Clinical content analytics engine

• US 9,996,670 B2
• Filed: 02/19/2014
• Issued: 06/12/2018
• Est. Priority Date: 05/14/2013
• Status: Active Grant

• Current Assignee: Zynx Health, Inc. (The Hearst Corporation)
• Original Assignee: Zynx Health, Inc. (The Hearst Corporation)
• Inventors: Dejori, Mathaeus
• Primary Examiner(s): Patel, Neha
• Application Number:
  US14/183,919
• Publication Number:
  US 20140343957A1
• Time in Patent Office: 1,574 Days
• 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

Abstract

Clinical content analytics engines and associated processes are described. An engine receives a clinical decision support document, accesses corresponding reference content, identifies and extracts medical intervention content from the clinical decision support document, segments extracted medical intervention content into a first plurality of segments including at least a first segment comprising a first set of text, determines if the first segment corresponds to at least a first item included in the reference content, the first item comprising a second set of text comprising terminology different than that found in the first set of text, and in response to determining that the first segment corresponds to the first item included in the reference content, causing a report to include an indication that the first segment corresponds to the first item included in the reference content.

Claims

1. A computer-implemented method of analyzing a clinical decision support (CDS) document and improving content analyzer system accuracy, the method comprising:

improving content analyzer system accuracy in identifying CDS document deficiencies and consistencies with respect to reference content, the reference content comprising clinical guidelines, by repeatedly training a machine learning module, hosted by a content analyzer system, based on new incoming data,

wherein the machine learning module is configured to automatically determine which content features are to be used to determine whether content is to be designated as relevant and matching to the reference content and which content is to be designated as non-relevant and to construct or modify an electronic model accordingly, wherein the features comprise one or more of text length, presence of a medication term, medical intervention language, use of a negation, or context,

wherein improving content analyzer system accuracy by training the machine learning module comprises repeatedly;

collecting positive and negative cases from CDS documents,

training the electronic model using the collected positive and negative cases from CDS documents,

taking as input a text segment extracted from a CDS document and returning a likelihood that the text segment matches a reference checklist item, and

wherein the new incoming data indicates whether the likelihood that the text segment matches a reference checklist item is correct or incorrect;

receiving at a computer system, including hardware and comprising an analytics engine, a clinical decision support document from a medical service provider system;

accessing over a network from a remote system, by the computer system, reference content corresponding at least in part to the clinical decision support document, the reference content comprising clinical guidelines;

using, by the computer system, the electronic model to identify and extract medical intervention content from the clinical decision support document;

using feedback with respect to the identification of the medical intervention content to refine the electronic model;

segmenting, by the computer system, at least a portion of the extracted medical intervention content into a first plurality of segments including;

at least a first segment, comprising a first set of text, and

a second segment comprising a second set of text,

wherein a given segment in the first plurality of segments is evaluated to identify a core concept, wherein identifying the core concept further comprises determining whether a given segment includes a plurality of medications, and determining which of the plurality of medications are part of the core concept and which of the plurality of medications are not part of the core concept, and

if the core concept of the given segment comprises at least one a medical intervention,

determining whether a negation is associated with the at least one medical intervention;

determining, by the trained machine learning engine, if the first segment corresponds to at least a first item included in the reference content, the first item comprising a third set of text comprising terminology not present in the first and second sets of text;

at least partly in response to determining that the first segment, comprising the first set of text, corresponds to the first item included in the reference content, the first item comprising the third set of text, causing a version of the clinical decision support document to be generated to include a visual indication that the first segment corresponds to the first item included in the reference content;

determining, by the trained machine learning engine, if a second item included in the reference content corresponds to at least one of the first plurality of segments;

at least partly in response to determining that the second item included in the reference content does not correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to be generated to include a visual indication that the first plurality of segments fails to include at least one segment that corresponds to the second item included in the reference content; and

at least partly in response to determining that the second item included in the reference content does correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to include a visual indication that the second item corresponds to at least one segment in the first plurality of segments.

2. The method as defined in claim 1, wherein the clinical decision support document comprises at least one of an order set, a care plan, or clinical support rules.

3. The method as defined in claim 1, wherein the reference content comprises a clinical guideline or a checklist, or both a clinical guideline and a checklist.

4. The method as defined in claim 1, wherein determining if the first segment corresponds to the first item included in the reference content further comprises determining if the first segment comprises a functional synonym using different terminology with respect to the first item included in the reference content.

5. The method as defined in claim 1, wherein determining if the first segment corresponds to the first item included in the reference content further comprises determining if a phrase in the first segment comprises a core concept of the first segment.

6. The method as defined in claim 1, wherein determining if the first segment corresponds to the first item included in the reference content further comprises generating a syntactic parse tree and using the syntactic parse tree to determine if a phrase in the first segment comprises a core concept of the first segment.

7. The method as defined in claim 1, wherein the clinical decision support document comprises field identifiers and corresponding data.

8. The method as defined in claim 1, the method further comprising:

determining whether a first character string in the clinical decision support document corresponds to a network protocol; and

at least partly in response to determining that the first character string in the clinical decision support document corresponds to a network protocol, excluding the first character string from the first plurality of segments.

9. The method as defined in claim 1, the method further comprising:

determining whether a first character string in the clinical decision support document exceeds a first number of words or characters; and

at least partly in response to determining that the first character string in the clinical decision support document exceeds the first number of words or characters, excluding the first character string from the first plurality of segments.

10. The method as defined in claim 1, wherein segmenting, by the computer system, at least a portion of the extracted content into the first plurality of segments further comprises identifying text that is part of a same medically-related intervention.

11. The method as defined in claim 1, wherein segmenting, by the computer system, at least a portion of the extracted content into the first plurality of segments further comprises identifying text that is part of a same medical-related intervention based at least in part on one or more of punctuation or indentions.

12. The method as defined in claim 1, wherein the analytics engine comprises a text extraction module, an entity alignment module, and a pattern matching engine and/or a machine learning engine.

13. The method as defined in claim 1, the method further comprising using a pattern matching engine and/or a machine learning engine to identify content that is to be included in the first plurality of segments.

14. The method as defined in claim 1, wherein the identification and extraction of medical intervention content from the clinical decision support document further comprises:

identifying content related to medicine dosages; and

identifying content related to a vital signs measurement.

15. The method as defined in claim 1, wherein the first segment, corresponding to the first item, does not include any words in common with the first item.

16. The method as defined in claim 1, the method further comprising generating an assessment document indicating how many interventions have and have not been Verified in two or more categories of intervention.

17. A computer-implemented method of analyzing a clinical decision support (CDS) document and improving content analyzer system accuracy, the method comprising:

improving content analyzer system accuracy in identifying CDS document deficiencies and consistencies with respect to reference content, the reference content comprising clinical guidelines, by repeatedly training a machine learning module, hosted by a content analyzer system, based on new incoming data,

wherein the machine learning module is configured to automatically determine which content features are to be used to determine whether content is to be designated as relevant and matching to the reference content and which content is to be designated as non-relevant and to construct or modify an electronic model accordingly, wherein the features comprise one or more of text length, presence of a medication term, medical intervention language, use of a negation, or context,

wherein improving content analyzer system accuracy by training the machine learning module comprises repeatedly;

collecting positive and negative cases from CDS documents,

training the electronic model using the collected positive and negative cases from CDS documents,

taking as input a text segment extracted from a CDS document and returning a likelihood that the text segment matches a reference checklist item, and

wherein the new incoming data indicates whether the likelihood that the text segment matches a reference checklist item is correct or incorrect;

receiving at a computer system, including hardware and comprising an analytics engine, a clinical decision support document and/or a data extract from a medical service provider system;

accessing over a network from a remote system, by the computer system, reference content, the reference content comprising clinical guidelines, corresponding at least in part to the clinical decision support document and/or the data extract from the medical service provider system;

using, by the computer system, the electronic model to identify and extract medical intervention content from the clinical decision support document and/or the data extract from the medical service provider system;

using feedback with respect to the identification of the medical intervention content to refine the electronic model;

segmenting, by the computer system, at least a portion of the extracted medical intervention content into a first plurality of segments including at least a first segment, comprising a first set of text, and a second segment comprising a second set of text, wherein a given segment in the first plurality of segments is evaluated to identify a core concept, wherein identifying the core concept further comprises determining whether a given segment includes a plurality of medical interventions, and determining which of the plurality of medical interventions are part of the core concept and which of the plurality of medical interventions are not part of the core concept, and

if the core concept of the given segment comprises at least one a medical intervention,

determining whether a negation is associated with the medical intervention;

determining, by the trained machine learning engine, if the first segment corresponds to at least a first item included in the reference content, the first item comprising a third set of text different than the first and second sets of text;

at least partly in response to determining that the first segment, comprising the first set of text, corresponds to the first item included in the reference content, the first item comprising the third set of text, causing a report to be generated to include a visual indication that the first segment corresponds to the first item included in the reference content;

determining, by the trained machine learning engine, if a second item included in the reference content corresponds to at least one of the first plurality of segments;

at least partly in response to determining that the second item included in the reference content does not correspond to at least one of the first plurality of segments, causing the report to include a visual indication that the first plurality of segments fails to include at least one segment that corresponds to the second item included in the reference content; and

at least partly in response to determining that the second item included in the reference content does correspond to at least one of the first plurality of segments, causing the report to include a visual indication that the first plurality of segments includes at least one segment that corresponds to the second item included in the reference content.

18. The method as defined in claim 17, wherein the identification and extraction of medical intervention content from the clinical decision support document further comprises:

identifying content related to medicine dosages; and

identifying content related to a vital signs measurement.

19. The method as defined in claim 17, wherein determining if the first segment corresponds to the first item included in the reference content further comprises determining if the first segment comprises a functional synonym using different terminology with respect to the first item included in the reference content.

20. The method as defined in claim 17, wherein determining if the first segment corresponds to the first item included in the reference content further comprises determining if a phrase in the first segment comprises a core concept of the first segment.

21. The method as defined in claim 17, the method further comprising:

determining whether a first character string in the clinical decision support document corresponds to a network protocol; and

at least partly in response to determining that the first character string in the clinical decision support document corresponds to a network protocol, excluding the first character string from the first plurality of segments.

22. The method as defined in claim 17, the method further comprising:

determining whether a first character string in the clinical decision support document exceeds a first number of words or characters; and

at least partly in response to determining that the first character string in the clinical decision support document exceeds the first number of words or characters, excluding the first character string from the first plurality of segments.

23. The method as defined in claim 17, wherein segmenting, by the computer system, at least a portion of the extracted content into the first plurality of segments further comprises identifying text that is part of a same medical-related intervention.

24. The method as defined in claim 17, wherein segmenting, by the computer system, at least a portion of the extracted content into the first plurality of segments further comprises identifying text that is part of a same medical-related intervention based at least in part on one or more of punctuation or indentions.

25. The method as defined in claim 17, wherein the analytics engine comprises a text extraction module, an entity alignment module, and a pattern matching engine and/or a machine learning engine.

26. The method as defined in claim 17, wherein the identification and extraction of medical intervention content from the clinical decision support document further comprises identifying content related to a medical intervention.

27. The method as defined in claim 17, wherein the identification and extraction of medical intervention content from the clinical decision support document further comprises identifying content related to medicine dosages.

28. The method as defined in claim 17, wherein the identification and extraction of medical intervention content from the clinical decision support document further comprises identifying content related to a vital signs measurement.

29. The method as defined in claim 17, wherein the first segment, corresponding to the first item, does not include any words in common with the first item.

30. The method as defined in claim 17, the method further comprising generating an assessment document indicating how many interventions have and have not been verified in two or more categories of intervention.

31. An analytics system configured to improve content analyzer system accuracy, comprising:

at least one processing device comprising hardware;

non-transitory media comprising a text extraction module, an entity alignment module, and at least one or a pattern matching engine or a machine learning engine, the when executed by the at least one processing device, are configured to cause the analytics system to perform operations comprising;

improving content analyzer system accuracy in identifying CDS document deficiencies and consistencies with respect to reference content, the reference content comprising clinical guidelines, by repeatedly training a machine learning module based on new incoming data,

wherein the machine learning module is configured to automatically determine which content features are to be used to determine whether content is to be designated as relevant and matching to the reference content and which content is to be designated as non-relevant and to construct or modify an electronic model accordingly, wherein the features comprise one or more of text length, presence of a medication term, medical intervention language, use of a negation, or context,

wherein improving content analyzer system accuracy by training the machine learning module comprises repeatedly;

collecting positive and negative cases from CDS documents,

training the electronic model using the collected positive and negative cases from CDS documents,

taking as input a text segment extracted from a CDS document and returning a likelihood that the text segment matches a reference checklist item, and

wherein the new incoming data indicates whether the likelihood that the text segment matches a reference checklist item is correct or incorrect;

receiving a clinical decision support document;

accessing reference content corresponding at least in part to the clinical decision support document, the reference content comprising clinical guidelines;

identifying and extracting, using the electronic model, medical intervention content from the clinical decision support document;

using feedback with respect to the identification of the medical intervention content to refine the electronic model;

segmenting at least a portion of the extracted medical intervention content into a first plurality of segments including at least a first segment, comprising a first set of text, and a second segment comprising a second set of text, wherein a given segment in the first plurality of segments is evaluated to identify a core concept, wherein identifying the core concept further comprises determining whether a given segment includes a plurality of medical interventions, and determining which of the plurality of medical interventions are part of the core concept and which of the plurality of medical interventions are not part of the core concept, and

if the core concept of the given segment comprises at least one a medical intervention, determining whether a negation is associated with the medical intervention;

determining, using the trained machine learning engine, if the first segment corresponds to at least a first item included in the reference content, the first item comprising a third set of text different than the first and second sets of text;

at least partly in response to determining that the first segment, comprising the first set of text, corresponds to the first item included in the reference content, the first item comprising the third set of text, causing a report be generated to include a visual indication that the first segment corresponds to the first item included in the reference content;

determining, using the trained machine learning engine, if a second item included in the reference content corresponds to at least one of the first plurality of segments;

at least partly in response to determining that the second item included in the reference content does not correspond to at least one of the first plurality of segments, causing the report to include a visual indication that the first plurality of segments fails to include at least one segment that corresponds to the second item included in the reference content;

at least partly in response to determining that the second item included in the reference content does correspond to at least one of the first plurality of segments, causing the report to include a visual indication that the first plurality of segments includes at least one segment that corresponds to the second item included in the reference content.

32. The system as defined in claim 31, wherein identifying and extracting content from the clinical decision support document further comprises:

identifying content related to medicine dosages; and

identifying content related to a vital signs measurement.

33. The system as defined in claim 31, wherein determining if the first segment corresponds to the first item included in the reference content further comprises determining if the first segment comprises a functional synonym using different terminology with respect to the first item included in the reference content.

34. The system as defined in claim 31, wherein determining if the first segment corresponds to the first item included in the reference content further comprises determining if a phrase in the first segment comprises a core concept of the first segment.

35. The system as defined in claim 31, the system further comprising a negation detection and syntactic analysis system.