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.

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: 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, 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: 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: 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 managing a metabolic metric of a user by determining an activity behavior score for the user based on self-monitoring activity inputs and activity performance. A carbohydrate behavior score for the user may be determined based on self-monitoring carbohydrate inputs and carbohydrate performance. A medicine behavior score may be determined based on user consumption of medicine according to a medicine schedule. A user cluster may be identified from a plurality of clusters that are determined based on receiving initial user activity, carbohydrate and medicine behavior scores for a plurality of initial users. The plurality of user clusters may be generated by applying a clustering algorithm to the initial user activity, carbohydrate, medicine behavior scores. A metabolic metric trend may be determined based on the user cluster and used to generate a treatment plan for the user to improve a metabolic metric outcome.

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 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: This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient behavior information, clinical information, or personal information; learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms; identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns; preparing a plan based on the collected information and the identified interventions; and/or presenting the plan to a user or executing the plan.

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

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: This present disclosure relates to systems and methods for providing an Adaptive Analytical Behavioral and Health Assistant. These systems and methods may include collecting one or more of patient behavior information, clinical information, or personal information; learning one or more patterns that cause an event based on the collected information and one or more pattern recognition algorithms; identifying one or more interventions to prevent the event from occurring or to facilitate the event based on the learned patterns; preparing a plan based on the collected information and the identified interventions; and/or presenting the plan to a user or executing the plan.

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 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: 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.