Decision support with clinical nomenclatures

• US 10,446,273 B1
• Filed: 01/06/2014
• Issued: 10/15/2019
• Est. Priority Date: 08/12/2013
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Inventors: McNair, Douglas S., Murrish, John Christopher, Kailasam, Kanakasabha
• Primary Examiner(s): Chng, Joy
• Assistant Examiner(s): Barr, Evangeline
• Application Number:
  US14/148,050
• Time in Patent Office: 2,108 Days
• Field of Search: None
• 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: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16H 80/00 ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Abstract

Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients. Embodiments of the invention provide decision support services including providing timely contextual patient information including condition risks, risk factors and relevant clinical information that are dynamically updatable; imputing missing patient information; dynamically generating assessments for obtaining additional patient information based on context; data-mining and information discovery services including discovering new knowledge; identifying or evaluating treatments or sequences of patient care actions and behaviors, and providing recommendations based on this; intelligent, adaptive decision support services including identifying critical junctures in patient care processes, such as points in time that warrant close attention by caregivers; near-real time querying across diverse health records data sources, which may use diverse clinical nomenclatures and ontologies; improved natural language processing services; and other decision support services.

Claims

1. A computer-readable device having computer-executable instructions embodied thereon that when executed, facilitate a method for improving a clinical decision support system, the method comprising:

receiving a plurality of first clinical concepts encoded in a first nomenclature that is used by an electronic medical record system associated with a healthcare entity;

generating a plurality of mapped encoded first clinical concepts by mapping the plurality of first encoded clinical concepts from the first nomenclature that is used by the electronic medical record system associated with the healthcare entity to a second nomenclature associated with a metadata smart layer;

assessing from a library of clinical condition programs, a first clinical condition program implemented by an autonomous software agent operating on the metadata smart layer associated with the second nomenclature to determine a probability that a patient has a first clinical condition encoded in the second nomenclature;

utilizing the autonomous software agent within an autonomous multi-agent software system to determine the probability that the patient has the first clinical condition encoded in the second nomenclature based on a subset of second clinical concepts encoded in the second nomenclature that corresponds to the plurality of mapped first encoded clinical concepts;

identifying a set of first-condition information encoded in the second nomenclature to be mapped to the first nomenclature, the set of first-condition information including the first clinical condition and the subset of second clinical concepts used by the first clinical condition program, the first clinical condition and subset of clinical concepts being encoded in the second nomenclature;

mapping the set of first-condition information from the second nomenclature to the first nomenclature; and

providing, via a user interface, the mapped first-condition information and the probability that the patient has the first clinical condition to the healthcare entity in the first nomenclature.

2. The computer readable device of claim 1 further comprising:

presenting in the user interface, a menu including the first clinical condition; and

responsive to selecting the first clinical condition, presenting;

(1) a condition risk score representing the determined probability that the patient has for the first clinical condition;

(2) a set of risk factors corresponding to the subset of clinical concepts used by the first clinical condition program; and

(3) for at least a portion of the risk factors, a clinical value for the factor, the value determined based on information derived from the patient.

3. The computer readable device of claim 1, wherein generating the plurality of mapped encoded first clinical concepts by mapping the plurality of first encoded clinical concepts from the first nomenclature to the second nomenclature uses the autonomous multi-agent software system.

4. The computer readable device of claim 2, wherein at least one of the presented menu, the presented condition risk score, or the set of risk factors are dynamically responsive to changes in the first clinical condition program.

5. The computer readable device of claim 1, wherein the plurality of first clinical concepts comprise lab results, test results, patient conditions, patient health history, patient demographic information, or other discretized health-related information.

6. The computer readable device of claim 1, wherein the first clinical condition includes one or more of a disease, diagnoses, medical issue, or medical event.

7. The computer readable device of claim 1, wherein probability for the first clinical condition is a calculated probability that the patient has or will develop the first clinical condition based on at least a portion of the mapped first encoded clinical concepts for the patient.

8. The computer readable device of claim 2, wherein risk factors comprise one or more clinical variables of health data from population of patients that are determined to contribute to the that a given patient has or will develop the first clinical condition.

9. The computer readable device of claim 8, wherein the clinical variables correspond to clinical concepts.

10. The computer readable device of claim 2, wherein the clinical value comprises a patient-specific value corresponding to a clinical variable.

11. The computer readable device of claim 1, wherein the plurality of first clinical concepts comprises patient health information including specific lab results, tests, health-related findings, patient conditions, patient history, or other clinical-information component of a patient'"'"'s health data.

12. The computer readable device of claim 2, wherein the presented condition risk score and set of risk factors are dynamically responsive to changes in the first clinical condition program.

13. The computer readable device of claim 2, further comprising presenting a condition assessment area for presenting a contextually-driven assessment based on patient information relevant to diagnosing or treating a condition, and for receiving patient information in response to presenting the assessment, wherein the received information includes one or more clinical concepts encoded in the first nomenclature.

14. The computer readable device of claim 2, further comprising presenting a condition assessment area for presenting a contextually-driven assessment based on patient health data relevant to diagnosing or treating the first clinical condition and determining that patient information is absent or stale.

19. The computer readable device of claim 1, wherein the first clinical condition program is patient-specific.

20. The computer readable device of claim 1, wherein the metadata smart layer comprises de-identified patient data.

15. A computer-implemented method for improving a clinical decision support system, the method comprising:

receiving a plurality of first clinical concepts encoded in a first nomenclature that is used by an electronic medical record system associated with a healthcare entity;

generating a plurality of mapped encoded first clinical concepts by mapping the plurality of first encoded clinical concepts from the first nomenclature that is used by the electronic medical record system associated with the healthcare entity to a second nomenclature associated with a metadata smart layer;

assessing from a library of clinical condition programs, a first clinical condition program implemented by an autonomous software agent operating on the metadata smart layer associated with the second nomenclature to determine a probability that a patient has a first clinical condition encoded in the second nomenclature;

utilizing the autonomous software agent within an autonomous multi-agent software system to determine the probability that the patient has the first clinical condition encoded in the second nomenclature based on a subset of second clinical concepts encoded in the second nomenclature that corresponds to the plurality of mapped first encoded clinical concepts;

identifying a set of first-condition information encoded in the second nomenclature to be mapped to the first nomenclature, the set of first-condition information including the first clinical condition and the subset of second clinical concepts used by the first clinical condition program, the first clinical condition and subset of clinical concepts being encoded in the second nomenclature;

mapping the set of first-condition information from the second nomenclature to the first nomenclature; and

providing, via a user interface, the mapped first-condition information and the probability that the patient has the first clinical condition to the healthcare entity in the first nomenclature.

16. The method of claim 15, wherein generating the plurality of mapped encoded first clinical concepts by mapping the plurality of first encoded clinical concepts from the first nomenclature to the second nomenclature uses the autonomous multi-agent software system.

17. The method of claim 15, wherein the presented condition risk score and set of risk factors are dynamically responsive to changes in the first clinical condition program.

18. The method of claim 15, further comprising presenting a condition assessment area for presenting a contextually-driven assessment based on patient information relevant to diagnosing or treating the condition, and for receiving patient information in response to presenting the assessment, wherein the received information includes one or more clinical concepts encoded in the first nomenclature.