COMPUTER-IMPLEMENTED SYSTEM AND METHOD FOR ASSOCIATING PRESCRIPTION DATA AND DE-DUPLICATION

• US 20120185264A1
• Filed: 01/18/2011
• Published: 07/19/2012
• Est. Priority Date: 01/18/2011
• Status: Abandoned Application

• Current Assignee: Source Healthcare Analytics LLC (ICON PLC)
• Original Assignee: Source Healthcare Analytics LLC (ICON PLC)
• Inventors: DEMOGENES, PETER, LITTLE, JEFFREY, HAYES, KARIN CHUN, SHERIDAN, KEITH, WEIDMAN, PAULETTE
• Application Number:
  US13/008,102
• Publication Number:
  US 20120185264A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients

Abstract

A prescription association system and method for grouping together prescription-related transactions and creating groups or “clusters” of prescriptions having similar characteristics. Through an association process, the prescription cluster describes the events surrounding prescription activity. This includes prescribing patterns, payer influences, and patient acceptance of therapy. The same prescription transaction from one data provider may contain additional or different information that can enhance a corresponding duplicate transaction or set of claim lifecycle transactions from another provider. The disclosed processes create unique linking across claims, payers, and patients and form the basis for relating and measuring payer, patient, practitioner, and pharmaceutical promotion influences on healthcare utilization and treatment.

Claims

1. A processor-implemented method for associating prescription-related data, comprising the steps of:

providing at least one processor configured to;

receive batch transaction data comprising a plurality of predetermined classifications;

process the classifications in order to obtain attributes for the classifications;

process the attributes to determine matches among the attributes for a respective classification for a predetermined period of time; and

group into one or more clusters the matches for each attribute during the predetermined period of time to identify events associated with the prescription-related data.

2. The method of claim 1, wherein the attributes comprise at least two of the following:

pharmacy identification numbers, data source code, prescription fill date, refill code, prescription number, drug identification number, and claim status code.

3. The method of claim 1, wherein the events comprise prescribing patterns, payer influences, and patient acceptance of therapy.

4. The method of claim 1, wherein the batch transaction data is received from an operational data store.

5. The method of claim 1, wherein the grouping of the matches in the computer system comprises the step of designating one of the plurality of attributes as an anchor to upon which the grouped matches will be based.

6. The method of claim 1, wherein processing the attributes in the computer system comprises the step of identifying first matching attributes comprising pharmacy identification, prescription number, refill code, and a prescription fill date within a specified range.

7. The method of claim 6, wherein processing the attributes comprises the step of identifying second matching attributes comprising pharmacy identification, prescription number, drug identification code, and a prescription fill date within a specified range.

8. The method of claim 7, wherein identified second matching attributes include no identified first matching attributes.

9. The method of claim 8, wherein processing the attributes comprises the step of identifying third matching attributes comprising pharmacy identification, prescription fill date, drug identification code, new or refill code, patient gender code, and patient age within a specified range.

10. The method of claim 9, wherein identified third matching attributes include no identified second matching attributes.

11. The method of claim 10, wherein processing the attributes comprises the step of identifying fourth matching attributes comprising pharmacy identification, prescription fill date, drug identification code, new or refill code, practitioner identification code, and days'"'"' supply.

12. The method of claim 11, wherein identified fourth matching attributes include no identified third matching attributes.

13. The method of claim 12, wherein processing the attributes comprises the step of identifying fifth matching attributes comprising pharmacy identification, prescription fill date, drug identification code, new or refill code, practitioner identification, patient gender code, and patient age within a specified range.

14. The method of claim 13, wherein identified fifth matching attributes contain no identified fourth matching attributes.

15. The method of claim 1, further comprising the step of processing the matches to identify and remove at least one duplicate in the batch transaction data.

16. A computer system for associating prescription-related data, comprising:

a memory;

a communication device operatively coupled to the memory to receive batch transaction data relating to the prescription-related data, said batch transaction data comprising a plurality of predetermined classifications; and

at least one processor, operatively coupled to the communications device for processing the classifications in order to obtain attributes for the classifications and processing the attributes to determine matches among the attributes for a respective classification for a predetermined period of time;

wherein the at least one processor groups the matches for each attribute during the predetermined period of time to identify events associated with the prescription-related data.

17. The computer system of claim 16, wherein the at least one processor is configured to process the classifications in order to obtain attributes comprising at least two of pharmacy identification number, data source code, prescription fill date, refill code, prescription number, drug identification number, and claim status code.

18. The computer system of claim 16, wherein the at least one processor is configured to identify events comprising prescribing patterns, payer influences and patient acceptance of therapy.

19. The computer system of claim 16, wherein the memory comprises an operational data store.

20. The computer system of claim 16, wherein the at least one processor is configured to group the matches by designating one of the plurality of attributes as an anchor upon which the grouped matches will be based.

21. The computer system of claim 16, wherein the at least one processor is configured to process the attributes by identifying first matching attributes comprising pharmacy identification, prescription number, refill code, and a prescription fill date within a specified range.

22. The computer system of claim 21, wherein the at least one processor is configured to process the attributes by identifying second matching attributes comprising pharmacy identification, prescription number, drug identification code, and a prescription fill date within a specified range.

23. The computer system of claim 22, wherein identified second matching attributes contain no identified first matching attributes.

24. The computer system of claim 23, wherein attributes are processed in the computer by identifying third matching attributes comprising pharmacy identification, prescription fill date, drug identification code, new or refill code, patient gender code, and patient age within a specified range.

25. The computer system of claim 24, wherein identified third matching attributes contain no identified second matching attributes.

26. The computer system of claim 25, wherein the attributes are processed by identifying fourth matching attributes comprising pharmacy identification, prescription fill date, drug identification code, new or refill code, practitioner identification code, and days'"'"' supply.

27. The computer system of claim 26, wherein identified fourth matching attributes contain no identified third matching attributes.

28. The computer system of claim 27, wherein the attributes are processed by identifying fifth matching attributes comprising pharmacy identification, prescription fill date, drug identification code, new or refill code, practitioner identification, patient gender code, and patient age within a specified range.

29. The computer system of claim 28, wherein identified fifth matching attributes contain no fourth matching attributes.

30. The computer system of claim 16, further comprising the step of processing the matches to identify and remove duplicates in the batch transaction data.

31. A computer network for associating prescription-related data, comprising:

at least one memory device;

at least one communication device operatively coupled to the at least one memory device to receive batch transaction data relating to the prescription-related data, said batch transaction data comprising a plurality of predetermined classifications; and

at least one processor, operatively coupled to the communications device for processing the classifications in order to obtain attributes for the classifications and processing the attributes to determine matches among the attributes for a respective classification for a predetermined period of time;

wherein the at least one processor (i) groups the matches for each attribute during the predetermined period of time to identify events associated with the prescription-related data, (ii) processes the attributes by identifying a first matching attributes comprising pharmacy identification, prescription number, refill code, and a prescription fill date within a specified range, and (iii) processes the attributes by identifying a second matching attribute comprising pharmacy identification, prescription number, drug identification code, and a prescription fill date within a specified range.