US-20260127977-A1 - System and Method for Training Medical Providers in Obesity and Cardiometabolic Health Management Using Artificial Intelligence and Competency-Based Assessments

Abstract

A system for training medical providers in obesity management and obesity-related comorbidities includes a user device presenting a conversational interface to a medical provider, an application server managing user sessions, a database server storing user data including course completion records and clinical performance audits, and a learning management system server delivering educational modules based on established competencies. An artificial intelligence server processes queries from the medical provider and provides personalized medical advice using a large language model trained on obesity-related medical content including obesity physiology, pharmacotherapy, and patient management. The artificial intelligence server receives outcome information related to patient health metrics following the personalized medical advice and retrains the large language model based on the outcome information. A performance audit system monitors clinical documentation, prescribing patterns, and patient outcomes of the medical provider to ensure ongoing compliance and performance improvements.

Inventors

• David H. Bass
• Leon Igel
• Christina Lorenzo
• Cheryl Pegus
• Nathan Lesch
• Guadalupe Minero
• Venkateswaran Suriyanarayanan

Assignees

• Intellihealth, Inc.

Dates

Publication Date

20260507

Application Date

20251030

Claims (20)

1. 1 . A system for training medical providers in obesity management and obesity-related comorbidities, the system comprising: a user device configured to present a conversational interface to a medical provider; an application server in communication with the user device, the application server configured to manage user sessions and process requests from the user device; a database server in communication with the application server, the database server configured to store user data comprising course completion records, knowledge assessment results, and clinical performance audits; a learning management system server in communication with the application server, the learning management system server configured to deliver educational modules based on established competencies and to track user progress through the educational modules; an artificial intelligence server in communication with the application server, the artificial intelligence server configured to process queries from the medical provider and provide personalized medical advice using a large language model trained on obesity-related medical content comprising obesity physiology, pharmacotherapy, and patient management, wherein the artificial intelligence server is further configured to receive outcome information related to patient health metrics following the personalized medical advice and to retrain the large language model based on the outcome information; and a performance audit system in communication with the application server, the performance audit system configured to monitor clinical documentation, prescribing patterns, and patient outcomes of the medical provider.
2. 2 . The system of claim 1 , wherein the database server comprises a combination of relational databases and NoSQL databases.
3. 3 . The system of claim 1 , further comprising a cloud hosting layer providing scalable storage and processing capabilities for the application server, the database server, the learning management system server, the artificial intelligence server, and the performance audit system.
4. 4 . The system of claim 3 , further comprising a security layer compliant with Health Insurance Portability and Accountability Act regulations, the security layer configured to protect data through encryption for data at rest and in transit.
5. 5 . The system of claim 1 , further comprising a collaboration server configured to facilitate real-time communication between the medical provider and subject matter experts through Health Insurance Portability and Accountability Act compliant chat channels and video conferencing.
6. 6 . The system of claim 5 , further comprising a feedback and frequently asked questions module in communication with the collaboration server, the feedback and frequently asked questions module configured to compile questions and responses to create a dynamic knowledge base.
7. 7 . The system of claim 1 , further comprising an analytics server in communication with the application server, the analytics server configured to process data comprising course completions, assessment scores, and clinical practice audits to generate insights into training effectiveness.
8. 8 . The system of claim 7 , wherein the analytics server is configured to apply machine learning algorithms to identify knowledge gaps and inform personalized feedback for the medical provider.
9. 9 . The system of claim 1 , wherein the learning management system server is configured to administer a series of educational modules based on Obesity Medicine Educational Collaborative competencies during an onboarding process.
10. 10 . The system of claim 9 , wherein the learning management system server is configured to conduct assessments at a beginning of the onboarding process, after the onboarding process, and at a designated time post-onboarding to evaluate competencies of the medical provider in obesity-related domains.
11. 11 . The system of claim 1 , wherein the learning management system server is configured to perform a skills assessment to verify proficiency of the medical provider in performing tasks within an electronic medical record system.
12. 12 . The system of claim 1 , wherein the performance audit system is configured to evaluate quality of patient care and identify areas where additional training is required and trigger educational interventions.
13. 13 . The system of claim 1 , wherein the obesity-related medical content further comprises type 2 diabetes, pre-bariatric surgery care, post-bariatric surgery care, metabolic dysfunction-associated steatohepatitis, metabolic dysfunction-associated steatotic liver disease, nonalcoholic fatty liver disease, non-alcoholic steatohepatitis, women's health, polycystic ovary syndrome, and menopausal weight gain.
14. 14 . A method for training medical providers in obesity treatment and obesity-related comorbidities, the method comprising: administering, by a learning management system server, a series of educational modules based on Obesity Medicine Educational Collaborative competencies during an onboarding process for a new medical provider; conducting, by the learning management system server, assessments at a beginning of the onboarding process, after the onboarding process, and at a designated time post-onboarding to evaluate competencies of the new medical provider in obesity-related domains; performing, by the learning management system server, a skills assessment to verify proficiency of the new medical provider in performing tasks within an electronic medical record system and clinical protocols; providing, by an artificial intelligence server, tailored remedial education based on gaps identified during a knowledge assessment; and continuously auditing, by a performance audit system, clinical documentation, prescriptions, and patient interactions of the new medical provider to ensure ongoing compliance and performance improvements.
15. 15 . The method of claim 14 , further comprising processing, by the artificial intelligence server, queries from the new medical provider using a large language model trained on obesity-related medical content.
16. 16 . The method of claim 15 , further comprising retraining, by the artificial intelligence server, the large language model based on outcome information related to patient health metrics to improve accuracy of future advice.
17. 17 . The method of claim 14 , wherein the obesity-related domains comprise obesity physiology, diabetes management, bariatric surgery care, and cardiometabolic comorbidities.
18. 18 . A system for training a medical provider in treating obesity and obesity-related comorbidities, the system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to: receive, from the medical provider via a user-facing application with a conversational interface, a query; provide advice to the medical provider based on a large language model trained on training content comprising obesity physiology, obesity pathophysiology, type 2 diabetes, pre-bariatric surgery care, post-bariatric surgery care, metabolic dysfunction-associated steatohepatitis, metabolic dysfunction-associated steatotic liver disease, nonalcoholic fatty liver disease, non-alcoholic steatohepatitis, women's health, polycystic ovary syndrome, and menopausal weight gain; receive outcome information based on the advice, wherein the outcome information comprises at least one of biomarker information, information about a medication, or diet type information; and retrain the large language model based on the outcome information for improved advice for treating a comorbid condition.
19. 19 . The system of claim 18 , wherein the large language model comprises a neural network.
20. 20 . The system of claim 18 , wherein the conversational interface is configured to understand and analyze the query with medical accuracy.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application claims the beneift and priority of US provisional application 63/716,064, filed on Nov. 4, 2024, US provisional application 63/716,074, filed on Nov. 4, 2024, and US provisional application 63/716,082, filed on Nov. 4, 2024 including all references and appendicies cited therein in their entireties, for all purposes, as if fully set forth herein. FIELD The present disclosure relates to systems and methods for training medical providers in obesity medicine and cardiometabolic health management using artificial intelligence, large language models, and competency-based assessment frameworks. SUMMARY A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. One general aspect includes a system for training medical providers in obesity management and obesity-related comorbidities. The system also includes a user device configured to present a conversational interface to a medical provider; an application server in communication with the user device, the application server configured to manage user sessions and process requests from the user device; a database server in communication with the application server, the database server configured to store user data may include course completion records, knowledge assessment results, and clinical performance audits; a learning management system server in communication with the application server, the learning management system server configured to deliver educational modules based on established competencies and to track user progress through the educational modules; an artificial intelligence server in communication with the application server, the artificial intelligence server configured to process queries from the medical provider and provide personalized medical advice using a large language model trained on obesity-related medical content may include obesity physiology, pharmacotherapy, and patient management, where the artificial intelligence server is further configured to receive outcome information related to patient health metrics following the personalized medical advice and to retrain the large language model based on the outcome information; and a performance audit system in communication with the application server, the performance audit system configured to monitor clinical documentation, prescribing patterns, and patient outcomes of the medical provider. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. Implementations may include one or more of the following features. The system where the database server may include a combination of relational databases and noSQL databases. The system may include a cloud hosting layer providing scalable storage and processing capabilities for the application server, the database server, the learning management system server, the artificial intelligence server, and the performance audit system. The system may include a security layer compliant with health insurance portability and accountability act regulations, the security layer configured to protect data through encryption for data at rest and in transit. The system may include a collaboration server configured to facilitate real-time communication between the medical provider and subject matter experts through health insurance portability and accountability act compliant chat channels and video conferencing. The system may include a feedback and frequently asked questions module in communication with the collaboration server, the feedback and frequently asked questions module configured to compile questions and responses to create a dynamic knowledge base. The system may include an analytics server in communication with the application server, the analytics server configured to process data may include course completions, assessment scores, and clinical practice audits to generate insights into training effectiveness. The analytics server is configured to apply machine learning algorithms to identify knowledge gaps and inform personalized feedback for the medical provider. The learning management system server is configured to administer a series of educational modules based on obesity medicine educational collaborative competencies during an onboarding process. The learning management system server is configured to conduct assessments at a beginning of the onboarding process, after the onboarding process, and at a designated time