Click Therapeutics, Inc.

A method that includes receiving patient-generated event data over a network from a patient device associated with a patient having an active digital therapy prescription for treating an underlying disease or disorder. The patient-generated event data is encrypted by the patient device and includes at least one timestamped event related to the active digital therapy prescription. In response to receiving the patient-generated event data, the method includes decrypting, anonymizing, and storing the anonymized patient-generated event data on memory hardware. The method further includes receiving a patient record request over the network from a healthcare provider (HCP) system that requests the patient-generated event data and includes an authentication token. In response to receiving the patient record request, the method includes retrieving and encrypting the anonymized patient-generated event data from the memory hardware using the authentication token. The method also includes transmitting the encrypted patient-generated event data to the HCP system.

Provided herein are systems and methods for methods for detecting expected reductions of interactions across sessions. A computing system can identify event data identifying interactions by a user with a first session for digital therapeutic content to address a condition. The computing system can apply the event data to a machine learning (ML) model. The computing system can generate a likelihood of the user interacting with a second session for digital therapeutic content based on applying the event data to the ML model. The computing system can detect an expected reduction in interactions by the user with the second session, responsive to the likelihood of the user not satisfying a threshold. The computing system can provide an output for the user based on detecting the expected reduction in interactions. In this manner, the efficacy of the medication that user is concurrently taking to address the condition may be improved.

Presented herein are systems and methods for managing data structures for remote monitoring and verification of user devices. A computing system may maintain a data structure including an activity field and a verification field, and determine that activity data generated responsive to the user performing an activity via the application satisfies an activity condition. The computing system may update the data structure to include an activity value and receive verification data. The computing system may update the data structure to include a verification value and execute an operation to update the profile using a token based on the activity value and the verification value. The computing system may improve the efficacy of a medication to address a condition in the user.

Aspects of the present disclosure are directed to systems, methods, and computer readable media for configuring generation of digital therapeutic content for provision. A computing system may identify content to be provided via a network. The computing system may apply the content to a machine learning (ML) model to generate an output. The computing system may determine, from the output, a compliance status of the content. The computing system may identify, based on applying the ML model, at least a subsection of the content to be modified. The computing system may identify the content to be modified responsive to determining the compliance status of the content.

Provided herein are systems and methods for selecting a weight loss medication using a machine learning model to address obesity. A computing system may receive physiological measurements of a user. The computing system may apply the measurements to a machine learning model. The machine learning model may be trained using a plurality of examples, each example comprising sample physiological measurements of a sample user and a corresponding sample weight loss medication administered to the sample user. The computing system may generate based on applying the one or more physiological measurements to the machine learning model, a metric indicating an expected outcome associated with a weight loss medication for the user. The computing system may provide to a user device associated with the user, a message indicating the expected outcome associated with the weight loss medication. The computing system can improve efficacy of the medication in addressing the obesity.

Provided herein are systems and methods for dynamically providing messages based on indications of states to users at risk of migraines. A server can maintain, on a database, an identification of a set of activities for users at risk of migraines, with each activity having a set of messages to be presented in accordance with a rule. The server can receive, via a user interface, an indication identifying a state of the user associated with the migraine. The server can select, based on the state of the user identified in the response, an activity to provide to the user for the session. The server can identify, in accordance with the rule for the activity, a message from the set of messages. The server can cause the message identifying the activity to be presented via the user interface.

Provided herein are systems and methods for communicating with user devices over networked environments. A server may provide, to a user device over a communication network, an instruction to present a graphical user interface (GUI) on a user device to prompt a user to input values for event parameters for events on the user device according to a data protocol. The server may receive, from the user device, a plurality of values for a plurality of event parameters The server may determine, based on inputting via the GUI, a metric. The server may apply the plurality of values and the metric into a machine learning model to generate a likelihood to satisfy a target associated with the data protocol. The server may, responsive to the likelihood not satisfying a threshold, transmit a subsequent instruction.

Provided herein are systems and methods for communicating with user devices over networked environments. A server may provide, to a user device over a communication network, an instruction to present a graphical user interface (GUI) on a user device to prompt a user to input values for event parameters for events on the user device according to a data protocol. The server may receive, from the user device, a plurality of values for a plurality of event parameters The server may determine, based on inputting via the GUI, a metric. The server may apply the plurality of values and the metric into a machine learning model to generate a likelihood to satisfy a target associated with the data protocol. The server may, responsive to the likelihood not satisfying a threshold, transmit a subsequent instruction.

Aspects of the present disclosure are directed to systems, methods, and computer readable media for generating messages targeted to address conditions in users. A computing system may identify (i) a condition of a user to be addressed and (ii) one or more parameters defining messages to be presented via an application towards achieving an endpoint associated with the condition for the user. The computing system may generate a prompt using the condition and the one or more parameters in accordance with a template. The computing system may apply the prompt to a generative model to output a message identifying at least one activity toward achieving the endpoint. The generative model may be trained using a corpus. The computing system may provide, the message for presentation to prompt the user to perform the activity via the application towards achieving the endpoint to address the condition.