User feedback in semi-automatic question answering systems

• US 8,463,673 B2
• Filed: 09/23/2011
• Issued: 06/11/2013
• Est. Priority Date: 09/23/2010
• Status: Active Grant

• Current Assignee: 3M Health Information Systems Inc (Solventum Corp.)
• Original Assignee: MModal IP LLC (3M Company)
• Inventors: Koll, Detlef, Polzin, Thomas
• Primary Examiner(s): Shaawat, Mussa A
• Application Number:
  US13/242,532
• Publication Number:
  US 20120078763A1
• Time in Patent Office: 627 Days
• Field of Search: None
• US Class Current: 705/34
• CPC Class Codes: G06Q 30/04 Billing or invoicing

Abstract

A system applies rules to a set of documents to generate codes, such as billing codes for use in medical billing. A human operator provides input specifying whether the generated codes are correct. Based on the input from the human operator, the system attempts to identify which clause(s) in the rules which were relied on to generate the particular code are correct and which such clause(s) are incorrect. The system then assigns praise to components of the system responsible for generating codes in the correct clauses, and assigns blame to components of the system responsible for generating codes in the incorrect clauses. Such blame and praise may then be used to determine whether particular code-generating components are insufficiently reliable. The system may disable, or take other remedial action in response to, insufficiently reliable code-generating components.

Claims

1. A method performed by at least one computer processor executing computer program instructions tangibly stored on at least one non-transitory computer-readable medium,

the method for use with a system including a data source and a first billing code,

the method comprising using the at least one computer processor to perform operations of:

(A) receiving input from a user, wherein the input represents a verification status of the first billing code;

(B) applying first inverse logic to the input, the billing code, and a set of forward logic, to identify first and second concept extraction components, wherein (B) comprises;

(B)(1) identifying a first logic component that generated the first billing code, wherein the first logic component comprises means for implementing first logic, wherein the first logic includes a first condition, wherein the first condition includes a first sub-condition and a second sub-condition; and

(B)(2) applying first inverse logic to the input received from the user to identify at least one of the first and second sub-conditions; and

(C) applying reinforcement to the first and second concept extraction components, comprising;

(B)(1) determining whether the verification status indicates that the first billing code is accurate;

(B)(2) if the verification status indicates that the first billing code is inaccurate, then applying negative reinforcement to the first and second concept extraction components, comprising apportioning the negative reinforcement between the first and second concept extraction components.

2. The method of claim 1, wherein (C) further comprises:

(B)(3) if the verification status does not indicate that the first billing code is inaccurate, then applying positive reinforcement to the first and second concept extraction components, comprising apportioning the positive reinforcement to the first and second concept extraction components.

3. The method of claim 1, further comprising:

(D) determining whether the first concept extraction component is unreliable at generating concept codes; and

(E) if the first concept extraction component is determined to be unreliable at generating concept codes, then;

(B)(1) at the first concept extraction component, generating a concept code; and

(B)(2) requiring human review of the concept code before adding the concept code to the data source.

4. The method of claim 1, further comprising:

(D) determining whether the first concept extraction component is unreliable at generating concept codes; and

(E) if the first concept extraction component is determined to be unreliable at generating concept codes, then;

(E)(1) at the identified concept extraction component, generating a concept code;

(E)(2) at a logic component in the system, generating a second billing code based on the concept code; and

(E)(3) requiring human review of the second billing code before adding the billing code to the system.

5. The method of claim 1, wherein (B) comprises:

(B)(1) determining that the first concept extraction component includes means for generating concept codes representing instances of a first concept;

(B)(2) determining that the first billing code was generated by a first logic component in reliance on a concept code representing an instance of the first concept;

(B)(3) identifying the first concept extraction component based on the determination that the first billing code was generated by the first logic component.

6. The method of claim 1, wherein a first reliability score is associated with the first concept extraction component, wherein the first reliability score represents an estimate of a first degree to which the first concept extraction component generates concept codes accurately, and

wherein applying the negative reinforcement comprises associating a second reliability score with the first concept extraction component, wherein the second reliability score represents an estimate of a second degree to which the first concept extraction component generates concept codes accurately, wherein the second degree is lower than the first degree.

7. The method of claim 1, wherein (B) comprises:

(B)(1) identifying a first logic component that generated the first billing code;

(B)(2) identifying, based on the input from the user, a concept relied upon by the first logic component to generate the first billing code; and

(B)(3) identifying the first concept extraction component based upon the concept relied upon by the first logic component.

8. The method of claim 7, wherein (B)(3) comprises identifying the first concept extraction component by determining that the first concept extraction component generates concept codes representing instances of the concept relied upon by the first logic component.

9. The method of claim 1, wherein (B)(2) comprises identifying exactly one of the first and second sub-conditions, and wherein (B) further comprises:

(B)(1) identifying a first concept that satisfies the identified one of the first and second sub-conditions; and

(B)(2) identifying a concept extraction component comprising means for generating concept codes representing instances of the first concept.

10. The method of claim 1, wherein (B)(2) comprises identifying both of the first and second sub-conditions.

11. A non-transitory computer-readable medium comprising computer-readable instructions tangibly stored on the computer-readable medium, wherein the instructions are executable by at least one computer processor to perform a method for use with a system including a data source and a first billing code, the method comprising:

(A) receiving input from a user, wherein the input represents a verification status of the first billing code;

(B) applying first inverse logic to the input, the billing code, and a set of forward logic, to identify first and second concept extraction components, wherein (B) comprises;

(B)(1) identifying a first logic component that generated the first billing code, wherein the first logic component comprises means for implementing first logic, wherein the first logic includes a first condition, wherein the first condition includes a first sub-condition and a second sub-condition; and

(B)(2) applying first inverse logic to the input received from the user to identify at least one of the first and second sub-conditions; and

(C) applying reinforcement to the first and second concept extraction components, comprising;

(B)(1) determining whether the verification status indicates that the first billing code is accurate;

(B)(2) if the verification status indicates that the first billing code is inaccurate, then applying negative reinforcement to the first and second concept extraction components, comprising apportioning the negative reinforcement between the first and second concept extraction components.

12. The computer-readable medium of claim 11, wherein (C) further comprises:

(B)(3) if the verification status does not indicate that the first billing code is inaccurate, then applying positive reinforcement to the first and second concept extraction components, comprising apportioning the positive reinforcement to the first and second concept extraction components.

13. The computer-readable medium of claim 11, further comprising:

(D) determining whether the first concept extraction component is unreliable at generating concept codes; and

(E) if the first concept extraction component is determined to be unreliable at generating concept codes, then;

(B)(1) at the first concept extraction component, generating a concept code; and

(B)(2) requiring human review of the concept code before adding the concept code to the data source.

14. The computer-readable medium of claim 11, further comprising:

(F) determining whether the first concept extraction component is unreliable at generating concept codes; and

(G) if the first concept extraction component is determined to be unreliable at generating concept codes, then;

(E)(4) at the identified concept extraction component, generating a concept code;

(E)(5) at a logic component in the system, generating a second billing code based on the concept code; and

(E)(6) requiring human review of the second billing code before adding the billing code to the system.

15. The computer-readable medium of claim 11, wherein (B) comprises:

(B)(4) determining that the first concept extraction component includes means for generating concept codes representing instances of a first concept;

(B)(5) determining that the first billing code was generated by a first logic component in reliance on a concept code representing an instance of the first concept;

(B)(6) identifying the first concept extraction component based on the determination that the first billing code was generated by the first logic component.

16. The computer-readable medium of claim 11, wherein a first reliability score is associated with the first concept extraction component, wherein the first reliability score represents an estimate of a first degree to which the first concept extraction component generates concept codes accurately, and

wherein applying the negative reinforcement comprises associating a second reliability score with the first concept extraction component, wherein the second reliability score represents an estimate of a second degree to which the first concept extraction component generates concept codes accurately, wherein the second degree is lower than the first degree.

17. The computer-readable medium of claim 11, wherein (B) comprises:

(B)(4) identifying a first logic component that generated the first billing code;

(B)(5) identifying, based on the input from the user, a concept relied upon by the first logic component to generate the first billing code; and

(B)(6) identifying the first concept extraction component based upon the concept relied upon by the first logic component.

18. The computer-readable medium of claim 17, wherein

(B)(3) comprises identifying the first concept extraction component by determining that the first concept extraction component generates concept codes representing instances of the concept relied upon by the first logic component.

19. The computer-readable medium of claim 11, wherein (B)(2) comprises identifying exactly one of the first and second sub-conditions, and wherein

(B) further comprises:

(B)(3) identifying a first concept that satisfies the identified one of the first and second sub-conditions; and

(B)(4) identifying a concept extraction component comprising means for generating concept codes representing instances of the first concept.

20. The computer-readable medium of claim 11, wherein (B)(2) comprises identifying both of the first and second sub-conditions.