SKILL EVALUATION

• US 20070172803A1
• Filed: 08/22/2006
• Published: 07/26/2007
• Est. Priority Date: 08/26/2005
• Status: Abandoned Application

• Current Assignee: University of Washington
• Original Assignee: University of Washington
• Inventors: Rosen, Jacob, Hannaford, Blake, Brown, Jeffrey, Chang, Lily, Sinanan, Mika, Kowalewski, Timothy
• Application Number:
  US11/466,269
• Publication Number:
  US 20070172803A1
• Time in Patent Office: Days
• US Class Current: 434/262
• CPC Class Codes: A61B 2017/00707 Dummies, phantoms; Devices simulating patient or parts of patient
• CPC Class Codes: G09B 23/285 for injections, endoscopy, bronchoscopy, sigmoidscopy, insertion of contraceptive devices or enemas
• 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

Software tools, methods and apparatus for objectively assessing surgical and medical procedural skills are described. Data corresponding to performance of a manipulative task by a subject is modeled using Markov modeling techniques and compared with stored models corresponding to each of a plurality of proficiency levels. A particular proficiency level is selected based on proximity of the subject data relative to each of the stored models.

Claims

1. A system comprising:

a data receiver for receiving subject performance data corresponding to performance of a manipulative task by the subject;

a database including a plurality of models, each particular model in one to one relation with a particular proficiency level of a plurality of proficiency levels, wherein each model corresponds to performance of the manipulative task at a particular proficiency level; and

a processor coupled to the database and data receiver and configured to generate a specimen model corresponding to the subject performance data and configured to select a proficiency level for the subject based on proximity between the specimen model and each of the plurality of models.

2. The system of claim 1 wherein the data receiver includes at least one of a surgical robot, an instrumented tool, and a simulator.

3. The system of claim 1 further including an output device coupled to the processor.

4. The system of claim 3 wherein the output device includes at least one of a printer, a display, a transmitter, and a network interface.

5. The system of claim 1 wherein the processor is configured to execute a set of instructions for generating a model corresponding to skill.

6. The system of claim 1 wherein the processor is configured to execute a set of instructions for generating a statistical model.

7. The system of claim 1 wherein the processor is configured to execute a set of instructions for generating at least one of a Markov model and a hidden Markov model.

8. The system of claim 1 wherein the processor is configured to execute a set of instructions for generating a fuzzy logic model.

9. The system of claim 1 wherein the data receiver includes an instrumented surgical tool having an output corresponding to at least one of kinematics, contact information between the tool and a medium, and a recorded display of a surgical scene.

10. A method comprising:

receiving subject performance data corresponding to performance of a manipulative task by the subject;

accessing a database including a plurality of models, each particular model in one to one relation with a particular proficiency level of a plurality of proficiency levels, wherein each model corresponds to performance of the manipulative task at a particular proficiency level;

generating a specimen model corresponding to the subject performance data; and

selecting a proficiency level for the subject based on proximity between the specimen model and each of the plurality of models.

11. The method of claim 10 wherein generating the specimen model includes generating a model corresponding to skill.

12. The method of claim 10 wherein generating the specimen model includes generating a statistical model.

13. The method of claim 10 wherein generating the specimen model includes generating at least one of a Markov model and a hidden Markov model.

14. The method of claim 10 wherein generating the specimen model includes generating a fuzzy logic model.

15. The method of claim 10 wherein generating the specimen model includes generating model data for at least one prime element.

16. The method of claim 10 wherein selecting the proficiency level includes determining a probability.

17. The method of claim 10 wherein selecting the proficiency level includes computing a generalized finding.

18. The method of claim 10 wherein selecting the proficiency level includes computing a fuzzy logic membership function.

19. The method of claim 10 wherein receiving subject performance data includes receiving data from a plurality of sensors.

20. The method of claim 19 wherein receiving data from the plurality of sensors includes receiving data from at least one of a force sensor, a torque sensor, a position sensor, a velocity sensor, an acceleration sensor, a pressure sensor, a visual display of a scene being analyzed, a clock, and a temperature sensor.

21. A computer readable medium having instructions stored thereon for causing a computer to implement a method comprising:

receiving subject performance data corresponding to performance of a manipulative task by the subject;

accessing a database including a plurality of models, each particular model in one to one relation with a particular proficiency level of a plurality of proficiency levels, wherein each model corresponds to performance of the manipulative task at a particular proficiency level;

generating a specimen model corresponding to the subject performance data; and

selecting a proficiency level for the subject based on proximity between the specimen model and each of the plurality of models.

22. The computer readable medium of claim 21 wherein generating the specimen model includes generating a model corresponding to skill.

23. The computer readable medium of claim 21 wherein generating the specimen model includes generating a statistical model.

24. The computer readable medium of claim 21 wherein generating the specimen model includes generating at least one of a Markov model and a hidden Markov model.

25. The computer readable medium of claim 21 wherein generating the specimen model includes generating a fuzzy logic model.

26. The computer readable medium of claim 21 wherein generating the specimen model includes generating model data for at least one prime element.

27. The computer readable medium of claim 21 wherein selecting the proficiency level includes determining a probability.

28. The computer readable medium of claim 21 wherein receiving subject performance data includes receiving data from a plurality of sensors.

29. The computer readable medium of claim 28 wherein receiving data from the plurality of sensors includes receiving data from at least one of a force sensor, a torque sensor, a position sensor, a velocity sensor, an acceleration sensor, a pressure sensor, a visual display of a scene being analyzed, a clock, and a temperature sensor.

30. A system comprising:

a data receiver for receiving subject performance data corresponding to performance of a manipulative task by the subject;

at least one model corresponding to performance of the task at a particular proficiency level; and

a processor coupled to the data receiver and configured for classifying the subject performance data relative to the at least one model.

31. The system of claim 30 wherein the data receiver includes at least one of a surgical robot, an instrumented tool, and a simulator.

32. The system of claim 30 further including an output device coupled to the processor.

33. The system of claim 32 wherein the output device includes at least one of a printer, a display, a transmitter, and a network interface.

34. The system of claim 30 wherein the processor is configured to execute a set of instructions for generating at least one of a Markov model and a hidden Markov model.

35. The system of claim 30 wherein the processor is configured to execute a set of instructions for generating a model corresponding to skill.

36. The system of claim 30 wherein the processor is configured to execute a set of instructions for generating a statistical model.

37. The system of claim 30 wherein the processor is configured to execute a set of instructions for generating a fuzzy logic model.