Systems and methods for using machine learning models to predict risks posed to patients having various respiratory conditions. According to certain aspects, an electronic device may generate a machine learning model using training data indicating various patient and medication data. The electronic device may access a set of real-world patient data and input the real-world patient data into the machine learning model. An output of the machine learning model may indicate a likelihood of the patients needing intervention care, and the electronic device may facilitate outreach efforts to certain patients in an attempt to decrease this likelihood.

Exemplary embodiments are disclosed of systems and methods for proactively preempting/mitigating anxiety-related behaviors and associated issues/events.

A computer-implemented method includes generating an intervention model for a population of users based on engagement data indicating successfulness of prior interventions for each of the population of users. Each prior intervention corresponds to one of multiple engagement channels, and the intervention model includes multiple channel-specific models. The method includes supplying data related to a first user as input to the intervention model to determine multiple channel-specific intervention expectations. Each channel-specific intervention expectation indicates a likelihood that the first user will take action in response to an intervention being executed using the corresponding engagement channel. The method includes determining a likelihood of a gap in care for the first user, and in response to the gap in care likelihood exceeding a minimum threshold, selecting a first intervention according to the channel-specific intervention expectation that has a highest determined value, and scheduling the selected first intervention for execution.

The invention generally relates to clinical monitoring of patient data. Embodiments provide a system comprising a processor for performing a method and/or computer-implemented method. In certain embodiments the method determines an intervention recommendation for a patient and may comprise analysing patient need data and intervention data to determine the intervention recommendation. In other embodiments, the method monitors, and/or facilitates monitoring of, the monitoring of a patient. The method may comprise receiving providing patient data to a monitor via a user interface on a computing device, monitoring activity and/or one or more actions of the monitor on the user interface, analysing monitoring data, and effecting an action based on the analysis of the monitoring data and/or generating a notification suggesting an action and sending the notification to the monitor based on the analysis of the monitoring data.

A method can include receiving, using one or more processors, a first record including a first data associated with a personal identification from a first database, receiving, using the one or more processors, a second record including a second data associated with a user identification from a second database, pairing, using the one or more processors, the first data and the second data based upon a shared data item contained in the first record and the second record, and displaying, using one or more processors, a report based upon the first data and the second data.

A food portioning system may include a food plate body and a scale associated with the food plate body to sense a weight of food carried thereby. The system may also include wireless communications circuitry coupled to the scale, and a mobile wireless communications device associated with a given user. The mobile wireless communications device may be configured to obtain a user-selected food recipe from the given user, obtain user health data associated with the given user, and obtain a desired consumable food weight for the food plate body based upon the user-selected food recipe and the user health data associated with the given user. The mobile wireless communications device may also be configured to wirelessly communicate with the wireless communications circuitry to obtain a sensed consumable food weight, compare the sensed consumable food weight with the desired consumable food weight, and generate a notification based upon the comparing.

The present disclosure relates to integrated systems, methods and apparatuses for assisting individuals in managing acute life-threatening conditions. A system in accordance with the current disclosure may comprise an electronic circuit configured to be attached to a container of a medication and one or more devices in communication with the electronic circuit in a private network. In an aspect, the one or more devices may work in concert to determine the safety level of an individual based on predetermined usage settings. In some aspects, the system may be configured to determine whether a medication would expire before its manufactured expiry date. In another aspect, the system may assist an individual in locating a medication. In a further aspect, the system may determine whether an individual is having an anaphylactic reaction. In some aspects, the system may detect a known allergen and alert the individual.

Embodiments herein relate to ear-wearable devices and systems that can detect a risk of infection in a device wearer. In a first aspect, an ear-wearable infection sensor device is included having a control circuit, a microphone, a sensor package, and an electroacoustic transducer, wherein the electroacoustic transducer is in electrical communication with the control circuit.

The ear-wearable infection sensor device can be configured to analyze data from the sensor package to determine physiological parameters of a device wearer and evaluate the physiological parameters to detect the risk of an infection. Other embodiments are also included herein.

An ear-worn electronic device includes a right ear device comprising a first processor and a left ear device comprising a second processor communicatively coupled to the first processor. A physiologic sensor module comprises one or more physiologic sensors configured to sense at least one physiologic parameter from a wearer. A motion sensor module comprises one or more sensors configured to sense movement of the wearer. The first and second processors are coupled to the physiologic and motion sensor modules. The first and second processors are configured to produce a three-dimensional virtual sound environment comprising relaxing sounds, generate verbal instructions within the three-dimensional virtual sound environment that guide the wearer through a predetermined mental exercise that promotes wearer relaxation, and generate verbal commentary that assesses wearer compliance with the predetermined mental exercise in response to one or both of the sensed movement and the at least one physiologic parameter.

A method of monitoring compliance with an insulin regimen prescribed for a diabetic patient includes receiving continuous glucose monitoring (CGM) data for the patient over a period of time; determining a degree of variability in the CGM data obtained over the period of time; evaluating compliance with the prescribed insulin regimen based at least in part on the degree of variability in the CGM data that is determined; and responsive to the evaluating, causing at least one action to be performed to facilitate a change in patient behavior that increases compliance with the prescribed insulin regimen when compliance is determined to be less than required to optimize therapeutic treatment of diabetes in the diabetic patient.