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US 12,217,876 B2Granted
Method for recommending continuing education to health professionals based on patient outcomes
AssigneeImpactivo, LLC
InventorMaria Levis
Life of this patent
Estimating the term…
Abstract
Methods and systems for providing health professionals with continued education are based on performance gaps identified from patient data available in transactional systems of record. The methods can include creating a repository of educational material, measuring patient and team level performance gaps, associating the identified performance gaps with appropriate educational material, alerting the person about the appropriate educational material, capturing a user's interaction with the educational materials, and issuing credits or rewards for substantial consumption of the educational materials.
Claims19
A method for providing continuing education to a health care professional comprising:
- continually assessing, electronically, via a system application server, actual clinical performance of the health care professional as recorded in a clinical data system;
- automatically creating a set of granules of health care knowledge in a database on a system application server, the set of granules of health care knowledge including information, skills, and aptitudes that are intended to improve performance of the health care provider;
- training patient health risk factor machine learning models to generate patient health risk factor scores using social determinants and clinical data that correlate workflow assignment to patient outcomes;
- training continuing education machine learning models with data from the trained patient risk factor machine learning models to identify which of the set of granules of health care knowledge is correlated with improved patient outcomes;
- defining a set of configurable conditions on the system application server that use data from the clinical data system to trigger granule suggestions to the health care provider, wherein the configurable conditions include data from a specific one of the health care providers and data from one or more care teams to which the health care provider is assigned;
- analyzing the actual clinical performance of the healthcare provider continually to determine whether the actual clinical performance falls within the defined configurable conditions;
- using the trained continuing education machine learning models to deploy knowledge granules in accordance with the learner's preferences and learning styles;
- and
- electronically delivering the health care knowledge granule based on established preferences of the health care provider automatically.
6 more elements
A computer-assisted method for delivering health care knowledge to a health care provider, based on actual clinical performance recorded in a clinical data system, comprising:
- participating in interactions with patients and recording associated data in the clinical data system in communication with a system application server;
- automatically triggering one or more suggested health care knowledge granules based on performance of the health care provider, the one or more suggested health care knowledge granules electronically stored in a database of the system application server;
- obtaining a request, by the system application server, from the health care provider to deliver the one or more suggested health care knowledge granules;
- delivering, by the database of the system application server, the one or more suggested granules that are triggered for and requested by the health care provider;
- recording an interaction of the health care provider with the one or more suggested health care knowledge granules on the system application server;
- training patient health risk factor machine learning models to generate patient health risk factor scores using social determinants and clinical data that correlate workflow assignment to patient outcomes;
- training continuing education machine learning models with data from the trained patient risk factor machine learning models to identify which knowledge granules are correlated with improved patient outcomes and under what conditions;
- and
- using the trained continuing education machine learning models to assess knowledge granules that are related to learner preferences and learning styles.
6 more elements
A computer-assisted method for delivering health care knowledge to a health care provider, based on actual clinical performance recorded in a clinical data system, comprising:
- participating in interactions with patients and recording associated data in the clinical data system in communication with a system application server;
- automatically triggering one or more suggested health care knowledge granules based on performance of the health care provider, the one or more suggested health care knowledge granules electronically stored in a database of the system application server;
- obtaining a request, by the system application server, from the health care provider to deliver the one or more suggested health care knowledge granules;
- delivering, by the database of the system application server, the one or more suggested granules that are triggered for and requested by the health care provider;
- recording an interaction of the health care provider with the one or more suggested health care knowledge granules on the system application server;
- training workflow assignment machine learning models to assign patients to workflows to achieve improved patient outcomes;
- training patient health risk factor machine learning models to generate patient health risk factor scores using social determinants and clinical data that correlate workflow assignment to patient outcomes;
- and
- using the trained workflow assignment machine learning models to assign patients according to a patient health risk factor score to a workflow to improve assignment logic and rules for achieving patient outcomes.
6 more elements
Dependencies are inferred from the claim text.