Digital therapeutic systems and methods

A computer-implemented method for deploying a digital therapeutic program, the method comprising:

• determining a likelihood of use of a digital therapeutic for each of a plurality of users, wherein a machine learning model receives a plurality of user characteristics to output the likelihood of use of the digital therapeutic for each of the plurality of users, wherein the digital therapeutic for a given user from the plurality of users communicates with at least one external application associated with the given user, each respective target user electronic device configured to received data from a clinical data server and a user interface server, the user interface server configured to receive and process user inputs, each respective target user electronic device comprising a presentation layer selected from a web browser, application, or messaging interface,
• wherein the digital therapeutic is configured to output a treatment based on:
• initial data comprising current medication, adherence history to prescribed medications, carbohydrate intake, weight, and blood glucose levels;
• and
• a user history of engagement with a respective target user electronic device;
• determining a feedback potential for each of the plurality of users, the feedback potential corresponding to a likelihood of feedback of the digital therapeutic;
• identifying a plurality of target users from the plurality of users for the digital therapeutic based on one or more target parameters comprising the likelihood of use of the digital therapeutic and the feedback potential for each of the plurality of target users;
• determining access to technology for each of the plurality of users;
• determining a technological sophistication for each of the plurality of users;
• identifying the plurality of target users further based on the access to technology for each of the plurality of users, and excluding the plurality of users for whom the access to technology is not sufficient;
• identifying optimal outreach for one or more of the plurality of target users using an automated outreach medium;
• identifying an activation to optimize use of the digital therapeutic using respective target user electronic devices wherein the machine learning model identifies the activation based the user characteristics, past activation, and past characteristics;
• providing medical treatments via the digital therapeutic on respective target user electronic devices, based on importing activity tracking device data for the respective target users, the medical treatments comprising a specific treatment plan;
• receiving an engagement level of the digital therapeutic by one or more of the plurality of target users;
• and
• updating the machine learning model based on the received engagement level.

A system for deploying a digital therapeutic, the system comprising:

• a data storage device storing a machine learning model, wherein the machine learning model is trained using at least one of supervised training or unsupervised training;
• and
• a processor operatively connected to the data storage device and configured to execute the machine learning model for:
• determining a likelihood of use of a digital therapeutic for each of a plurality of users, wherein a machine learning model receives a plurality of user characteristics to output the likelihood of use of the digital therapeutic for each of the plurality of users, wherein the digital therapeutic for a given user from the plurality of users communicates with at least one application associated with the given user, each respective user device configured to received data from a data server and a user interface server, the user interface server configured to receive and process user inputs, each respective user device comprising a presentation layer selected from a web browser, application, or messaging interface,
• wherein the digital therapeutic is configured to output a treatment based on:
• initial data comprising current medication, adherence history to prescribed medications, carbohydrate intake, weight, and blood glucose levels;
• determining a feedback potential for each of the plurality of users, the feedback potential corresponding to a likelihood of feedback of the digital therapeutic;
• identifying a plurality of users from the plurality of users for the digital therapeutic based on one or more target parameters comprising the likelihood of use of the digital therapeutic and the feedback potential for each of the plurality of users;
• determining access to technology for each of the plurality of users;
• identifying the plurality of users further based on the access to technology for each of the plurality of users, and excluding the plurality of users for whom the access to technology is not sufficient;
• identifying optimal outreach for one or more of the plurality of users using an automated outreach medium;
• identifying an activation to optimize use of the digital therapeutic using respective user devices wherein the machine learning model identifies the activation based the user characteristics, past activation, and past characteristics;
• providing medical treatments via the digital therapeutic on respective user devices, based on importing activity tracking device data for the respective users, the medical treatments comprising a specific treatment plan;
• receiving an engagement level of the digital therapeutic by one or more of the plurality of users;
• and
• updating the machine learning model based on the received engagement level.

A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform operations for deploying a digital therapeutic program, the operations comprising:

• determining a likelihood of use of a digital therapeutic for each of a plurality of users, wherein a machine learning model receives a plurality of user characteristics to output the likelihood of use of the digital therapeutic for each of the plurality of users, wherein the digital therapeutic for a given user from the plurality of users communicates with at least one external application associated with the given user, each respective target user electronic device configured to received data from a clinical data server and a user interface server, the user interface server configured to receive and process user inputs, each respective target user electronic device comprising a presentation layer selected from a web browser, application, or messaging interface,
• wherein the digital therapeutic is configured to output a treatment based on:
• initial data comprising current medication, adherence history to prescribed medications, carbohydrate intake, weight, and blood glucose levels;
• and
• a user history of engagement with a respective target user electronic device;
• determining a feedback potential for each of the plurality of users, the feedback potential corresponding to a likelihood of feedback of the digital therapeutic;
• identifying a plurality of target users from the plurality of users for the digital therapeutic based on one or more target parameters comprising the likelihood of use of the digital therapeutic and the feedback potential for each of the plurality of target users;
• determining access to technology for each of the plurality of users;
• determining a technological sophistication for each of the plurality of users;
• identifying the plurality of target users further based on the access to technology for each of the plurality of users, and excluding the plurality of users for whom the access to technology is not sufficient;
• identifying optimal outreach for one or more of the plurality of target users using an automated outreach medium;
• identifying an activation to optimize use of the digital therapeutic using respective target user electronic devices wherein the machine learning model identifies the activation based the user characteristics, past activation, and past characteristics;
• providing medical treatments via the digital therapeutic on respective target user electronic devices, based on importing activity tracking device data for the respective target users, the medical treatments comprising a specific treatment plan;
• receiving an engagement level of the digital therapeutic by one or more of the plurality of target users;
• and
• updating the machine learning model based on the received engagement level.