Abstract: Provided are computer implemented method and systems for providing and monitoring patient compliance with a patient healthcare treatment plan. The method includes receiving, from a healthcare provider over a network, application features for generating a patient application including patient instructions for using a medical therapy, and generating an application for a patient. The application includes at least an input for the user to input data for use in evaluating patient compliance with a treatment plan. In addition, the method includes receiving, from the healthcare provider over the network, a prescription for the application for the patient, and activating the application after the patient receives training on use of the application. The method also may include receiving patient compliance data from the application over the network based on the input.

Abstract: Systems and methods are provided for selecting one or more models for predicting medical conditions. An exemplary method may include receiving data related to a patient and extracting metadata from the received data. The method may further include selecting the one or more models from a library of models based on the extracted metadata and applying the selected one or more models. The method also may include generating a notification when the application of the selected one or more model indicates an intervention is necessary.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Computer implemented methods are disclosed. The methods may include receiving historical data comprising at least one of provider data and patient data, and processing, using a processor, the historical data to identify one or more patterns. The method also may include generating one or more decision models from the historical data and the decision patterns, and providing one or more recommendations based on the one or more decision models.

Abstract: Methods and devices include database management and graphical user interfaces for measurements collected by analyzing blood.

Abstract: A method may receive historical metabolic values for an individual having a first medical condition. A method may provide a first subset of the historical metabolic values to a machine learning model to train a generative machine learning model. A method may generate a first predicted metabolic value based on the first subset of historical metabolic values. A method may calculate a root mean square error (RMSE) between the first predicted metabolic value and a corresponding actual metabolic value of a second subset of historical metabolic values. A method may train the generative machine learning model to minimize the RMSE. A method may generate a trained generative machine learning model based on the training.

Abstract: Computer implemented methods are disclosed. The methods may include receiving historical data comprising at least one of provider data and patient data, and processing, using a processor, the historical data to identify one or more patterns. The method also may include generating one or more decision models from the historical data and the decision patterns, and providing one or more recommendations based on the one or more decision models.

Abstract: Methods and devices include database management and graphical user interfaces for measurements collected by analyzing blood.

Abstract: Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, and determining, using the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels.

Abstract: Methods and devices include automated coaching for management of glucose states by receiving a user's glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing self-management behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.

Abstract: A method may receive historical metabolic values for an individual having a first medical condition. A method may provide a first subset of the historical metabolic values to a machine learning model to train a generative machine learning model. A method may generate a first predicted metabolic value based on the first subset of historical metabolic values. A method may calculate a root mean square error (RMSE) between the first predicted metabolic value and a corresponding actual metabolic value of a second subset of historical metabolic values. A method may train the generative machine learning model to minimize the RMSE. A method may generate a trained generative machine learning model based on the training.

Abstract: Methods and devices include automated coaching for management of glucose states by receiving a user's glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing self-management behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.

Abstract: Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, and determining, using the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels.

Abstract: Systems and methods are provided for managing medical adherence. An exemplary method may include managing medical adherence utilizing data aggregating and processing to determine impact on a user's health based on their behavior related to prescribed medication. The method may entail utilizing data related to a medication regimen and patient behavior to determine a patient's compliance to the regimen in terms of dosage and time. These values may be utilized to calculate a medical adherence value representing a patient's adherence to a prescribed regimen. Responsive to determining low medical adherence, a notification may be generated which may result in an intervention with the patient.

Abstract: Methods and devices include predicting future glucose and engagement levels for a user by receiving the user's glucose levels collected by a continuous glucose monitoring (CGM) device over a time period, receiving engagement data associated with the user, wherein the engagement data are associated with the user's medication intake, diet, physical activity, laboratory results, and education activity, determining a first glycemia risk index (GRI) value, determining, using a machine learning model and responsive to the user's glucose levels and the engagement data collected over the time period, one or more predictions for future glucose levels for the user including a prediction that a future GRI value is greater than or less than the first GRI value, and determining, using the machine learning model and responsive to the user's engagement data collected over the time period, one or more predictions for future engagement levels.

Abstract: Methods and devices include identifying a plurality of target users for the digital therapeutic based on one or more target parameters, conducting outreach to one or more of the plurality of target users using an outreach medium, identifying an activation mechanism to optimize use of the digital therapeutic, and encouraging an engagement level of the digital therapeutic by one or more of the plurality of target users.

Abstract: Provided are computer implemented method and systems for providing and monitoring patient compliance with a patient healthcare treatment plan. The method includes receiving, from a healthcare provider over a network, application features for generating a patient application including patient instructions for using a medical therapy, and generating an application for a patient. The application includes at least an input for the user to input data for use in evaluating patient compliance with a treatment plan. In addition, the method includes receiving, from the healthcare provider over the network, a prescription for the application for the patient, and activating the application after the patient receives training on use of the application. The method also may include receiving patient compliance data from the application over the network based on the input.

Abstract: Methods and devices include automated coaching for management of glucose states by receiving a user's glucose levels using a continuous glucose monitoring (CGM) device, determining a time in range (TIR) value, determining a TIR state, receiving a glucose variability (GV) value, determining a GV state, determining a starting state based on the TIR state and the GV state, determining that the starting state corresponds to a non-ideal state, generating an optimized pathway to reach an ideal state based on one or more account vectors such as addressing self-management behavior including food, activity, and medication use. The optimized pathway may further be based on computer detection and classification of significant events of interest over time.

Abstract: Methods, computer-readable media, and systems for providing disease management. In one implementation, a system develops a treatment recommendation for a patient based on patient data. The system may receive patient data, execute a basic model of the physiological system of the patient to generate a modified model for the patient based on the patient data, perform a statistical analysis of the patient data to detect data excursions of the parameter values; use the modified model to determine factors causing the data excursions; and use the model to develop a treatment recommendation to ameliorate negative effects of the disease.

Abstract: Methods and devices include identifying a plurality of target users for the digital therapeutic based on one or more target parameters, conducting outreach to one or more of the plurality of target users using an outreach medium, identifying an activation mechanism to optimize use of the digital therapeutic, and encouraging an engagement level of the digital therapeutic by one or more of the plurality of target users.