Disease simulation system and method

• US 20010047252A1
• Filed: 03/16/2001
• Published: 11/29/2001
• Est. Priority Date: 01/10/1997
• Status: Active Grant

• Current Assignee: Health Hero Network Incorporated (Robert Bosch GmbH)
• Original Assignee: Health Hero Network Incorporated (Robert Bosch GmbH)
• Inventors: Brown, Stephen J.
• Granted Patent: US 7,167,818 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/6896 Toys
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G01N 2035/00891 Displaying information to the operator
• CPC Class Codes: G01N 33/48792 Data management, e.g. communication with processing unit for in vivo diagnostics A61B5/0002; transmission systems for measured values G08C
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 20/13 delivered from dispensers
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/63 for local operation
• 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: G16H 70/20 relating to practices or guidelines
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics

Abstract

A system and method for predicting the effect of patient self-care actions on a disease control parameter. A future disease control parameter value X(t_j) at time t_j is determined from a prior disease control parameter value X(t_i) at time t_i based on an optimal control parameter value R(t_j) at time t_j, the difference between the prior disease control parameter value X(t_i) and an optimal control parameter value R(t_i) at time t_i, and a set of differentials between patient self-care parameters having patient self-care values S_M(t_i) at time t_i and optimal self-care parameters having optimal self-care values O_M(t_i) at time t_i. The differentials are multiplied by corresponding scaling factors K_M. The system includes an input device for entering the patient self-care values S_M(t_i). A memory stores the optimal control parameter values R(t_i) and R(t_j), the prior disease control parameter value X(t_i), the optimal self-care values O_M(t_i), and the scaling factors K_M. A processor in communication with the input device and memory calculates the future disease control parameter value X(t_j). A display is connected to the processor to display the future disease control parameter value X(t_j) to a patient.

Claims

1. A system for simulating a disease control parameter such that a future disease control parameter value X(t_j) at time t_j is determined from a prior disease control parameter value X(t_i) at time t_i based on an optimal control parameter value R(t_j) at time t_j, the difference between said prior disease control parameter value X(t_i) and an optimal control parameter value R(t_i) at time t_i, and a set of differentials between patient self-care parameters having patient self-care values S_M(t_i) at time t_i and optimal self-care parameters having optimal self-care values O_M(t_i) at time t_i, said differentials being multiplied by corresponding scaling factors K_M, said system comprising:

a) an input means for entering said patient self-care values S_M(t_i);

b) a memory means for storing said optimal control parameter values R(t_i) and R(t_j), said prior disease control parameter value X(t_i), said optimal self-care values O_M(t_i), and said scaling factors K_M;

c) a processor means in communication with said input means and said memory means for calculating said future disease control parameter value X(t_j); and

d) a display means for displaying said future disease control parameter value X(t_j).

2. The system of

claim 1

, wherein said processor means calculates said future disease control parameter value X(t_j) according to the equation:

3. The system of

claim 1

, further comprising a recording means in communication with said processor means for recording an actual control parameter value A(t_i) at time t_i, an actual control parameter value A(t_j) at time t_j, and actual self-care parameters having actual self-care values C_M(t_i) at time t_i, and wherein said processor means further comprises means for adjusting said scaling factors K_M based on the difference between said actual control parameter value A(t_j) and said optimal control parameter value R(t_j), the difference between said actual control parameter value A(t_i) and said optimal control parameter value R(t_i), and the difference between said actual self-care values C_M(t_i) and said optimal self-care values O_M(t_i).

4. The system of

claim 3

, wherein said recording means comprises a measuring means for producing measurements of said actual control parameter values A(t_i) and A(t_j).

5. The system of

claim 1

, wherein said input means further comprises means for entering a value of a physiological parameter of a patient and said processor means further comprises means for determining at least one of said scaling factors from said value of said physiological parameter.

6. The system of

claim 5

, wherein said physiological parameter is selected from the group consisting of a body mass, an insulin sensitivity, a metabolism rate, and a fitness level.

7. The system of

claim 1

, wherein said disease control parameter comprises a blood glucose level.

8. The system of

claim 7

, wherein at least one of said patient self-care parameters is selected from the group consisting of a food exchange, an insulin dose, and an exercise duration.

9. The system of

claim 1

, wherein said input means, said memory means, and said processor means are operated on a stand-alone computing device.

10. The system of

claim 1

, wherein said input means, said memory means, and said processor means are operated on a plurality of computers communicating over a network.

11. The system of

claim 1

, wherein said input means, said memory means, and said processor means comprise a patient multi-media processor and a healthcare provider computer communicating with said patient multi-media processor via a smart card.

12. A system for predicting an effect of patient self-care actions on a disease control parameter such that a future disease control parameter value X(t_j) at time t_j is determined from an actual disease control parameter value A(t_i) at time t_i based on an optimal control parameter value R(t_j) at time t_j, the difference between said actual disease control parameter value A(t_i) and an optimal control parameter value R(t_i) at time t_i, and a set of differentials between patient self-care parameters having patient self-care values S_M(t_i) at time t_i and optimal self-care parameters having optimal self-care values O_M(t_i) at time t_i, said differentials being multiplied by corresponding scaling factors K_M, said system comprising:

a) an input means for entering said actual disease control parameter value A(t_i) and said patient self-care values S_M(t_i);

b) a memory means for storing said optimal control parameter values R(t_i) and R(t_j), said optimal self-care values O_M(t_i), and said scaling factors K_M;

c) a processor means in communication with said input means and said memory means for calculating said future disease control parameter value X(t_j); and

d) a display means for displaying said future disease control parameter value X(t_j).

13. The system of

claim 12

, wherein said processor means calculates said future disease control parameter value X(t_j) according to the equation:

14. The system of

claim 12

, wherein said input means further comprises means for entering an actual control parameter value A(t_j) at time t_j and actual self-care parameters having actual self-care values C_M(t_i) at time t_i, and wherein said processor means further comprises means for adjusting said scaling factors K_M based on the difference between said actual control parameter value A(t_j) and said optimal control parameter value R(t_j), the difference between said actual control parameter value A(t_i) and said optimal control parameter value R(t_i), and the difference between said actual self-care values C_M(t_i) and said optimal self-care values O_M(t_i).

15. The system of

claim 14

, further comprising a measuring means connected to said input means for producing measurements of said actual control parameter values A(t_i) and A(t_j).

16. The system of

claim 12

, wherein said input means further comprises means for entering a value of a physiological parameter of a patient and said processor means further comprises means for determining at least one of said scaling factors from said value of said physiological parameter.

17. The system of

claim 16

, wherein said physiological parameter is selected from the group consisting of a body mass, an insulin sensitivity, a metabolism rate, and a fitness level.

18. The system of

claim 12

, wherein said disease control parameter comprises a blood glucose level.

19. The system of

claim 18

, wherein at least one of said patient self-care parameters is selected from the group consisting of a food exchange, an insulin dose, and an exercise duration.

20. The system of

claim 12

, wherein said input means, said memory means, and said processor means are operated on a stand-alone computing device.

21. The system of

claim 12

, wherein said input means, said memory means, and said processor means are operated on a plurality of computers communicating over a network.

22. The system of

claim 12

, wherein said input means, said memory means, and said processor means comprise a patient multi-media processor and a healthcare provider computer communicating with said patient multi-media processor via a smart card.

23. A method for simulating a disease control parameter in a simulation system such that a future disease control parameter value X(t_j) at time t_j is determined from a prior disease control parameter value X(t_i) at time t_i based on an optimal control parameter value R(t_j) at time t_j, the difference between said prior disease control parameter value X(t_i) and an optimal control parameter value R(t_i) at time t_i, and a set of differentials between patient self-care parameters having patient self-care values S_M(t_i) at time t_i and optimal self-care parameters having optimal self-care values O_M(t_i) at time t_i, said differentials being multiplied by corresponding scaling factors K_M, said simulation system comprising a memory, a processor connected to said memory, a display connected to said processor, and an input means for entering in said processor said patient self-care values S_M(t_i), said method comprising the following steps:

a) storing in said memory said optimal control parameter values R(t_i) and R(t_j), said prior disease control parameter value X(t_i), said optimal self-care values O_M(t_i), and said scaling factors K_M;

b) entering in said processor said patient self-care values S_M(t_i);

c) calculating in said processor said future disease control parameter value X(t_j); and

d) displaying said future disease control parameter value X(t_j) on said display.

24. The method of

claim 23

, wherein said future disease control parameter value X(t_j) is calculated according to the equation:

25. The method of

claim 23

, further comprising the steps of recording an actual control parameter value A(t_i) at time t_i, an actual control parameter value A(t_j) at time t_j, and actual self-care parameters having actual self-care values C_M(t_i) at time t_i and adjusting said scaling factors K_M based on the difference between said actual control parameter value A(t_j) and said optimal control parameter value R(t_j), the difference between said actual control parameter value A(t_i) and said optimal control parameter value R(t_i), and the difference between said actual self-care values C_M(t_i) and said optimal self-care values O_M(t_i).

26. The method of

claim 23

, further comprising the steps of determining a value of a physiological parameter of a patient and determining at least one of said scaling factors from said value of said physiological parameter.

27. The method of

claim 26

, wherein said physiological parameter is selected from the group consisting of a body mass, an insulin sensitivity, a metabolism rate, and a fitness level.

28. The method of

claim 23

, wherein said disease control parameter comprises a blood glucose level.

29. The method of

claim 28

, wherein at least one of said patient self-care parameters is selected from the group consisting of a food exchange, an insulin dose, and an exercise duration.

30. A method for predicting in a simulation system an effect of patient self-care actions on a disease control parameter such that a future disease control parameter value X(t_j) at time t_j is determined from an actual disease control parameter value A(t_i) at time t_i based on an optimal control parameter value R(t_j) at time t_j, the difference between said actual disease control parameter value A(t_i) and an optimal control parameter value R(t_i) at time t_i, and a set of differentials between patient self-care parameters having patient self-care values S_M(t_i) at time t_i and optimal self-care parameters having optimal self-care values O_M(t_i) at time t_i, said differentials being multiplied by corresponding scaling factors K_M, said simulation system comprising a memory, a processor connected to said memory, a display connected to said processor, and an input means for entering in said processor said actual disease control parameter value A(t_i) and said patient self-care values S_M(t_i), said method comprising the following steps:

a) storing in said memory said optimal control parameter values R(t_i) and R(t_j), said optimal self-care values O_M(t_i), and said scaling factors K_M;

b) entering in said processor said actual disease control parameter value A(t_i) and said patient self-care values S_M(t_i) ;

c) calculating in said processor said future disease control parameter value X(t_j); and

d) displaying said future disease control parameter value X(t_j) on said display.

31. The method of

claim 30

, wherein said future disease control parameter value X(t_j) is calculated according to the equation:

32. The method of

claim 30

, further comprising the steps of entering in said processor an actual control parameter value A(t_j) at time t_j and actual self-care parameters having actual self-care values C_M(t_i) at time t_i and adjusting said scaling factors K_M based on the difference between said actual control parameter value A(t_j) and said optimal control parameter value R(t_j), the difference between said actual control parameter value A(t_i) and said optimal control parameter value R(t_i), and the difference between said actual self-care values C_M(t_i) and said optimal self-care values O_M(t_i).

33. The method of

claim 30

, further comprising the steps of determining a value of a physiological parameter of a patient and determining at least one of said scaling factors from said value of said physiological parameter.

34. The method of

claim 33

, wherein said physiological parameter is selected from the group consisting of a body mass, an insulin sensitivity, a metabolism rate, and a fitness level.

35. The method of

claim 30

, wherein said disease control parameter comprises a blood glucose level.

36. The method of

claim 35

, wherein at least one of said patient self-care parameters is selected from the group consisting of a food exchange, an insulin dose, and an exercise duration.