A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

An implantable medical device is disclosed. A housing includes a hollow body forming a first electrode on an outer surface with end caps affixed to opposite ends, one end cap forming a second electrode. A microcontroller circuit is provided and includes a microcontroller operable under program instructions stored within a non-volatile memory device. An analog front end is interfaced to the electrodes to sense electrocardiographic signals. A transceiver circuit is operable to wirelessly communicate with an external data device. The program instructions define instructions to continuously sample the electrocardiographic signals into the non-volatile memory device and to offload the non-volatile memory device to the external data device. A receiving coil and a charging circuit are operable to charge an onboard power source for the microcontroller circuit.

An implantable medical device is disclosed. A housing includes a hollow body forming a first electrode on an outer surface with end caps affixed to opposite ends, one end cap forming a second electrode. A microcontroller circuit is provided and includes a microcontroller operable under program instructions stored within a non-volatile memory device. An analog front end is interfaced to the electrodes to sense electrocardiographic signals. A transceiver circuit is operable to wirelessly communicate with an external data device. The program instructions define instructions to continuously sample the electrocardiographic signals into the non-volatile memory device and to offload the non-volatile memory device to the external data device. A receiving coil and a charging circuit are operable to charge an onboard power source for the microcontroller circuit.

Systems and methods are disclosed herein for switching an application executing on an ambulatory medical device to a new application without interrupting therapy provided by the ambulatory medical device to a subject. The ambulatory medical device may receive an indication that an update to an application executing on the ambulatory insulin pump is available, establish a communication connection to a host computing system, download and install the application update, while a prior version of the application continues to run. The disclosed systems and methods can confirm successful installation of the application update on the ambulatory medical device and switch control of the ambulatory medical device from the prior version to the new version of the application without interrupting therapy provided to the subject.

Systems and methods are disclosed herein for switching an application executing on an ambulatory medical device to a new application without interrupting therapy provided by the ambulatory medical device to a subject. The ambulatory medical device may receive an indication that an update to an application executing on the ambulatory insulin pump is available, establish a communication connection to a host computing system, download and install the application update, while a prior version of the application continues to run. The disclosed systems and methods can confirm successful installation of the application update on the ambulatory medical device and switch control of the ambulatory medical device from the prior version to the new version of the application without interrupting therapy provided to the subject.

A system for cryptographically secured outputs from telemedicine sessions includes a computing device at a first location, the computing device configured to initiate a secure communication interface between the computing device and a client device associated with a human subject and at a second location, receive, from at least a remote sensor at the second location, a plurality of current biological data associated with the human subject, input, using the secure communication interface, an identifier of a biochemical element, determine, as a function of the plurality of current biological data, a tolerability of the biochemical element, and generate a digitally signed authorization datum as a function of the determination.

A system for cryptographically secured outputs from telemedicine sessions includes a computing device at a first location, the computing device configured to initiate a secure communication interface between the computing device and a client device associated with a human subject and at a second location, receive, from at least a remote sensor at the second location, a plurality of current biological data associated with the human subject, input, using the secure communication interface, an identifier of a biochemical element, determine, as a function of the plurality of current biological data, a tolerability of the biochemical element, and generate a digitally signed authorization datum as a function of the determination.

A method for training and using a machine-learning model to determine an execution date for an instruction and to determine an associated action item. A machine-learning model can be trained by receiving entity data that includes historical prescription data, by generating training data by labeling data in the entity data, and by training the machine-learning model by mapping the labeled data to possible predictions for subsequent prescription executions for the entity. Data, which includes at least a prescription and a previous execution date can be received. A subsequent execution date and an associated action item can be determined. A prescription can be executed on the subsequent execution date. The action item can be executed before the subsequent execution date.

A method for training and using a machine-learning model to determine an execution date for an instruction and to determine an associated action item. A machine-learning model can be trained by receiving entity data that includes historical prescription data, by generating training data by labeling data in the entity data, and by training the machine-learning model by mapping the labeled data to possible predictions for subsequent prescription executions for the entity. Data, which includes at least a prescription and a previous execution date can be received. A subsequent execution date and an associated action item can be determined. A prescription can be executed on the subsequent execution date. The action item can be executed before the subsequent execution date.