Method of extracting real-time structured data and performing data analysis and decision support in medical reporting

• US 20100145720A1
• Filed: 12/07/2009
• Published: 06/10/2010
• Est. Priority Date: 12/05/2008
• Status: Abandoned Application

• Current Assignee: Bruce Reiner
• Original Assignee: Bruce Reiner
• Inventors: Reiner, Bruce
• Application Number:
  US12/591,979
• Publication Number:
  US 20100145720A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 50/205 Education administration or guidance
• CPC Class Codes: G06Q 50/2057 Career enhancement or continuing education service
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• 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: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The present invention relates to a methodology for the conversion of unstructured, free text data (contained within medical reports) into standardized, structured data, and also relates to a decision support feature for use in diagnosis and treatment options.

Claims

1. A computer-implemented method of identifying and extracting predetermined conceptual information from a free text report, comprising:

extracting data elements from the free text report;

performing a statistical analysis of said data elements to identify the predetermined conceptual information and locate synonymous nomenclature;

mapping said synonymous nomenclature to a standardized lexicon such that a single set of structured data elements is recorded as report data in a report in a report database; and

performing clinical validation of said nomenclature mapping step to verify said standardized lexicon.

2. The method according to claim 1, wherein said data elements include at least technical data, historical data, clinical data, and imaging data.

3. The method according to claim 2, further comprising:

performing outcomes analysis of said report data.

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

establishing a profile for a clinician that defines context-specific data requirements for said clinician.

6. The method according to claim 2, further comprising:

automatically editing said report.

7. The method according to claim 2, further comprising:

performing prospective structured data analysis of said report.

8. The method according to claim 2, further comprising:

providing data specific to said structured data elements; and

presenting educational content specific to said structured data elements.

5. The method according to claim 5, further comprising:

performing trending analysis to provide statistical data outlining performance metrics and best practice guidelines.

9. A computer-implemented method of providing data analysis and decision support in a medical application, comprising:

activating an automated differential diagnosis function;

inputting specific data elements derived from multiple informational data sources;

creating a list of differential diagnoses based upon said inputted data elements;

providing a statistical probability for each said list of differential diagnoses in rank order;

specifying a degree in which said inputted data elements contribute to or ignore said list of differential diagnoses;

providing another list of data elements which could confirm or deny said differential diagnoses; and

determining a medical diagnosis and a relative risk thereof.

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

providing information on a specific diagnosis, and supporting or conflicting data thereon.

11. The method according to claim 10, further comprising:

inputting patient-specific genetic data to determine a probability of disease occurrence.

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

retrieving data from a database to identify which data is available for analysis and which data is not available for analysis, after said inputting step.

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

determining association relationships between disparate data elements specific to said medical diagnosis.

15. The method according to claim 9, wherein an analysis of said database is used to create a user-specific decision support profile for at least an education/training program.

14. A computer-implemented method of providing data analysis and decision support in a medical application, comprising:

activating an automated differential diagnosis function;

inputting a specific medical diagnosis;

determining specific data elements derived from multiple informational data sources related to said medical diagnosis;

specifying a degree in which said data elements contribute to or ignore said medical diagnosis; and

determining whether said data elements confirm or deny said medical diagnosis.

16. A computer-implemented method of providing data analysis and decision support in a medical application, comprising:

providing medical data on a patient from a database;

identifying specific data of said medical data related to the patient and retrieving current and prior data from said database;

providing a statistical probability of relative importance of each specific data;

receiving a list of differential diagnoses;

performing an automated differential diagnosis function;

deriving a weighted differential diagnosis; and

providing specific data which contributed to said weighted differential diagnosis.

17. The method according to claim 16, further comprising:

selecting an individual diagnosis and providing diagnosis and/or treatment planning options.

18. The method according to claim 16, further comprising:

obtaining a statistical analysis to identify comparative data between different diagnoses and/or treatment planning options.

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

providing comparative complication rates in a defined geographic area.

20. The method according to claim 16, further comprising:

cross-referencing insurance data of said patient with provider data to determine a provider with a lowest complication rate.

21. The method according to claim 16, further comprising:

generating recommendations for disease prevention, diagnosis and/or treatment in accordance with patient and provider specific data.

22. The method according to claim 16, further comprising:

providing disease-specific data into said database and locating patients with similar data elements and defined diagnoses.

23. The method according to claim 16, further comprising:

inputting tests and/or procedures into said database to derive a statistical likelihood of iatrogenic complications or adverse reactions.

24. The method according to claim 16, further comprising:

inputting diagnosis and procedural data into said database to determine clinical outcomes.

25. The method according to claim 16, further comprising:

performing a cross-correlation of data to derive disease-specific best practice guidelines.

26. The method according to claim 16, further comprising:

creating technology and provider-specific clinical outcomes statistics from specific diagnoses and patient profiles.

27. The method according to claim 16, further comprising:

utilizing multi-institutional databases to create patient, institutional, and technology-specific profiles.

28. The method according to claim 16, further comprising:

marking specific structured data elements contained within report data;

providing data specific to said structured data elements; and

providing educational content specific to said highlighted structured data elements.

29. A computer-implemented method of providing an education and training feature in a medical application, comprising:

activating an education option for a user;

displaying a selected option from one of diagnosis, prevention or treatment;

providing the user with a training option;

providing the user with an option for obtaining additional data, or testing with a cost/benefit analysis thereof;

providing feedback to the user as to which data is supportive or which data is contradictory along with relative weighting of said data; and

providing analyses to the user along with derived data and comparative data of peers.

30. The method according to claim 29, further comprising:

recording said data for future review and analyses.