneficiaries, triggered by decision strategies, and provide timely resolution to complex improper payment scenarios.

9. The method of claim 1 further including the step of executing a Feedback Loop and systematically optimizing decision strategies, contact management strategies, treatment and actions, as well as measure the incremental benefit of a test over a control position.

10. The method of claim 1 further including the step of empirically optimizing strategy management algorithms that are designed to adapt to changing patterns of cost dynamics for improper payments.

11. The method of claim 1 further including the step of performing population risk adjustment modeling and profiling capabilities, to allow an investigator a mathematical and graphical capability to normalize population health and co-morbidity and follow beneficiary care and provider services and treatments across all healthcare segments, provider specialty groups, healthcare merchants, geographies and market segments.

12. The method of claim 1 further including the step of performing empirical comparisons and statistical analyses performed on “

similar”

types of claims, providers, healthcare merchants and beneficiaries, using statistical methods, including but not limited to methods such as Chi-Square.

13. The method of claim 1 further including the step of treatment optimization, in which new treatments are tested, using unbiased and scientifically approved sampling methods or techniques, to improve efficiency and effectiveness, through a Managed Learning Environment.

14. The method of claim 13 wherein the treatments are selected from the group consisting of queue, research, payment, decline payment, educate, and add a provider to a warning list.

15. The method of claim 7 wherein dynamic navigation is provided through the Forensic Graphical User Interface that allows a user to quickly navigate through a complex collection, but efficiently organized, amount of data to quickly identify, fraudulent, abusive, wasteful or compliance edit failure activity by an entity, and efficiently bring resolution such as decline, pay or queue.

16. The method of claim 1 further including the step of systematic analysis and reporting of score performance results, including:

a. A Feedback Loop to dynamically update model coefficients or probabilistic decision strategies, as well as monitor emerging improper payment trends in a real-time fashion;

b. Validation and on-demand queue reporting available to track improper payment identification and model and strategy validations;

c. Complete cost benefit analysis that provides normalized estimates for fraud and abuse prevention, detection or recovery;

d. Risk adjusted waste, over servicing or overutilization assessments that calculate provider cost or waste indexes, that are presented mathematically and graphically for use in educating the provider or creating cohort benchmarks for determining punitive actions;

e. Error assessment analysis and recovery estimates, and

f. Business reports that summarize risk management performance, provide standard, ad hoc, customizable and dynamic reporting capabilities to summarize performance, statistics and to better manage fraud, abuse, over-servicing, over-utilization, waste and error prevention and return on investment.

17. The method of claim 1 further including the step of providing real-time triggers to activate intelligence capabilities, combined with predictive scoring models, to take action when risk thresholds are exceeded.

18. The method of claim 1 further including the step of providing real time monitoring, measuring, identification and visual presentation of performance and changing patterns of fraud or abuse in a dashboard format for an operations (“

ops”

) room, control room or war-room type display environment.

19. The method of claim 1 further including the step of securely memorializing investigations, documentation, action, files and data through an internal or external case management system that can be accessed through multiple electronic mediums, including, but not limited to a smartphone, a computer, a tablet or a notepad.

20. The method of claim 1 further including the step of providing investigator analysis and real time filters, which allows a healthcare investigator to explore complex data relationships and underlying individual transactions, as identified by the mathematical algorithms and probabilistic model scores and their associated reason codes when a provider, healthcare merchant, beneficiary or claim is identified as high risk.

21. The method of claim 1 further including the step of statistically and empirically comparing a unique provider'"'"'s activities with activities of similar populations to contrast provider behavior for those providers who are identified as high risk.

22. The method of claim 1 further including the step of statistically and empirically comparing a unique healthcare merchant activities with activities of similar populations to contrast healthcare merchant behavior for those healthcare merchants who are identified as high risk.

23. The method of claim 1 further including the step of statistically and empirically comparing a unique beneficiaries activities with activities of similar populations to contrast healthcare merchant behavior for those healthcare merchants who are identified as high risk.

24. The method of claim 1 further including the step of statistically and empirically comparing a unique claim activities with activities of similar populations to contrast claims behavior for those claims which are identified as high risk.

25. The method of claim 1 further including the step of statistically and empirically comparing a unique facility activities with activities of similar populations to contrast facility behavior for those facilities which are identified as high risk.

26. The method of claim 1 further including the step of dynamically view dimensions, in real time, that contain automated and targeted reports for researching and resolving fraud, abuse, waste, over-servicing or over-utilization quickly and efficiently.

27. An internet software service for identifying and preventing improper healthcare payments comprising:

a server connected to the internet, the server containing a program running in memory which is configured to;

a. access data on historic claims;

b. analyze the data to create a predictive scoring model;

c. access at least one current claim to process;

d. calculate at least one fraud and abuse score for the at least one current claim;

e. provide reason codes to support the calculated fraud and abuse score for the at least one current claim;

f. process the at least one claim against a Provider Cost index;

g. process the at least one claim using Edit Analytics decision logic;

h. sort and rank the at least one claim based upon the at least one predictive model score, Provider Cost Index and Edit Analytics failures,

whereby the capability to cost-effectively identify, queue and present only the highest-risk and highest value claims to investigate.

28. An Automated Healthcare Risk Management System comprised of:

a. Hosted Software as a Service technology design;

b. Real-time multi-dimensional predictive models to identify individual healthcare cost dynamic fraud;

c. Real-time multi-dimensional predictive models to identify individual healthcare cost dynamic abuse;

d. Real-time multi-healthcare segment population risk-adjusted provider cost index to identify individual healthcare cost dynamic waste;

e. Real-time multi-healthcare segment edit analytics to identify individual healthcare cost dynamic errors;

f. Strategy manager to cost-effectively identify, queue and present only the highest-risk and highest value claims to investigators, as identified by any combination of predictive model score, provider cost index or edit analytics;

g. Managed learning environment, combined with contact management, to segment populations for organizing test/control actions and treatments to measure and maximize return;

h. Forensic graphical user interface, combined with case management reporting system to efficiently navigate, investigate and pursue suspect cases as presented by the strategy manager and managed learning environment.

29. Utilizing a computerized method of claim 28 to uniquely identify the individual healthcare cost dynamics of fraud, abuse, waste and errors using individualized methods:

a. Determining the healthcare state of fraud individually using a computer method to review healthcare claims prior to payment;

b. Determining the healthcare state of abuse individually using a computer method to review healthcare claims prior to payment;

c. Determining the healthcare state of waste individually using a computer method to review healthcare claims prior to payment, and

d. Determining the healthcare state of errors individually using a computer method to review healthcare claims prior to payment.

30. Utilizing the computerized method of claim 28 to review millions of healthcare claims over a selected time period in a real-time fashion.

31. Utilizing the computerized method of claim 28 to individually identify healthcare fraud cost dynamic using predictive models to review healthcare claims prior to payment.

32. Utilizing the computerized method of claim 28 to individually identify healthcare abuse cost dynamic using predictive models to review healthcare claims prior to payment.

33. Utilizing the computerized method of claim 28 to individually identify healthcare waste cost dynamic using population health risk-adjusted models to review healthcare claims prior to payment.

34. Utilizing the computerized method of claim 28 to individually identify healthcare error cost dynamic using industry approved compliance edits and client proprietary edits to review healthcare claims prior to payment.

35. The method of claim 31, wherein the healthcare states are providers providing procedures to clients.

36. The method of claim 31, wherein the healthcare states are healthcare merchants providing procedures to clients.

37. The method of claim 31, wherein the healthcare states are facilities providing procedures to clients.

38. The method of claim 31, wherein the healthcare states are services codes, procedure codes, revenue codes or diagnosis related group for healthcare procedures.

39. The method of claim 31, wherein the healthcare states are:

a. The healthcare providers;

b. The healthcare merchant;

c. Are the healthcare facility;

d. Are the healthcare beneficiary (patient, member or customer).

40. The method of claim 31, wherein the healthcare states are:

a. Provider-days, provider-months, provider quarters or provider-years;

b. Healthcare merchant-days, healthcare-months, healthcare quarters or healthcare-years;

c. Facility-days, facility-months, facility quarters or facility-years, and

d. Beneficiary-days, beneficiary-months, beneficiary-quarters or beneficiary-years.

41. A method of detecting fraud or abuse or waste or errors individually, in the healthcare industry, the method comprising:

a. Inputting historical claims data;

b. Developing scoring variables from the historical claims data;

c. Developing claim, provider, healthcare merchant and patient statistical behavior patterns by specialty group, facility, provider geography and patient geography and demographics based on the historical healthcare claims data and other external data sources and external scores, and/or link analysis;

d. Inputting at least one claim, or components of the claim, for scoring;

e. Combining the variables into the predictive model by calculating a probability score, and

f. Determining a score for at least one claim, using the predictive model selected from the group consisting of the predictive model which detects fraud, the predictive model which detects abuse, the predictive model which detects waste.

43. The method of claim 41 including the step of creating empirical decision criteria and decision parameters real time, within a strategy manager, using for example, predictive models, scores, provider cost index, edit analytic results or internal or external data to systematically evaluate, trigger and investigate specific claims or transactions, created by providers, healthcare merchants or beneficiaries who were determined to be risky.

44. The method of claim 41 including the step of utilizing a managed learning environment, with contact management design embedded within strategy manager to randomly test new concepts, models, data, actions, treatments and contact methods against control positions and measure incremental benefits.

45. The method of claim 41 including the step of deploying real time or batch queuing, based upon strategy manager criteria, managed learning environment and contact management design, where immediate results can be accessed via a Forensic Graphical User Interface (GUI), with Case Management by nurses, physicians, medical investigators, law enforcement or adjustors and risk management experts.

46. The method of claim 41 including the step of executing a feedback loop and systematically capture actions, outcomes and performance.

47. Utilizing the method of claim 43 of the strategy manager to:

a. Create real-time queues for investigators to access suspect providers, healthcare merchants, beneficiaries and facilities;

b. Make real-time changes to strategies, criteria or thresholds to quickly respond to emerging trends of fraud, abuse, waste, errors;

c. Access external data or external scores to include in strategies or criteria;

d. Take automated actions such as pay, decline, queue or educate based upon risk and expected value of suspect claim, provider, healthcare merchant, beneficiary or facility, and

e. Status suspect claim, provider, healthcare merchant, beneficiary or facility as fraud, abuse, waste or error.

48. Utilizing the method of claim 44 of the managed learning environment to:

a. Utilize experimental design with random digits to test different actions or treatments at claim-level, provider-level, healthcare-merchant level, beneficiary-level and facility-level

b. Test new strategies, models, actions, treatments and data against the control position;

c. Measure the incremental benefit of a test over a control position through controlled testing, and

d. Utilize contact management to optimize interaction costs and outcomes from touch points such as letter, email, call, face to face meeting between investigators and participants such as provider, healthcare merchant, beneficiary or facility.

49. Utilizing the method of claim 45 of the forensic graphical user interface to:

a. Investigate fraud, abuse, waste and errors individually within segregated queues and screens;

b. Access 1-2 years of historical procedure, claim, provider, healthcare merchant, beneficiary or facility data with only a click of a mouse;

c. Execute efficient resolution to suspect cases identified using transparent reason codes from models, cost index and edit analytics; and

d. Memorialize case outcomes via notes, actions taken and data in the case management design.

50. Utilizing the method of claim 46 of the feedback loop to:

a. Systematically update predictive model coefficients for fraud models using feedback loop outcomes;

b. Systematically update predictive model coefficients for abuse models using feedback loop outcomes;

c. Systematically update provider cost index using feedback loop outcomes;

d. Automatically adjust strategy manager, including actions and treatments based upon feedback loop outcomes, and

e. Measure the incremental benefit of a strategy, model or data test over the control position.

42. A method of detecting errors individually, in the healthcare industry, the method comprising:

a. Inputting historical claims data;

b. Developing edit analytics variables from the historical claims data;

c. Developing edit compliance errors by specialty group, facility, provider geography and patient geography and demographics based on the historical healthcare claims data and other external data sources and external scores, and/or link analysis;

d. Inputting at least one claim, or components of the claim, for calculating the edit analytics;

e. Combining the variables into the edit analytics by applying compliance or client edits, and

f. Determining an edit failure for at least one claim, using the edit analytics which determines errors.