Computer architecture and process of patient generation, evolution, and simulation for computer based testing system

• US 20060004608A1
• Filed: 09/08/2005
• Published: 01/05/2006
• Est. Priority Date: 10/30/1996
• Status: Active Grant

• Current Assignee: American Board of Family Medicine, Inc.
• Original Assignee: American Board Of Family Practice, Inc.
• Inventors: Truszczynski, Miroslav, Marek, Victor W., Sumner, Walton II, Rovinelli, Richard J.
• Granted Patent: US 7,653,556 B2
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G09B 23/28 for medicine
• CPC Class Codes: G09B 7/02 of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics

Abstract

A computer implemented simulation and evaluation method simulates interventions to a patient by a user, and evaluates the interventions responsive to predetermined criteria and the interventions. The method includes defining a test area to evaluate the user to at least one of predetermined criteria and a user profile, selecting genetic information of the patient responsive to the test area, and generating a patient history responsive to the test area and the genetic information. The method also includes receiving at least one intervention input by the user, and evaluating the user responsive to the intervention and predetermined criteria.

Claims

1. A computer implemented method for training a user regarding the operation of a complex system, including a patient having at least one predetermined health state, said method comprising the steps of:

(a) generating an initial patient history state, wherein said initial patient history state comprises a predetermined set of health states; and

(b) iterating through a prescribed treatment protocol with said user, wherein said iterating step comprises evolving, through at least one intermediate health state, the initial patient history state forward in time to a predetermined subsequent patient history health state.

2. A computer implemented method according to claim 1, further comprising the steps of:

(c) accessing a profile for said user;

(d) selecting a training area to evaluate said user based on at least the user'"'"'s profile;

(e) dynamically generating multiple instances of patients responsive to said training area.

3. A computer implemented method according to claim 1, further comprising the steps of:

(c) evolving the patient to a subsequent health state responsive to the at least one intervention; and

(d) evaluating the user responsive to the at least one intervention input by the user.

4. A computer implemented method according to claim 1, further comprising the steps of:

(c) evolving the patient to a subsequent health state responsive to the at least one intervention and the patient history; and

(d) evaluating the user responsive to at least one of the at least one intervention input by the user, and the subsequent health state.

5. A computer implemented method according to claim 1, further comprising the steps of:

(c) evolving the patient to a subsequent health state responsive to the at least one intervention and the patient history;

(d) receiving at least one other intervention input by the user;

(e) evolving the patient responsive to the at least one other intervention to at least one other subsequent health state; and

(f) evaluating the user responsive to at least one of the at least one other intervention, the at least one subsequent health state, and the at least one other subsequent health state.

6. A computer implemented method according to claim 5, wherein said evolving step (c) uses an entity relationship model.

7. A computer implemented method according to claim 6, wherein the entity relationship model comprises population, record, agents of change, health states, findings and courses of action.

8. A computer implemented method according to claim 7, wherein the findings include specific findings, patterns and sub-patterns describing patient behaviors and characteristics.

9. A computer implemented method according to claim 8, wherein the patterns describe one or more features over time.

10. A computer implemented method according to claim 8, wherein the sub-patterns describe consequences of patient related events.

11. A computer implemented method according to claim 8, wherein the patterns model time and characterize interrelated medical observations.

12. A computer implemented method according to claim 8, further comprising the step of performing a differential diagnosis responsive to the findings, the patterns and the sub-patterns.

13. A computer implemented method according to claim 8, wherein confidence in a presence of the patterns increases with passage of time.

14. A computer implemented method according to claim 7, wherein the courses of action describe tasks and methods used to apply, modify, and evaluate health state information and characteristics described in the entity relationship model.

15. A computer implemented method according to claim 7, wherein the courses of action describe patient activities, including at least one of medical and non-medical activities.

16. A computer implemented method according to claim 7, wherein the courses of action describe potential interventions input by the user including at least one of diagnostic and management strategies.

17. A computer implemented method according to claim 7, wherein the courses of action comprise one or more elementary courses of action used to construct at least one course of action, one or more types of elementary courses of action corresponding to the one or more elementary course of action, and weighting factors corresponding to the one or more elementary courses of action.

18. A computer implemented method according to claim 6, wherein the entity relationship model includes entity relations.

19. A computer implemented method according to claim 18, further comprising the step of evolving the patient responsive to the at least one intervention, the entity relations and the patient history to at least one subsequent health state.

20. A computer implemented method according to claim 6, wherein the at least one intervention by the user is considered by the entity relationship model in evolving the patient from a first health state to a subsequent health state.

21. A computer implemented method according to claim 6, wherein the entity relationship model includes one or more of the following relations between entities:

Population Contacts Population

Population Related to Population

Population Interacts with Courses of Action

Population Exposed to Agents of Change

Population Has Health States

Population Exhibits Findings

Agents of Change Cause Health States

Health States Lead to Health States

Findings Associated with Health States

Findings Link to Findings

Course of Action use Agents of Change

Courses of Action Identify Agents of Change

Courses of Action Treat Health States

Courses of Action Alter Findings

Courses of Action Reveal Findings

Courses of Action Evaluation Findings.

22. A computer implemented method according to claim 7, wherein the entity relationship model links the findings with the patterns to a health state, rather than linking a range of finding values to the health state.

23. A computer implemented method according to claim 7, wherein the patterns include sensitivity and specificity represented as age dependent, rather than as constants.

24. A computer implemented method according to claim 1, wherein said generating patient history step (a) is executed once for each simulation to generate the patient history used in said computer implemented simulation and evaluation method.

25. A computer implemented method according to claim 3, further compromising the step of repeating said evolving step (c), and said evaluating step (d) a plurality of times.

26. A computer implemented method according to claim 1, further comprising the step of generating the patient history comprising a progression of health states and risk factors traversed by the patient from a normal health condition to a specified health condition.

27. A computer implemented method according to claim 1, further comprising the step of generating the patient history backwards in time from a specified health condition to a normal health condition including successive precursor health states and onset times therebetween.

28. A computer implemented method according to claim 1, further comprising the step of generating the patient history using a Monte Carlo process to generate a plurality of potential patient histories.

29. A computer implemented method according to claim 6, wherein the entity relationship model utilizes tree structures to describe a probability density function conditioned on comorbidities, treatments, risk factors, and the interventions.

30. A computer implemented method according to claim 6, wherein the entity relationship model includes diagnostic complexities and disease interaction.

31. A computer implemented method according to claim 1, wherein parallel networks of health states are used to model transactions that occur among a set of health conditions responsive to the at least one intervention by the user.

32. A computer implemented method according to claim 31, wherein the parallel networks of health states describe at least one of a chronic condition and non-chronic condition.

33. A computer implemented method according to claim 32, wherein the non-chronic condition includes acute exacerbations describing acute flares of illness that occur during a more chronic health condition.

34. A computer implemented method according to claim 31, wherein the parallel networks of health states form at least one of the following interactions:

(1) independent interaction between parallel networks so that patient evolution between first and second parallel networks are unrelated to each other;

(2) unilateral interaction between the parallel networks so that patient evolution on a first parallel network is unrelated to patient evolution on a second parallel network, and patient evolution on the second parallel network is related to the patient evolution on the first parallel network; and

(3) mutually dependent interaction between the parallel networks so that patient evolution between the first and second parallel networks are related to each other.

35. A computer implemented method according to claim 3, further comprising the step of repeating said evolving step (c) responsive to:

(1) parallel health states of the patient; and

(2) a target health state and health state combinations that lead to different parallel states.

36. A computer implemented method according to claim 31, wherein the parallel networks of health states comprise:

(1) a primary network including primary health conditions defining a health domain;

(2) a risk factor network including risk factors for progression through the primary network; and

(3) complications attributed to treating the primary health conditions in the primary network.

37. A computer implemented method according to claim 36, wherein the parallel networks of health states are generated using the following information:

(1) how long at least one of the risk factors exists before influencing a transition between primary health conditions in the primary network;

(2) time required for transitions in the primary network, considering different combinations of the risk factors; and

(3) number of transitions the patient is allowed to make between a specified health state and a normal health state.

38. A computer implemented method for training a user on patient treatment practices through the use of patient simulation, said method comprising:

selecting subject matter on which to train the user;

generating a first target health state of a patient, wherein said first target health state is determined by said subject matter;

generating a medical history of said patient; and

iterating through at least one subsequent target health state in response to at least one query said patient to a final target health state, wherein said iterating comprises;

(a) prompting a user to enter at least one predetermined query;

(b) evolving said first target health state forward in time in response to said at least one predetermined query including at least one of an intervention and a request for additional information regarding the patient to a second target health state; and

(c) repeating (a) and (b) until treatment of said patient is completed.