Secure health information connectivity system

• US 20050273367A1
• Filed: 05/12/2005
• Published: 12/08/2005
• Est. Priority Date: 05/14/2004
• Status: Abandoned Application

• Current Assignee: Accelere, Inc.
• Original Assignee: Accelere, Inc.
• Inventors: Chen, David A., Nourie, Michael, Hudes, Matthew K., Benjamin, Roy
• Application Number:
  US11/129,652
• Publication Number:
  US 20050273367A1
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

A method is disclosed. The method includes receiving site specific patient information including a plurality of site specific data sets for a plurality of patients from different sites at a server computer, where the site specific patient information was previously converted from a first data format to a second data format. The site specific patient information including the site specific data sets is converted into standard patient information including standard data sets at the server computer.

Claims

1. A method comprising:

receiving site specific patient information including a plurality of site specific data sets for a plurality of patients from different sites at a server computer, wherein the site specific patient information was previously converted from a first data format to a second data format; and

converting the site specific patient information including the site specific data sets into standard patient information including standard data sets at the server computer.

2. The method of claim 1 further comprising:

comparing the standard data sets to a scenario to select a subset of data sets.

3. The method of claim 1 further comprising converting the standard patient information including the standard data sets into study specific information including study specific data sets.

4. The method of claim 1 wherein the first format is an HL7 format and the second format is a markup language format.

5. The method of claim 1 further comprising:

performing steps in a workflow process after converting the site specific patient information.

6. A system comprising:

a data connect element adapted to convert site specific patient information for a plurality of patients from a first format to a second format; and

a server computer adapted to convert the site specific patient information to standard patient information.

7. The system of claim 6 further comprising the one or more client computers operatively coupled to the server computer.

8. The system of claim 6 further comprising a plurality of data connect elements, wherein each data connect element is adapted to convert site specific patient information from the first format to the second format, and wherein the data connect elements are at different healthcare sites.

9. The system of claim 6 wherein the server computer comprises a workflow engine, wherein the workflow engine is adapted to execute a process to obtain consent to participate in a clinical or observational study from one or more patients selected from the plurality of patients.

10. The system of claim 6 wherein the server computer comprises code for assisted data entry of screened patient information.

11. The system of claim 6 wherein the server computer further comprises a normalizing engine that is adapted to normalize the site specific patient information.

12. A method of selecting a candidate for a study, comprising:

receiving a plurality of patient data sets at a data store located within an internal network, wherein each data set comprises at least one site specific term and an associated site specific patient value;

processing each of the received data sets as needed to transform the data sets into private data sets, and further, wherein the processed and private data sets are compatible with transport over an external network;

communicating the processed data sets over the external network to an external data store;

processing the communicated data sets as needed to transform each of the data sets into a standard data set including a canonical term and an associated canonical value;

comparing the transformed communicated data sets to a study scenario comprising a plurality of study parameters including the canonical data term and at least the associated canonical data value; and

processing the compared data sets to select a candidate data set for a study.

13. The method of claim 12 further comprising:

providing the selected candidate data set to a study coordinator.

14. The method of claim 12 further comprising associating the selected candidate data set with one or more individuals.

15. The method of claim 14 further comprising:

obtaining agreement of the associated individual to participate in the study.

16. The method of claim 12 further comprising:

charging a transaction fee for the selected candidate data set.

17. The method of claim 12 wherein, the plurality of candidate data sets further comprises a plurality of data sets from a single individual.

18. The method of claim 12 further comprising converting the candidate data set to a study specific data set.

19. The method of claim 12 wherein the processing of each of the received data sets as needed to transform the data sets into private data sets, and further, wherein the processed data sets are compatible with transport over an external network, further comprises:

assigning a client ID to the private data sets;

encrypting the private data sets; and

converting a format of the received data sets into a markup language transportable over the external network.

20. The method of claim 12 wherein the processing the communicated data sets as needed to transform each of the data sets into a standard data set including a canonical term and an associated canonical value comprises:

mapping the data sets to a list of a plurality of canonical data sets.