SYSTEMS AND METHODS FOR GENERATING LONGITUDINAL DATA PROFILES FROM MULTIPLE DATA SOURCES

• US 20160292456A1
• Filed: 03/31/2016
• Published: 10/06/2016
• Est. Priority Date: 04/01/2015
• Status: Active Application

• Current Assignee: Abbvie Incorporated
• Original Assignee: Abbvie Incorporated
• Inventors: Garr, Sanjeev, Yassick, Jessica, Sword, Shannon, Padmanaban, Anandbabu, Campbell, Denise, Dubey, Pankaj
• Application Number:
  US15/087,438
• Publication Number:
  US 20160292456A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/24573 using data annotations, e.g. user-defined metadata
• CPC Class Codes: G06F 16/81 Indexing, e.g. XML tags; Data structures therefor; Storage structures
• CPC Class Codes: G06F 21/602 Providing cryptographic facilities or services
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 2220/10 Usage protection of distributed data files
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records

Abstract

A computer-implemented method for generating a longitudinal data profile from multiple disparate data sources is provided. The method includes storing, at a central data hub, first de-identified data received from a first data source, the first de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on a master list that includes a list of identifiers and corresponding anonymous IDs for each identifier. The method further includes storing second de-identified data received from a second data source, and storing third de-identified data received from a third data source. The method further includes processing the first, second, and third de-identified data to link the first, second, and third de-identified data using the anonymous ID, and generating the longitudinal data profile from the linked first, second, and third de-identified data.

Claims

1. A computer-implemented method for generating a longitudinal data profile from multiple disparate data sources, the method comprising:

storing, at a central data hub, first de-identified data received from a first data source, the first de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on a master list that includes a list of identifiers and corresponding anonymous IDs for each identifier;

storing, at the central data hub, second de-identified data received from a second data source, the second de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on the master list;

storing, at the central data hub, third de-identified data received from a third data source, the third de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the anonymous ID is assigned based on the master list;

processing, at the central data hub, the first, second, and third de-identified data to link the first, second, and third de-identified data using the anonymous ID; and

generating, at the central data hub, the longitudinal data profile from the linked first, second, and third de-identified data by organizing the first, second, and third de-identified data to generate a comprehensive profile that includes data from multiple disparate data sources.

2. The computer-implemented method of claim 1, further comprising:

receiving, at a data analyzer computing device communicatively coupled to the central data hub, one or more user inputs, wherein the one or more user inputs define a subset of longitudinal data profiles stored at the central data hub, and wherein the one or more user inputs identify an organizational scheme for the subset of longitudinal data profiles;

generating, at the central data hub, an extract based on the one or more user inputs, wherein the extract includes the subset of longitudinal data profiles with each longitudinal data profile in the subset organized according to the organizational scheme;

transmitting the extract from the central data hub to the data analyzer computing device; and

performing, at the data analyzer computing device, an analysis on the subset of longitudinal data profiles included in the extract.

3. A computer-implemented method for generating a longitudinal data profile from multiple disparate data sources, the method comprising:

storing, at a central data hub, first de-identified data received from a claims data source, the first de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the claims data source stores insurance claims data associated with medical or pharmaceutical insurance coverage for patients, wherein the insurance claims data is encrypted at the claims data source using an encryption algorithm, and wherein the anonymous ID is assigned based on a patient master list that includes a list of patient identifiers and corresponding anonymous IDs for each patient identifier;

storing, at the central data hub, second de-identified data received from a services data source, the second de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the services data source stores services data that indicates whether patients are enrolled in a patient support program, wherein the services data is encrypted using the encryption algorithm, and wherein the anonymous ID is assigned based on the patient master list;

storing, at the central data hub, third de-identified data received from a pharmacy data source, the third de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the pharmacy data source stores pharmacy data associated with a prescription product for patients, wherein the pharmacy data is encrypted using the encryption algorithm, and wherein the anonymous ID is assigned based on the patient master list;

processing, at the central data hub, the first, second, and third de-identified data to link the first, second, and third de-identified data using the anonymous ID; and

generating, at the central data hub, the longitudinal data profile for a patient from the linked first, second, and third de-identified data by organizing the first, second, and third de-identified data to generate a comprehensive history for the patient that includes data from multiple disparate healthcare data sources.

4. The computer-implemented method of claim 3, further comprising:

receiving, at a data analyzer computing device communicatively coupled to the central data hub, one or more user inputs, wherein the one or more user inputs define a subset of longitudinal data profiles stored at the central data hub, and wherein the one or more user inputs identify an organizational scheme for the subset of longitudinal data profiles;

generating, at the central data hub, an extract based on the one or more user inputs, wherein the extract includes the subset of longitudinal data profiles with each longitudinal data profile in the subset organized according to the organizational scheme;

transmitting the extract from the central data hub to the data analyzer computing device; and

performing, at the data analyzer computing device, an analysis on the subset of longitudinal data profiles included in the extract.

5. The computer-implemented method of claim 4, wherein performing an analysis comprises comparing a first plurality of longitudinal data profiles associated with patients who are enrolled in the patient support program against a second plurality of longitudinal data profiles associated with patients who are not enrolled in the patient support program to determine an efficacy of the patient support program.

6. The computer-implemented method of claim 5, wherein comparing the first plurality of longitudinal data profiles against the second plurality of longitudinal data profiles comprises comparing adherence data for the first and second plurality of longitudinal data profiles.

7. The computer-implemented method of claim 3, further comprising analyzing, using a data analyzer computing device communicatively coupled to the central data hub, the generated longitudinal data profile for at least one of health economics outcomes research and marketing analytics business intelligence research.

8. The computer-implemented method of claim 7, wherein analyzing the generated longitudinal data profile comprises generating an output that identifies a cost differential between a patient who participates in the patient support program and a patient who does not participate in the patent support program.

9. The computer-implemented method of claim 3, wherein the services data is encrypted using the encryption algorithm at a claims switch communicatively coupled to the services data source.

10. A central data hub for generating a longitudinal data profile from multiple disparate data sources, said central data hub configured to:

store first de-identified data received from a claims data source, the first de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the claims data source stores insurance claims data associated with medical or pharmaceutical insurance coverage for patients, wherein the insurance claims data is encrypted at the claims data source using an encryption algorithm, and wherein the anonymous ID is assigned based on a patient master list that includes a list of patient identifiers and corresponding anonymous IDs for each patient identifier;

store second de-identified data received from a services data source, the second de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the services data source stores services data that indicates whether patients are enrolled in a patient support program, wherein the services data is encrypted using the encryption algorithm, and wherein the anonymous ID is assigned based on the patient master list;

store third de-identified data received from a pharmacy data source, the third de-identified data including a plurality of data records having encrypted identifying data and an anonymous ID assigned to each record, wherein the pharmacy data source stores pharmacy data associated with a prescription product for patients, wherein the pharmacy data is encrypted using the encryption algorithm, and wherein the anonymous ID is assigned based on the patient master list;

process the first, second, and third de-identified data to link the first, second, and third de-identified data using the anonymous ID; and

generate the longitudinal data profile for a patient from the linked first, second, and third de-identified data by organizing the first, second, and third de-identified data to generate a comprehensive history for the patient that includes data from multiple disparate healthcare data sources.

11. The central data hub of claim 10, wherein said central data hub is further configured to:

receive, from a data analyzer computing device communicatively coupled to said central data hub, one or more user inputs, wherein the one or more user inputs define a subset of longitudinal data profiles stored at the central data hub, and wherein the one or more user inputs identify an organizational scheme for the subset of longitudinal data profiles; and

generate an extract based on the one or more user inputs, wherein the extract includes the subset of longitudinal data profiles with each longitudinal data profile in the subset organized according to the organizational scheme; and

transmit the extract to the data analyzer computing device.

12. The central data hub of claim 10, wherein the services data is encrypted using the encryption algorithm at a claims switch communicatively coupled to the services data source.

13. A computer-implemented method for directly providing a user with an estimated patient cost for a prescription product and a co-pay eligibility determination for the prescription product, the method comprising:

receiving, at a host computing device, from a remote user computing device, a user request for the estimated patient cost and the co-pay eligibility determination, wherein the user request includes patient identification information, and wherein the remote user computing device and the host computing device are linked together via a wide area network that includes the Internet;

retrieving, by the host computing device, from at least one database, benefits data and medication history data based on the patient identification information;

generating, at the host computing device, the estimated patient cost and the co-pay eligibility determination based on the patient identification information and the retrieved benefits data and medication history data; and

causing the estimated patient cost and the co-pay eligibility determination to be displayed on the remote user computing device.

14. The computer-implemented method of claim 13, wherein receiving a user request comprises receiving a user request from a prospective patient via a pre-check application running on the remote user computing device.

15. The computer-implemented method of claim 13, wherein receiving a user request comprises receiving a user request from a healthcare provider (HCP) via an HCP portal running on the remote user computing device.

16. The computer-implemented method of claim 13, further comprising:

retrieving electronic marketing data related to the prescription product;

analyzing, at the host computing device, the electronic marketing data to identify prospective patients;

storing the prospective patients in a prospective patient database;

comparing, at the host computing device, a patient database to the prospective patient database to identify patients; and

electronically evaluating, at the host computing device, the electronic marketing data based on the comparison.

17. The computer-implemented method of claim 13, wherein generating, at the host computing device, the estimated patient cost and the co-pay eligibility determination comprises generating the estimated patient cost based on a coverage status for the patient, an estimated cost for the prescription product, a deductible amount for the patient, an out of pocket limit for the patient, a minimum co-pay amount for the patient, a maximum co-pay amount for the patient, and a co-insurance amount for the patient.

18. The computer-implemented method of claim 13, wherein retrieving benefits data and medication history data comprises retrieving the benefits data and medication history data as structured XML data.

19. The computer-implemented method of claim 18, further comprising converting, at the computing device, the structured XML data into a PDF format.

20. The computer-implemented method of claim 19, wherein receiving a user request comprises receiving a user request that does not include prescription insurance information and prescription information.