Mobile supplementation, extraction, and analysis of health records

1. A method for providing a physician with clinical decision support information about a patient whose RNA has been sequenced with a next generation sequencing (NGS) system, comprising:

receiving, from a mobile device, an image of a medical document comprising NGS medical information of the patient;

extracting a first region from the image;

extracting NGS medical information of the patient from the first region into a structured dataset, the extracted NGS medical information including at least one RNA expression;

correlating a portion of the extracted NGS medical information that includes the at least one RNA expression with summarized medical information from a cohort of patients similar to the patient; and

generating, for display on the mobile device, a clinical decision support report comprising the summarized medical information.

2. The method of claim 1 , wherein extracting the first region from the image is based at least in part from a template of a predefined model matched to the image, wherein the template encodes one or more rules.

3. The method of claim 2 , wherein the one or more rules comprise applying one or more masks to identify the first region.

4. The method of claim 2 , wherein the one or more rules are directed to document extraction features and comprise: a first rule for regular expressions; a second rule for natural language processing; and a third rule for column-row pairing to text identified in the one or more regions.

5. The method of claim 2 , wherein the one or more rules are directed to document format features and comprise one or more rules for validating the extracted NGS medical information based at least in part from a second region extracted from the medical document, wherein the second region is distinct from the first region.

6. The method of claim 1 , wherein the summarized medical information comprises treatment incidence information.

7. The method of claim 1 , wherein the summarized medical information comprises treatment response information.

8. The method of claim 1 , wherein correlating a portion of the extracted NGS medical information with summarized medical information from a cohort of patients similar to the patient comprises:

selecting an RNA expression in the extracted NGS medical information;

querying a structured data repository of medical information for patients having the selected RNA expression;

assembling the cohort of patients similar to the patient from the results of the query.

9. The method of claim 1 , wherein the summarized medical information comprises clinical decision support information not found in the medical document.

10. The method of claim 7 , wherein the treatment response information comprises a treatment and a clinical response level of the cohort of patients prescribed the treatment.

11. The method of claim 1 , wherein the summarized medical information comprises a treatment name, the method further comprising:

providing the treatment name to an insurer of the patient; and

receiving a pre-authorization for the treatment from the insurer.

12. The method of claim 1 , wherein extracting NGS medical information from the first region further comprises:

identifying a first type of NGS medical information from a template;

identifying a first extraction rule for extracting the first type of NGS medical information from the template;

extracting the first type of NGS medical information from the first region based at least in part on the first extraction rule; and

classifying the extracted first type of NGS medical information.

13. The method of claim 12 , wherein extracting the NGS medical information based at least in part on the first extraction rule further comprises:

determining a first concept from the NGS medical information; and

identifying a match to the first concept in a first list of concepts.

14. The method of claim 13 , further comprising:

identifying a first classification from the template;

generating a comparison of a classification of the first concept to the first classification; and

storing the match to the first concept as the extracted first type of NGS medical information based on a positive generated comparison.

15. The method of claim 14 , wherein classifying the extracted first type of NGS medical information further comprises:

identifying an additional NGS medical information in the first region;

extracting the additional NGS medical information; and

generating structured data from the extracted first type of NGS medical information and the extracted additional NGS medical information.

16. The method of claim 14 , wherein identifying a first classification from the template further comprises selecting the first classification from an enumerated list of classifications within the template.

17. The method of claim 12 , wherein extracting the first type of NGS medical information from the first region further comprises:

extracting the first type of NGS medical information from a second region of the medical document;

validating the extracted first type of NGS medical information from the second region with a verified set of NGS medical information previously extracted from the second region; and

validating the extracted first type of NGS medical information from the first region with the validated extracted first type of NGS medical information from the second region.

18. The method of claim 12 , wherein extracting the first type of NGS medical information from the first region further comprises:

determining a first concept from the NGS medical information;

identifying a plurality of matches to the first concept in a first list of concepts;

identifying a first classification from the template;

generating, for each match of the plurality of matches, a comparison of a classification of the first concept to the first classification; and

storing a first match to the first concept, of the plurality of matches, as the extracted first type of NGS medical information based on a positive generated comparison.

19. The method of claim 12 , wherein extracting the first type of NGS medical information from the first region further comprises:

determining a first concept from the NGS medical information;

identifying a plurality of matches to the first concept in a first list of concepts;

identifying a first classification from the template;

generating, for each match of the plurality of matches, a classification of the match;

generating, for each match of the plurality of matches, an estimated confidence reliability rating of the match; and

storing the match with the highest estimated confidence, of the plurality of matches, as the extracted first type of NGS medical information.

20. The method of claim 12 , wherein extracting the NGS medical information of the patient from the first region further comprises:

determining a first concept from the NGS medical information;

identifying a match to the first concept in a first list of concepts, wherein the first list of concepts is not a preferred authority;

referencing the first concept to an entity in a database of related concepts; and

identifying a match to a second concept in a second list of concepts, the second list of concepts not directly linked to the first list of concepts except by a relationship between the entity and the first concept and the entity and the second concept, wherein the second list of concepts is a preferred authority.

21. The method of claim 20 , further comprising:

identifying a first classification from the template;

generating a comparison of a classification of the match to the second concept to the first classification; and

storing the match to the second concept as the extracted first type of NGS medical information based on a positive generated comparison.

22. The method of claim 20 , wherein generating structured data from the extracted NGS medical information further comprises:

identifying a structured format from the template, the structured format including a plurality of fields;

assigning one or more values associated with the second concept to one or more of the plurality of fields;

identifying one or more values from the first region;

assigning one or more of the identified one or more values to one or more others of the plurality of fields; and

storing the structured format having the assigned one or more values and the assigned one or more of the identified values as the structured data.

23. The method of claim 20 , further comprising:

retrieving a predetermined degree of specificity, the predetermined degree of specificity identifying selection criteria;

identifying a degree of specificity of the second concept;

evaluating the degree of specificity of the second concept with the selection criteria of the predetermined degree of specificity;

normalizing the second concept within the preferred authority to a third concept satisfying the selection criteria of the predetermined degree of specificity; and

storing the third concept as the extracted first type of NGS medical information.

24. The method of claim 23 , wherein generating structured data from the extracted NGS medical information further comprises:

identifying a structured format from the template, the structured format including a plurality of fields;

assigning one or more values associated with the third concept to one or more of the plurality of fields;

identifying one or more values from the first region;

assigning one or more of the identified values to one or more others of the plurality of fields; and

providing the structured format having the one or more assigned values and the assigned one or more of the identified values as the structured data.

25. The method of claim 20 , wherein the first concept is extracted from sentences of text using natural language processing.

26. The method of claim 20 , wherein the first concept comprises at least one of medications, treatments, or NGS medical information of the patient.

27. The method of claim 1 , wherein the summarized medical information is generated from a method comprising:

deriving a plurality of first concepts from a publication, the publication separate from the medical document;

normalizing each concept in the plurality of first concepts; and

generating a knowledge database comprising each of the normalized concepts.

28. The method of claim 27 , further comprising:

generating links in the knowledge database between the publication and each instance of its respective normalized concepts;

identifying one or more normalized concepts within the structured data matching one or more normalized concepts linked to the publication; and

providing the links in the knowledge database to the identified normalized concepts in the summarized medical information.

29. The method of claim 12 , wherein extracting the first type of NGS medical information further comprises:

generating edited NGS medical information from the NGS medical information;

providing the NGS medical information and the edited NGS medical information to an abstraction engine to retrain the abstraction engine to extract the edited NGS medical information from the first region using a second extraction rule; and

replacing the first extraction rule of the first type of NGS medical information in the template with the second extraction rule.

30. The method of claim 1 , wherein the extracted NGS medical information further comprises one of a variant, mutation, copy number variation, fusion, biomarker, tumor mutational burden, or microsatellite instability.