Method And Apparatus For Adaptive Prefetching Of Medical Data

• US 20140297316A1
• Filed: 03/27/2013
• Published: 10/02/2014
• Est. Priority Date: 03/27/2013
• Status: Abandoned Application

• Current Assignee: Change Healthcare Holdings, LLC (UnitedHealth Group Incorporated)
• Original Assignee: Mckesson Financial Holdings Limited (McKesson Corp.)
• Inventors: Noordvyk, Allan
• Application Number:
  US13/851,519
• Publication Number:
  US 20140297316A1
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• 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: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A method, apparatus, and computer program product are described herein for providing clinically adaptive prefetch of datasets relating to prior medical studies. Upon indication of a new study, a fit function may depend on an exemplar set of new study to prior study relationships. The fit function may calculate an affinity value for a prior study, indicating the probability of relevancy to the new study. The fit function may consider structured data, and unstructured data, such as by natural language processing. Based on the affinity value, a dataset relating to the prior study may be flagged for prefetch, indicating a dataset should be prefetched from a lower tier memory to a higher tier memory, allowing for faster access to the dataset, from a clinical system. The fit function may be trained based on usage of the prior study datasets, including accounting for false positives and false negatives.

Claims

1. A method comprising:

receiving an indication of a request for a new study for a patient;

generating, with a processor, a partial clinical information lattice (PCIL) representative of the new medical study;

receiving metadata describing a dataset related to a prior study for the patient;

generating a complete clinical information lattice (CIL) representative of the dataset;

calculating an affinity value for the prior study based on a fit function comparing the PCIL and the CIL, the affinity value indicating a probability of relevancy of the prior study to the new study; and

identifying whether or not the dataset should be prefetched from lower tier memory.

2. The method of claim 1, wherein the PCIL comprises at least one of patient history, scan request, medical alert, pregnancy status, patient class, procedure type, body region, device modality, technologist notes, nursing notes, current patient location or requesting service.

3. A method of claim 1, wherein the CIL extends data elements of a PCIL to include at least one of a diagnostic report, location of study, make of modality device, model of modality device, lab results or quantitative measurements.

4. The method of claim 1, wherein at least one of the PCIL or CIL comprises at least unstructured text, and the fit function utilizes natural language processing to normalize terminology and analyze semantic relationships.

5. The method of claim 1, wherein identifying whether or not the dataset should be prefetched from lower tier memory is based on a comparison of the affinity value to a threshold affinity value.

6. The method of claim 5, further comprising:

analyzing a quantity of datasets identified for prefetch and a memory allocation; and

adjusting the threshold affinity value based on the analysis.

7. The method of claim 1, further comprising:

utilizing actual aggregate end user behavior to identify a false negative dataset, wherein the false negative dataset is a dataset not identified for prefetch, but is requested for retrieval; and

training the fit function based on the false negative and an associated CIL.

8. The method of claim 1, further comprising:

utilizing actual aggregate end user behavior to identify a false positive dataset, wherein the false positive dataset is a dataset erroneously identified for prefetch that is not utilized in the new study; and

training the fit function based on the false positive dataset and an associated CIL.

9. The method of claim 1, further comprising:

generating a notification indicating optimization of the fit function is limited by a capacity of a high level storage medium.

10. The method of claim 1, further comprising:

initializing the fit function based on an exemplar set of commonly accepted new study to relevant prior study relationships.

11. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the processor, cause the device to at least:

receive an indication of a request for a new study for a patient;

generate a partial clinical information lattice (PCIL) representative of the new medical study;

receive metadata describing a dataset related to a prior study for the patient;

generate a complete clinical information lattice (CIL) representative of the dataset;

calculate an affinity value for the prior study based on a fit function comparing the PCIL and the CIL, the affinity value indicating a probability of relevancy of the prior study to the new study; and

identify whether or not the dataset should be prefetched from lower tier memory.

12. The apparatus according to claim 11, wherein the PCIL comprises at least one of patient history, scan request, medical alert, pregnancy status, patient class, procedure type, body region, device modality, technologist notes, nursing notes, current patient location or requesting service.

13. The apparatus according to claim 11, wherein the CIL extends data elements of a PCIL to include at least one of a diagnostic report, location of study, make of modality device, model of modality device, lab results or quantitative measurements.

14. The apparatus according to claim 11, wherein at least one of the PCIL or CIL comprises at least unstructured text, and the fit function utilizes natural language processing to normalize terminology and analyze semantic relationships.

15. The apparatus according to claim 11, wherein identifying whether or not the dataset should be prefetched from lower tier memory is based on a comparison of the affinity value to a threshold affinity value.

16. The apparatus according to claim 15, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the device to at least:

analyze a quantity of datasets identified for prefetch and a memory allocation; and

adjust the threshold affinity value based on the analysis.

17. An apparatus according to claim 11, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the device to at least:

utilize actual aggregate end user behavior to identify a false negative dataset, wherein the false negative dataset is a dataset not identified for prefetch, but is requested for retrieval; and

train the fit function based on the false negative and an associated CIL.

18. An apparatus according to claim 11, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the device to at least:

utilize actual aggregate end user behavior to identify a false positive dataset, wherein the false positive dataset is a dataset erroneously identified for prefetch that is not utilized in the new study; and

train the fit function based on the false positive dataset and an associated CIL.

19. An apparatus according to claim 11, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the device to at least:

generate a notification indicating optimization of the fit function is limited by a capacity of a high level storage medium.

20. An apparatus according to claim 11, wherein the at least one memory and the computer program code are further configured to, with the processor, cause the device to at least:

initialize the fit function based on an exemplar set of commonly accepted new study to relevant prior study relationships.

21. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions to:

authenticate a user affiliated with a clinic;

receive clinical content associated with the clinic, provided by the user;

store the clinical content in association with the clinic such that the clinical content is retrievable based on an indication of the clinic;

receive an indication of a request to view content associated with the clinic; and

cause display of the clinical content in response to the indication of the request.

22. The computer program product of claim 21, wherein the PCIL comprises at least one of patient history, scan request, medical alert, pregnancy status, patient class, procedure type, body region, device modality, technologist notes, nursing notes, current patient location or requesting service.

23. The computer program product of claim 21, wherein the CIL extends data elements of a PCIL to include at least one of a diagnostic report, location of study, make of modality device, model of modality device, lab results or quantitative measurements.

24. The computer program product of claim 21, wherein at least one of the PCIL or CIL comprises at least unstructured text, and the fit function utilizes natural language processing to normalize terminology and analyze semantic relationships.

25. The computer program product of claim 21, wherein identifying whether or not the dataset should be prefetched from lower tier memory is based on a comparison of the affinity value to a threshold affinity value.

26. The computer program product of claim 25, wherein the computer-executable program code instructions further comprise program code instructions to:

analyze a quantity of datasets identified for prefetch and a memory allocation; and

adjust the threshold affinity value based on the analysis.

27. The computer program product of claim 21, wherein the computer-executable program code instructions further comprise program code instructions to:

utilize actual aggregate end user behavior to identify a false negative dataset, wherein the false negative dataset is a dataset not identified for prefetch, but is requested for retrieval; and

train the fit function based on the false negative and an associated CIL.

28. The computer program product of claim 21, wherein the computer-executable program code instructions further comprise program code instructions to:

utilize actual aggregate end user behavior to identify a false positive dataset, wherein the false positive dataset is a dataset erroneously identified for prefetch that is not utilized in the new study; and

train the fit function based on the false positive dataset and an associated CIL.

29. The computer program product of claim 21, wherein the computer-executable program code instructions further comprise program code instructions to:

generate a notification indicating optimization of the fit function is limited by a capacity of a high level storage medium.

30. The computer program product of claim 21, wherein the computer-executable program code instructions further comprise program code instructions to:

initialize the fit function based on an exemplar set of commonly accepted new study to relevant prior study relationships.

31. A system comprising:

(a) a first device configured to;

provide an indication of a request for a new study for a patient;

transmit the indication to a second device; and

receive, from the second device, an indication of at least one dataset to be prefetched from lower tier memory;

(b) a second device configured to;

receive, from the first device, the indication of a request for a new study for a patient;

generate a partial clinical information lattice (PCIL) representative of the new medical study;

receive metadata describing a dataset related to a prior study for the patient;

generate a complete clinical information lattice (CIL) representative of the dataset;

calculate an affinity value for the prior study based on a fit function comparing the PCIL and the CIL, the affinity value indicating a probability of relevancy of the prior study to the new study;

identify whether or not the dataset should be prefetched from lower tier memory; and

transmit, to the first device, an indication of at least one dataset to be prefetched from lower tier memory.