Predicting glucose trends for population management

A computer-implemented method in a clinical computing environment comprising:

• receiving, via a first computing process, glucose data for a plurality of patients from a plurality of sources, the glucose data including context data and demographic data associated with the glucose data and the plurality of patients;
• generating, via a second computing process, a predictive model based on the glucose data using one or more logistic or linear regression models;
• employing, via a third computing process, the predictive model to predict a likelihood of a particular patient to have blood glucose levels corresponding to a predetermined threshold;
• communicating, via a fourth computing process, a prediction and one or more interventions to a care team and the patient based on the likelihood;
• automatically adjusting a frequency or dosage of medication dispensed by an integrated home device associated with the patient based on the one or more interventions;
• and
• incorporating, via a fifth computing process, the one or more interventions into a clinical device workflow associated with a clinician on the care team;
• wherein each of the computing processes is performed by one or more computing devices.

A system comprising:

• a glucose database storing glucose data received for a plurality of patients from a plurality of sources, the glucose data including context data and demographic data associated with the glucose data and the plurality of patients;
• one or more processors;
• and
• one or more computer storage media storing instructions, the instructions when executed by the one or more processors, cause the one or more processors to:
• generate one or more predictive models based on the glucose data using one or more logistic or linear regression models;
• employ the one or more predictive models to predict a likelihood of a particular patient to have blood glucose levels corresponding to a predetermined threshold;
• communicate a prediction and one or more interventions to a care team or the particular patient based on the likelihood;
• and
• communicate a frequency or dosage of medication change to an integrated home device associated with the particular patient based on the one or more interventions.