Voice Based System and Method for Data Input

• US 20140019128A1
• Filed: 06/27/2013
• Published: 01/16/2014
• Est. Priority Date: 01/05/2011
• Status: Abandoned Application

• Current Assignee: Health Fidelity, Inc.
• Original Assignee: Daniel J. Riskin
• Inventors: Shroff, Anand, Riskin, Daniel J., Chow, Yan, Dummett, Brian A., Tiwari, Ritu Raj
• Application Number:
  US13/929,236
• Publication Number:
  US 20140019128A1
• Time in Patent Office: Days
• US Class Current: 704/235
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence
• CPC Class Codes: G10L 2015/221 Announcement of recognition results
• 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 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Described herein are systems and methods for transforming a speech input into machine-interpretable structured data. In some embodiments, a system may include an automated speech recognition (ASR) engine configured to receive a live speech input and to continuously generate a text of the live speech input, a natural language processing (NLP) engine configured to transform the text into machine-interpretable structured data, and a user interface device configured to display the live speech input and a corresponding portion of the structured data in a predetermined order with respect to the structured data. In some embodiments, the method may include the steps of receiving a speech input with a speech capture component of a user interface device, generating a text from the speech input, identifying textual cues in the text, modifying the text based on the textual cues, and transforming the modified text into machine-interpretable structured data.

Claims

1. A system for transforming a live speech input into machine-interpretable structured data, the system comprising:

an automated speech recognition (ASR) engine configured to receive a live speech input and to generate a text of the live speech input;

a natural language processing (NLP) engine configured to receive the text and to transform the text into machine-interpretable structured data; and

a user interface device configured to display the live speech input and a corresponding portion of the structured data in a predetermined order with respect to the structured data such that it may be reviewed, edited, or maintained as a record by a user.

2. The system of claim 1, wherein the display of the portion of the structured data provides real time feedback to the user.

3. A system for transforming a speech input into machine-interpretable structured data, the system comprising:

an automated speech recognition (ASR) engine configured to receive a speech input and to generate a text of the speech input;

a metaspeech processor configured to identify textual cues in the text and to modify the text based on the identified textual cues; and

a natural language processing (NLP) engine configured to receive the modified text and to transform the text into machine-interpretable structured data.

4. The system of claim 3, wherein the ASR engine is further configured to receive a portion of the machine-interpretable structured data in addition to the speech input and to generate a text with improved accuracy based on the combination of the speech input and the structured data.

5. The system of claim 3, wherein the speech input includes multiple subject matter sections that include at least two of a history of present illness section, a past medical history section, a past surgical history section, an allergies to medications section, a current medications section, a relevant family history section, and a social history section.

6. The system of claim 5, wherein the ASR engine is further configured to receive a portion of the structured data and to thereby classify a current subject matter section of the speech input based on the structured data and to change at least one of a lexicon and a word weighting used to generate the text according to the current subject matter section.

7. The system of claim 3, wherein identifying textual cues comprises at least one of identifying keywords in the text and identifying patterns in the text.

8. The system of claim 3, wherein the modification based on the identified textual cues includes at least one of organizing the text into sections and replacing words in the text.

9. The system of claim 3, wherein the modification based on the identified textual cues includes at least one of changing at least one of a lexicon and a word weighting used by the ASR engine to generate a text.

10. The system of claim 3, wherein the NLP engine is configured to employ an algorithm to scan the text and to apply syntactic and semantic rules to the text to transform the text into machine-interpretable structured data.

11. A method for transforming a speech input into machine-interpretable structured data, the method comprising:

generating a text from the speech input with an automated speech recognition (ASR) engine of a internet-based computer network;

identifying textual cues in the text;

modifying the text based on the textual cues by performing at least one of organizing the text into predetermined sections and substituting words in the text; and

transforming the modified text into machine-interpretable structured data with a natural language processing (NLP) engine of the internet-based computer network.

12. The method of claim 11, wherein the speech input comprises multiple subject matter sections include at least two of a history of present illness section, a past medical history section, a past surgical history section, an allergies to medications section, a current medications section, a relevant family history section, and a social history section.

13. The method of claim 12, wherein the generating a text step further comprises classifying the section of the speech input received by the ASR engine based on the structured data.

14. The method of claim 11, the identifying textual cues step further comprising at least one of identifying keywords and identifying patterns.

15. The method of claim 11, the modifying the text step further comprising at least one of organizing the text into sections and replacing words in the text.

16. The method of claim 11, the modifying the text step further comprising at least one of changing at least one of a lexicon and a word weighting used by the ASR engine in the generating a text step.

17. A method for transforming a speech input into machine-interpretable structured data, the method comprising:

receiving a speech input with an automated speech recognition (ASR) engine of an internet-based computer network;

generating a text from the speech input with the ASR engine using a first library;

transforming the text with a natural language processing (NLP) engine of the internet-based computer network into machine-interpretable structured data;

determining a context of the text based on the structured data;

generating an updated text from the speech input with the ASR engine using a second library selected based on the context of the text; and

transforming the updated text with the NLP engine of the internet-based computer network into updated machine-interpretable structured data.

18. The method of claim 17, wherein the first library is a general medical library.

19. The method of claim 17, wherein the second library is more specific than the first library.

20. The method of claim 19, wherein the second library is a context specific speech library.

21. The method of claim 17, wherein the determining a context of the text step further comprises performing a postprocessing analysis of the structured data.

22. The method of claim 17, wherein the speech input comprises multiple subject matter sections include at least two of a history of present illness section, a past medical history section, a past surgical history section, an allergies to medications section, a current medications section, a relevant family history section, and a social history section.

23. The method of claim 22, wherein the determining a context of the text step further comprises classifying the subject matter section of the speech input received by the ASR engine based on the structured data.

24. The method of claim 22, determining a context of the text step further comprising at least one of identifying keywords and identifying patterns.

25. The method of claim 22, determining a context of the text step further comprising scanning the text for keywords in the text.

26. The method of claim 22, determining a context of the text step further comprising employing an algorithm to scan the text and to apply syntactic and semantic rules to the text.

27. The method of claim 17, the transforming the text steps further comprising organizing the text into sections.

28. The method of claim 17, wherein the receiving a speech input step comprises receiving a speech input over the internet.

29. The method of claim 17, wherein the receiving a speech input step comprises receiving a speech input from a physician of an encounter note.

30. The method of claim 29, wherein the receiving a speech input step comprises receiving a speech input comprising at least one of a History and Physical (H&

P) note or a Subjective, Objective, Assessment, and Plan (SOAP) note.

31. The method of claim 17, wherein the step of transforming the text comprises transforming the text into structured data in at least one of a Clinical Document Architecture (CDA), a Continuity of Care Record (CCR), and a Continuity of Care Document (CCD) format.

32. The method of claim 17, wherein the step of transforming the text comprises transforming the text into structured data that is configured to be compatible with at least one of health information exchanges (HIES), Electronic Medical Records (EHRs), and personal health records.