Systems and methods for using machine learning algorithms to identify care gaps

A computing platform, comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed by the one or more processors, facilitate:
• obtaining population information for a plurality of individuals associated with an enterprise organization;
• filtering the population information using a plurality of care gap cohorts to determine a subset of the population information for each of the plurality of care gap cohorts;
• training a plurality of care gap machine learning-artificial intelligence (ML-AI) models based on the subset of the population information such that each of the plurality of care gap ML-AI models is associated with one or more care gap cohorts from the plurality of care gap cohorts;
• determining to retrain a first care gap ML-AI model, of the plurality of care gap ML-AI models, based on a first care gap cohort, of the plurality of care gap cohorts, having insufficient data to train the first care gap ML-AI model;
• based on the determination, combining a first subset of the population information associated with the first care gap cohort with a second subset of the population information associated with a second care gap cohort from the plurality of care gap cohorts to obtain combined population information;
• retraining the first care gap ML-AI model based on the combined population information;
• obtaining individual information of an individual, wherein the individual information indicates one or more medical conditions of the individual and a care gap of the individual indicating a gap in time that the individual has a lapse in receiving medical care for the one or more medical conditions;
• selecting the first care gap ML-AI model, from the plurality of care gap ML-AI models, based on comparing the gap in time that the individual has the lapse in receiving medical care for the one or more medical conditions with the plurality of care gap cohorts;
• determining care gap information of the individual based on using the first care gap ML-AI model and the individual information;
• and
• performing one or more care gap interventions based on the care gap information.

A non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed, facilitate:

• obtaining population information for a plurality of individuals associated with an enterprise organization;
• filtering the population information using a plurality of care gap cohorts to determine a subset of the population information for each of the plurality of care gap cohorts;
• training a plurality of care gap machine learning-artificial intelligence (ML-AI) models based on the subset of the population information such that each of the plurality of care gap ML-AI models is associated with one or more care gap cohorts from the plurality of care gap cohorts;
• determining to retrain a first care gap ML-AI model, of the plurality of care gap ML-AI models, based on a first care gap cohort, of the plurality of care gap cohorts, having insufficient data to train the first care gap ML-AI model;
• based on the determination, combining a first subset of the population information associated with the first care gap cohort with a second subset of the population information associated with a second care gap cohort from the plurality of care gap cohorts to obtain combined population information;
• retraining the first care gap ML-AI model based on the combined population information;
• obtaining individual information of an individual, wherein the individual information indicates one or more medical conditions of the individual and a care gap of the individual indicating a gap in time that the individual has a lapse in receiving medical care for the one or more medical conditions;
• selecting the first care gap ML-AI model, from the plurality of care gap ML-AI models, based on comparing the gap in time that the individual has the lapse in receiving medical care for the one or more medical conditions with the plurality of care gap cohorts;
• determining care gap information of the individual based on using the first care gap ML-AI model and the individual information;
• and
• performing one or more care gap interventions based on the care gap information.