Gap in care determination using a generic repository for healthcare

• US 10,540,448 B2
• Filed: 07/15/2014
• Issued: 01/21/2020
• Est. Priority Date: 07/15/2013
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Inventors: Anand, Vikram, Rahmanian, Farbod, Hasling, William Michael, Thornton, Brian, Premananth, Muthu, Antohe, Daniel, Niculescu, Radu Stefan, Egan, Camille Anne, Reese, Bette Jane, Landi, William A, Fritsche, Todd W
• Primary Examiner(s): Le, Linh Giang
• Application Number:
  US14/331,320
• Publication Number:
  US 20150019248A1
• Time in Patent Office: 2,016 Days
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• 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

By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Claims

1. A method for determination of a gap in care, the method comprising:

translating a human readable data representation of medical quality data into a machine generic data representation;

mining a data source associated with a patient for values of attributes of a concept in the care for that patient, the data source being located on a healthcare information technology system;

identifying an error source, wherein the error source is a data set of the data source or an error value in the data set of the data source;

performing the mining without including the error source;

storing the values in a semantically normalized data repository of the care for that patient, the semantically normalized data repository including concepts, attributes of the concepts, and the values for the attributes in the machine generic data representation;

converting, by a processor, a human readable medical rule of a quality measure about the care into a machine executable language for the semantically normalized data repository; and

evaluating the semantically normalized data repository of the care for that patient with the human readable medical rule in the machine executable language, wherein evaluating comprises;

identifying a cohort associated with the human readable medical rule, the patient being a member of the cohort;

determining a gap in the care of the patient for the quality measure with the human readable medical rule, wherein the gap comprises conflicting or non-determinative information in semantically normalized data repository of the care for that patient;

outputting information for the gap wherein outputting comprises outputting an intervention or a task for the gap; and

requesting documentation relating to the intervention or the task to resolve the gap.

2. The method of claim 1 wherein querying comprises mapping from a database of the electronic medical record to the semantically normalized data repository.

3. The method of claim 1 wherein querying comprises parsing unstructured text with natural language processing.

4. The method of claim 1 wherein querying comprises inference reasoning.

5. The method of claim 1 wherein querying comprises mining in response to an addition or a change to the electronic medical record of the patient during a visit of the patient, and wherein outputting comprises outputting during the visit.

6. The method of claim 1 wherein querying comprises extracting the values for attributes of the concepts from multiple different data sources, and wherein storing comprises storing the values with a unified coding to a database.

7. The method of claim 1 wherein storing comprises storing the values with the attributes relevant for each of the concepts, the concepts comprising categories for the care where the categories are generic, the concepts having linked identities for the patient and timing.

8. The method of claim 1 wherein storing comprises storing the values with the semantically normalized data repository being agnostic such that multiple interfaces with a corresponding multiple data formats are not used.

9. The method of claim 1 wherein storing comprises storing a table of the concepts linked to tables for attributes of the concepts, the values being stored in the tables for the attributes.

10. The method of claim 1 wherein storing comprises modifying the semantically normalized data repository, the modifying comprising adding another attribute.

11. The method of claim 10 wherein modifying comprises changing a schema of the semantically normalized data repository.

12. The method of claim 10 wherein modifying comprises adding an attribute as a column in the semantically normalized data repository.

13. The method of claim 1 wherein converting comprises compiling an xml rule into the language.

14. The method of claim 1 wherein converting comprises converting the rule from a guideline, manual entry, or governing body into the language.

15. The method of claim 1 wherein converting comprises converting the rule from a health quality measure format to functions interacting with the semantically normalized data repository.

16. The method of claim 1 wherein evaluating comprises parsing the medical rule in the language and applying logic of the parsed medical rule to the semantically normalized data repository.

17. The method of claim 1 wherein identifying the cohort comprises identifying as a function of the concept, and wherein determining the gap comprises determining a violation of the medical rule.

18. The method of claim 1 further comprising:

changing the intervention or task when new data is available in the semantically normalized data repository.

19. The method of claim 1 further comprising stratifying risk and selecting the intervention or the task as a function of the risk and a cost.

20. The method of claim 1 wherein outputting comprises updating the semantically normalized data repository for the patient with the information.

21. The method of claim 1 wherein outputting comprises outputting an indication of success of an intervention.

22. The method of claim 1 wherein outputting comprises formatting the information into a quality reporting document architecture format.

23. The method of claim 1 further comprising editing, with user input, the concept in the semantic normalized data repository and/or the medical rule.

24. The method of claim 1 further comprising:

adding, editing, or deleting, by a user, the human readable medical rule.

25. The method of claim 1 further comprising:

redoing the querying in response to addition of information to the electronic medical record of a patient;

redoing the converting in response to the human readable medical rule is updated by a source of the human readable medical rule;

redoing the evaluating in response to a change of the human readable medical rule or addition of data to the semantically normalized data repository;

or combinations thereof.

26. A system for determination of a gap in care, the system comprising:

at least one memory configured to store clinical data for a patient in a first format; and

a processor configured to;

mine the clinical data of the patient for values of attributes of a concept in care of the patient, wherein the mining comprises parsing unstructured text with natural language processing;

identify an error source in the clinical data, wherein the error source is a data set of the clinical data or an error value in the data set of the clinical data;

perform the mining without including the error source;

convert a human readable medical rule of a quality measure about the care into a machine executable language for a semantically normalized data repository; and

evaluate the semantically normalized data repository of the care for the patient with the rule in the machine executable language, wherein the evaluation comprises;

identifying a cohort associated with the human readable medical rule, the patient being a member of the cohort; and

determining a gap in the care of the patient for the quality measure with the human readable medical rule, wherein the gap comprises conflicting or non-determinative information in semantically normalized data repository of the care for that patient;

outputting information for the gap wherein outputting comprises outputting an intervention or a task for the gap; and

requesting documentation relating to the intervention or the task to resolve the gap.

27. A non-transitory computer readable storage medium having stored therein data representing instructions executable by a programmed processor for determination of a gap in care, the storage medium comprising instructions for:

mining a data source associated with a patient for values of attributes of a concept in care for the patient, wherein the mining comprises parsing unstructured text with natural language processing;

identifying an error source, wherein the error source is a data set of the data source or an error value in the data set of the data source;

performing the mining without including the error source;

converting, by a processor, a human readable medical rule of a quality measure about the care into a machine executable language for a semantically normalized data repository; and

evaluating the semantically normalized data repository of the care for the patient with the rule in the machine executable language, wherein evaluating comprises;

identifying a cohort associated with the human readable medical rule, the patient being a member of the cohort;

determining a gap in the care of the patient for the quality measure with the human readable medical rule, wherein the gap comprises conflicting or non-determinative information in semantically normalized data repository of the care for that patient;

outputting information for the gap wherein outputting comprises outputting an intervention or a task for the gap; and

requesting documentation relating to the intervention or the task to resolve the gap.