SYSTEM AND METHOD OF AGGREGATING AND INTERPRETING DATA FROM CONNECTED DEVICES

• US 20170177798A1
• Filed: 12/15/2016
• Published: 06/22/2017
• Est. Priority Date: 12/18/2015
• Status: Active Grant

• Current Assignee: Aetna Incorporated (CVS Health Corporation)
• Original Assignee: Aetna Incorporated (CVS Health Corporation)
• Inventors: Samuel, Robert, Ganesan, Sriram
• Granted Patent: US 11,616,825 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G16H 10/00 ICT specially adapted for the handling or processing of patient-related medical or healthcare data for medical reports G16H15/00; for therapies or health-improving plans G16H20/00; for the handling or processing of medical images G16H30/00
• CPC Class Codes: G16H 10/65 stored on portable record carriers, e.g. on smartcards, RFID tags or CD
• CPC Class Codes: G16H 80/00 ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

Embodiments of the disclosure provide a method for aggregating and providing health data records to an electronic device. The method is performed by a server that includes a processor and a non-transitory computer readable medium with processor-executable instructions stored thereon. When the instructions are executed by the processor, the server performs the method including: (a) receiving collected data from one or more client devices, the collected data comprising health related data including at least one of step count data, heart rate data, sleep sensor data; (b) extracting metadata from the collected data; (c) pseudonymizing the collected data; (d) categorizing the collected data using the extracted metadata and enterprise ontology of the server; and (e) storing the collected data.

Claims

1. A method for aggregating and providing health data records to an electronic device, the method performed by a server comprising a processor and a non-transitory computer readable medium with processor-executable instructions stored thereon, such that when the instructions are executed by the processor, the server performs the method comprising:

receiving collected data from one or more client devices, the collected data comprising health related data including at least one of step count data, heart rate data, sleep sensor data;

extracting metadata from the collected data;

pseudonymizing the collected data;

categorizing the collected data using the extracted metadata and enterprise ontology of the server; and

storing the collected data.

2. The method according to claim 1, wherein the receiving collected data from the client devices comprises at least one of:

receiving collected data in real-time from the client devices;

receiving collected data in batch from an intermediary;

receiving collected data after a data threshold has been surpassed; and

receiving collected data after a certain amount of time has elapsed.

3. The method according to claim 1, further comprising:

separating member-identifiable data and medical data in the collected data, wherein member-identifiable data comprises protected health information and personal identifiable information of a member, and medical data comprises doctor'"'"'s notes, clinical notes, diagnostic results, vital sign readings, or radiology images;

4. The method according to claim 3, wherein the pseudonymizing the collected data comprises:

creating a pseudonym using a cryptographic algorithm;

encrypting the pseudonym;

creating a map function to access the member-identifiable data through the pseudonym;

associating the medical data with the pseudonym;

generating a code to link the member-identifiable data and the medical data; and

encrypting the code.

5. The method according to claim 3, wherein the storing the collected data comprises:

storing the map function, the pseudonym, and the member-identifiable data in a database;

storing the medical data and the pseudonym in the database; and

storing the encrypted code and the pseudonym in the database.

6. The method according to claim 1, wherein the pseudonymizing the collected data comprises:

creating a pseudonym using a cryptographic algorithm;

encrypting the pseudonym; and

creating a map function to access the collected data through the pseudonym.

7. The method according to claim 1, further comprising:

re-identifying the pseudonymized data for an authorized user to obtain a health data record.

8. The method according to claim 7, wherein the re-identifying comprises:

obtaining a pseudonym by decrypting a link code;

accessing actual member-specific data by mapping the pseudonym with member-identifiable data; and

merging member-identifiable data and medical data to obtain the health data record.

9. The method according to claim 7, further comprising:

de-identifying data in the health data record for a secondary party to obtain a health data record with de-identified data.

10. The method according to claim 9, wherein the de-identifying comprises:

stratifying direct member-identifiers, quasi-identifiers, and medical data;

obtaining health data record with de-identified data by determining a type of direct member-identifier, quasi-identifier, and medical data and applying a de-identification algorithm based on the type of direct member-identifier, quasi-identifier, and medical data, wherein the de-identification algorithm obscures values related to the member-identifier, quasi-identifier and/or medical data.

11. The method according to claim 10, wherein the de-identification algorithm is selected from the group consisting of:

suppression;

generalization;

perturbation;

longitudinal consistency;

partial redaction;

redaction; and

swapping.

12. A server for aggregating and providing health data records to an electronic device, the server comprising a processor and a non-transitory computer readable medium with processor-executable instructions stored thereon, such that when the instructions are executed by the processor, the server performs the method comprising:

receiving collected data from one or more client devices, the collected data comprising health related data including at least one of step count data, heart rate data, sleep sensor data;

extracting metadata from the collected data;

pseudonymizing the collected data;

categorizing the collected data using the extracted metadata and enterprise ontology of the server; and

storing the collected data.

13. The server according to claim 12, wherein the receiving collected data from the client devices comprises at least one of:

receiving collected data in real-time from the client devices;

receiving collected data in batch from an intermediary;

receiving collected data after a data threshold has been surpassed; and

receiving collected data after a certain amount of time has elapsed.

14. The server according to claim 12, further performing the method comprising:

separating member-identifiable data and medical data in the collected data, wherein member-identifiable data comprises protected health information and personal identifiable information of a member, and medical data comprises doctor'"'"'s notes, clinical notes, diagnostic results, vital sign readings, or radiology images;

15. The server according to claim 14, wherein the pseudonymizing the collected data comprises:

creating a pseudonym using a cryptographic algorithm;

encrypting the pseudonym;

creating a map function to access the member-identifiable data through the pseudonym;

associating the medical data with the pseudonym;

generating a code to link the member-identifiable data and the medical data; and

encrypting the code.

16. The server according to claim 14, wherein the storing the collected data comprises:

storing the map function, the pseudonym, and the member-identifiable data in a database;

storing the medical data and the pseudonym in the database; and

storing the encrypted code and the pseudonym in the database.

17. The server according to claim 12, wherein the pseudonymizing the collected data comprises:

creating a pseudonym using a cryptographic algorithm;

encrypting the pseudonym; and

creating a map function to access the collected data through the pseudonym.

18. The server according to claim 12, further performing the method comprising:

re-identifying the pseudonymized data for an authorized user to obtain a health data record by (a) obtaining a pseudonym by decrypting a link code, (b) accessing actual member-specific data by mapping the pseudonym with member-identifiable data, and (c) merging member-identifiable data and medical data to obtain the health data record.

19. The server according to claim 18, further performing the method comprising:

de-identifying data in the health data record for a secondary party to obtain a health data record with de-identified data by;

(a) stratifying direct member-identifiers, quasi-identifiers, and medical data, and (b) obtaining health data record with de-identified data by determining a type of direct member-identifier, quasi-identifier, and medical data and applying a de-identification algorithm based on the type of direct member-identifier, quasi-identifier, and medical data, wherein the de-identification algorithm obscures values related to the member-identifier, quasi-identifier and/or medical data.

20. A non-transitory computer-readable medium for aggregating and providing health data records to an electronic device, the non-transitory computer-readable medium storing processor-executable instructions for performing the method comprising:

receiving collected data from one or more client devices, the collected data comprising health related data including at least one of step count data, heart rate data, sleep sensor data;

extracting metadata from the collected data;

pseudonymizing the collected data;

categorizing the collected data using the extracted metadata and enterprise ontology of the server; and

storing the collected data.