USING DATA IMPUTATION TO DETERMINE AND RANK OF RISKS OF HEALTH OUTCOMES

• US 20110105852A1
• Filed: 11/03/2009
• Published: 05/05/2011
• Est. Priority Date: 11/03/2009
• Status: Abandoned Application

• Current Assignee: Clinical Analysis Corporation
• Original Assignee: Clinical Analysis Corporation
• Inventors: LUCAS, DONALD, MORRIS, MACDONALD
• Application Number:
  US12/611,785
• Publication Number:
  US 20110105852A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Techniques for generating prediction of risks of medical outcomes and benefit scores for medical interventions, with imputation of missing patient data values, are disclosed. Apparatus or computer program products may be configured to receive a patient record for the patient from a database of a data storage unit, wherein one or more demographic data values or biometric data values in the patient record are missing or have null values; create and store a plurality of clone patient records in the database; impute a plurality of different substitute demographic data values or biometric data values and substitute a different one of the plurality of substitute values into each one of the clone patient records; determine, create and store a first metric, based at least in part on the clone patient records, wherein the first metric comprises a current health related metric for the patient; determine, create and store one or more medical intervention metrics, each based at least in part on an associated medical intervention and the clone patient records, representing a predicted health related metric for the patient when the associated medical intervention is performed; transform the database by updating the patient record to include the first metric and the one or more medical intervention metrics.

Claims

1. A data processing apparatus, comprising:

one or more processors;

a data storage unit storing a database of patient information associated with a patient of a healthcare provider;

query execution logic and an imputation engine coupled to the one or more processors and the data storage unit, and configured to;

receive a patient record for the patient from the database of the data storage unit, wherein one or more demographic data values or biometric data values in the patient record are missing or have null values;

create and store a plurality of clone patient records in memory;

impute different substitute demographic data values or biometric data values and substitute a different one of the substitute values into each one of the clone patient records;

determine, create and store a first metric, based at least in part on the clone patient records, wherein the first metric comprises a current health related metric for the patient;

determine, create and store one or more medical intervention metrics, each based at least in part on an associated medical intervention and the clone patient records, representing a predicted health related metric for the patient when the associated medical intervention is performed;

transform the database by updating the patient record to include the first metric and the one or more medical intervention metrics.

2. The apparatus of claim 1, wherein the query execution logic is configured to determine each of the one or more medical intervention metrics as a risk of a specified medical outcome.

3. The apparatus of claim 2, wherein the risk is any of myocardial infarction, stroke, onset of diabetes mellitus, or onset of complications of diabetes mellitus.

4. The apparatus of claim 1, wherein the query execution logic is further configured to determine one or more benefit scores comprising weighted sums of likelihoods of different medical outcomes within a specified time period.

5. The apparatus of claim 4, wherein each of the benefit scores measures a difference in a first predicted quality of life score in the specified time period if the patient receives an associated intervention compared to a second predicted quality of life score in the same specified time period if the patient does not.

6. The apparatus of claim 1, wherein the query execution logic is further configured to determine each of the one or more medical intervention metrics by determining average risks of a specified medical outcome and standard deviation of the risks across all the clone patient records.

7. The apparatus of claim 1, wherein the query execution logic is further configured to transform each of the one or more medical intervention metrics according to one or more medical rules when an action specified by any of the medical intervention metrics is inconsistent with the medical rules.

8. The apparatus of claim 1, wherein one of the intervention metrics is a combination metric based on a combination of two or more of the intervention metrics other than the combination metric.

9. The apparatus of claim 8, wherein the combination metric is represented by a value other than a sum of the two or more intervention metrics on which the combination metric is based.

10. The apparatus of claim 1, wherein one of the one or more medical intervention metrics is a stop current medication metric, representing a predicted health related metric for the patient when the patient stops taking one or more particular medications.

11. The apparatus of claim 1, wherein the query execution logic is further configured to determine a healthy metric, representing a simulated health related metric for a person of preferred health having one or more predetermined characteristics that match characteristics of the patient.

20. The computer-readable medium of claim 8, wherein the combination metric is represented by a value other than a sum of the two or more intervention metrics on which the combination metric is based.

12. A computer-readable medium carrying one or more sequences of instructions, which instructions, when executed by one or more processors, cause the one or more processors to:

receive a patient record for the patient from a database, wherein one or more demographic data values or biometric data values in the patient record is missing or has a null value;

create and store a plurality of clone patient records in the database;

impute a plurality of different substitute demographic data values or biometric data values and substitute a different one of the plurality of substitute values into each one of the clone patient records;

determine, create and store a first metric, based at least in part on the clone patient records, wherein the first metric comprises a current health related metric for the patient;

determine, create and store one or more medical intervention metrics, each based at least in part on an associated medical intervention and the clone patient records, representing a predicted health related metric for the patient when the associated medical intervention is performed;

transform the database by updating the patient record to include the first metric and the one or more medical intervention metrics.

13. The computer-readable medium of claim 12, further comprising instructions which when executed cause the one or more processors to determine each of the one or more medical intervention metrics as a risk of a specified medical outcome.

14. The computer-readable medium of claim 13, wherein the risk is any of myocardial infarction, stroke, onset of diabetes mellitus, or onset of complications of diabetes mellitus.

15. The computer-readable medium of claim 12, further comprising instructions which when executed cause the one or more processors to determine one or more benefit scores comprising weighted sums of likelihoods of different medical outcomes within a specified time period.

16. The computer-readable medium of claim 15, wherein each of the benefit scores measures a difference in a first predicted quality of life score in the specified time period if the patient receives an associated intervention compared to a second predicted quality of life score in the same specified time period if the patient does not.

17. The computer-readable medium of claim 12, further comprising instructions which when executed cause the one or more processors to determine each of the one or more medical intervention metrics by determining average risks of a specified medical outcome and standard deviation of the risks across all the clone patient records.

18. The computer-readable medium of claim 12, further comprising instructions which when executed cause the one or more processors to transform each of the one or more medical intervention metrics according to one or more medical rules when an action specified by any of the medical rules is inconsistent with the medical intervention metrics.

19. The computer-readable medium of claim 12, wherein one of the intervention metrics is a combination metric based on a combination of two or more of the intervention metrics other than the combination metric.

21. The computer-readable medium of claim 12, wherein one of the one or more medical intervention metrics is a stop current medication metric, representing a predicted health related metric for the patient when the patient stops taking one or more particular medications.

22. The computer-readable medium of claim 12, further comprising instructions which when executed cause the one or more processors to determine a healthy metric, representing a simulated health related metric for a person of preferred health having one or more predetermined characteristics that match characteristics of the patient.

23. A data processing method, comprising:

receiving a patient record associated with a patient of a healthcare provider, wherein one or more demographic data values or biometric data values in the patient record are missing or have null values;

creating and storing a plurality of clone patient records;

imputing different substitute demographic data values or biometric data values and substituting a different one of the substitute values into each one of the clone patient records;

determining, creating and storing a first metric, based at least in part on the clone patient records, wherein the first metric comprises a current health related metric for the patient;

determining, creating and storing one or more medical intervention metrics, each based at least in part on an associated medical intervention and the clone patient records, representing a predicted health related metric for the patient when the associated medical intervention is performed;

generating and causing displaying, on a display device, the first metric and the one or more medical intervention metrics;

wherein the method is performed by one or more computing devices.

24. The method of claim 23, further comprising generating and causing displaying one or more recommendations of medical interventions for the patient based at least in part on the one or more medical intervention metrics.

25. The method of claim 24, wherein the recommendations are displayed in a list that is ranked according to estimated benefit.

26. The method of claim 23, wherein each of the one or more medical intervention metrics is determined as a risk of a specified medical outcome.

27. The method of claim 26, wherein the risk is any of myocardial infarction, stroke, onset of diabetes mellitus, or onset of complications of diabetes mellitus.

28. The method of claim 23, further comprising determining one or more benefit scores comprising weighted sums of likelihoods of different medical outcomes within a specified time period.

29. The method of claim 28, wherein each of the benefit scores measures a difference in a first predicted quality of life score in the specified time period if the patient receives an associated intervention compared to a second predicted quality of life score in the same specified time period if the patient does not.

30. The method of claim 23, further comprising determining each of the one or more medical intervention metrics by determining average risks of a specified medical outcome and standard deviation of the risks across all the clone patient records.

31. The method of claim 23, further comprising transforming each of the one or more medical intervention metrics according to one or more medical rules when an action specified by any of the medical intervention metrics is inconsistent with the medical rules.

32. The method of claim 23, wherein one of the intervention metrics is a combination metric based on a combination of two or more of the intervention metrics other than the combination metric.

33. The method of claim 23, wherein the combination metric is represented by a value other than a sum of the two or more intervention metrics on which the combination metric is based.

34. The method of claim 23, wherein one of the one or more medical intervention metrics is a stop current medication metric, representing a predicted health related metric for the patient when the patient stops taking one or more particular medications.

35. The method of claim 23, further comprising determining a healthy metric, representing a simulated health related metric for a person of preferred health having one or more predetermined characteristics that match characteristics of the patient.

36. A data processing method, comprising:

receiving patient data for a patient of a healthcare provider, wherein one or more values in the patient data are missing or are null;

creating and storing a plurality of clone patient records;

imputing different substitute demographic or biometric data values and substituting a different one of the substitute values into each one of the clone patient records;

determining risks of outcomes for each of the clone patient records with or without one or more medical interventions;

determining benefits associated with the risks of outcomes;

determining a confidence level associated with the benefits;

generating and causing displaying, on a display device, one or more medical intervention recommendations for the patient based at least in part on the one or more medical interventions, benefits, and confidence level;

wherein the method is performed by one or more computing devices.

37. The method of claim 36, wherein the medical intervention recommendations are displayed in a list that is ranked according to estimated benefit.

38. The method of claim 36, wherein the risk is any of myocardial infarction, stroke, onset of diabetes mellitus, or onset of complications of diabetes mellitus.

39. The method of claim 36, wherein each of the benefits comprise weighted sums of likelihoods of different medical outcomes within a specified time period.

40. The method of claim 39, wherein each of the benefits measures a difference in a first predicted quality of life score in the specified time period if the patient receives an associated intervention compared to a second predicted quality of life score in the same specified time period if the patient does not.

41. The method of claim 36, further comprising determining each of the one or more medical interventions by determining average risks of a specified medical outcome and standard deviation of the risks across all the clone patient records.

42. The method of claim 36, further comprising transforming each of the one or more medical interventions according to one or more medical rules when an action specified by any of the medical interventions is inconsistent with the medical rules.