System and method for optimizing and routing health information

• US 9,703,927 B2
• Filed: 01/23/2014
• Issued: 07/11/2017
• Est. Priority Date: 09/01/2010
• Status: Active Grant

• Current Assignee: Apixio LLC
• Original Assignee: Apixio, Inc. (Centene Corporation)
• Inventors: Sethumadhavan, Vishnuvyas, Campana, Mary Ellen, Chaudhri, Imran N., Dastmalchi, Shahram Shawn, Rogers, Robert Derward
• Primary Examiner(s): ZAND, DAVOUD AMAN
• Application Number:
  US14/162,695
• Publication Number:
  US 20140200916A1
• Time in Patent Office: 1,265 Days
• Field of Search: 726 21, 707694, 707711, 707713, 707726, 707728, 707730, 707731, 707748, 707749, 707750, 707758, 707780, 709217, 709218, 709219
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 70/60 relating to pathologies

Abstract

A method is disclosed to receive health information request (HIR), including health information request query (HIRQ) and health information request data (HIRD), and to metatag the received HIR. The metatagged HIR is reconciled based on a semantic concept and HIRS is returned.

Claims

1. In a medical information navigation engine (MINE), a computerized method of transacting health information comprising:

receiving a health information request (HIR) from a user;

retrieving and meta-tagging health information in response to the HIR using a medical information navigation engine (MINE) wherein meta-tagging the health information includes;

identifying and tagging medical concepts associated with the meta-tagged health information, wherein the tagging medical concepts includes identifying associations between HIR data and medical concepts, wherein the medical concepts are standardized codes and text data medically related to a search term;

appending the identified medical concepts to the health information of the HIR;

identifying all additional medical concepts associated with the health information of the HIR as new information is added to the health information of the HIR to generate multiple levels of associated medical concepts;

determining associations between medical concepts by assembling every element of the health information into pairs with all other elements of the health information, and assigning a degree of association measured between each element of the pair;

annotating associations between multiple levels of associated medical concepts in a hierarchical structure;

processing the HIR to determine whether each of the medical concepts in the health information of the HIR is positive or negated based upon reference;

wherein the medical concepts include medications, allergies, problems and diagnoses, procedures, immunizations, measurements, observations and symptoms;

creating multi-dimensional relationships between the medical concepts using the MINE, wherein the multi-dimensional relationships indicate relevancy of apixions along dimensions of the multi-dimensional relationships; and

wherein the meta-tagged health information is reconciled by mapping based on at least one semantic concept; and

providing relevant health information based on the multi-dimensional relationships to the user.

2. The computerized method of claim 1 wherein the reconciliation includes at least one lexical search, and wherein at least one textual patient record is searched for occurrence of search term, its variants and synonyms, and the reconciliation further includes at least one concept search wherein data that is medically related to the search term is retrieved.

3. The computerized method of claim 1 further comprising consolidating and de-duplicating data, thereby removing redundant medical information.

4. The computerized method of claim 1 wherein the reconciliation includes using a set of explicit rules for fuzzy matching of the data items or referencing default rules.

5. The computerized method of claim 1 wherein the reconciliation includes recursively organizing data items into groups based on fuzzy matching of the semantic concepts represented by the data and further organizes the items within each group according to properties defined in the query.

6. The computerized method of claim 1 further comprising augmenting, disambiguating and filtering of health information, and wherein the health information includes documentation such as structured data and annotated textual data.

7. The computerized method of claim 6 further comprising translating the health information, and wherein the translating, augmenting and filtering of the health information includes support for wiki-base crown-source quality control of information thereby enabling the health information to be more actionable by increasing revenue, quality of care and patient safety, and reducing cost.

8. The computerized method of claim 1 further comprising transforming of the health information, and wherein the transformation includes converting concepts from one coding system.

9. A medical information navigation engine (MINE) configured to transact health information, the MINE comprising:

a processor completing the steps of;

meta-tag health information in response to a health information request (HIR) from a user, wherein the meta-tagging of the health information includes identifying and tagging medical concepts associated with the meta-tagged health information, wherein the tagging medical concepts includes identifying associations between HIR data and medical concepts, wherein the medical concepts are standardized codes and text data medically related to a search term;

append the identified medical concepts to the health information of the HIR;

identify all additional medical concepts associated with the health information of the HIR as new information is added to the health information of the HIR to generate multiple levels of associated medical concepts;

determine associations between medical concepts by assembling every element of the health information into pairs with all other elements of the health information, and assigning a degree of association measured between each element of the pair;

annotate associations between multiple levels of associated medical concepts in a hierarchical structure; and

process the HIR to determine whether each of the medical concepts in the health information of the HIR is positive or negated based upon reference, wherein the medical concepts include medications, allergies, problems and diagnoses, procedures, immunizations, measurements, observations and symptoms;

a mapper configured to create multi-dimensional relationships between the medical concepts, wherein the multi-dimensional relationships indicate relevancy of apixions along dimensions of the multi-dimensional relationships, and wherein the meta-tagged health information is reconciled by mapping based on at least one semantic concept; and

a visualizer configured to provide relevant health information based on the multi-dimensional relationships to the user.

10. The MINE of claim 9 wherein the reconciliation includes at least one lexical search, and wherein at least one textual patient record is searched for occurrence of search term, its variants and synonyms, and the reconciliation further includes at least one concept search wherein data that is medically related to the search term is retrieved.

11. The MINE of claim 9 wherein the processor further consolidates and de-duplicates data, thereby removing redundant medical information.

12. The MINE of claim 9 wherein the reconciliation includes using a set of explicit rules for fuzzy matching of the data items or referencing default rules.

13. The MINE of claim 9 wherein the reconciliation includes recursively organizing data items into groups based on fuzzy matching of the semantic concepts represented by the data and further organizes the items within each group according to properties defined in the query.

14. The MINE of claim 9 wherein the processor further augments, disambiguates and filters health information, and wherein the health information includes documentation such as structured data and annotated textual data.

15. The MINE of claim 14 wherein the processor further translates the health information, and wherein the translation, augmentation and filtration of the health information includes support for wiki-base crown-source quality control of information thereby enabling the health information to be more actionable by increasing revenue, quality of care and patient safety, and reducing cost.

16. The MINE of claim 9 wherein the processor further transforms the health information, and wherein the transformation includes converting concepts from one coding system.