Digital therapeutic systems and methods

1 . 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 each respective target user electronic device is configured to receive data from a clinical data server and a user interface server, the user interface server configured to receive and process user inputs,

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 access to technology for each of the plurality of users;

determining a technological sophistication for each of the plurality of users;

identifying a plurality of target users 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, wherein determining the plurality of target users includes determining whether each of the plurality of users is likely to use the digital therapeutic;

identifying optimal outreach for one or more of the plurality of target users using an outreach medium, wherein the optimal outreach is provided to a highest awareness output selected from one or more of a service output, a methodology output, an individual output, a group output, or an entity output, wherein the outreach is identified based on an outreach factor selected from one or more of an outreach method, an outreach modality, an outreach frequency, an outreach time, or an outreach level of interaction the outreach medium being conducted based on a scalability potential, wherein the outreach frequency is based on a balance ratio determined based on an amount of outreach over an outreach time period, the balance ratio output by the machine learning model to prevent oversaturation;

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 on the user characteristics, past activation, and past characteristics;

providing medical treatments via the digital therapeutic on respective target user electronic devices, wherein the medical treatments include at least one of an auditory treatment, visual treatment, olfactory treatment, or haptic treatment output by one or more sensors, and further include a message received on a mobile device;

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 and further by a training component based on a comparison result of the plurality of target users to a number of users reached via the optimal outreach.

2 . The computer-implemented method of claim 1 , further comprising generating a report based on one or more of the plurality of target users, the activation, or activating the digital therapeutic.

3 . The computer-implemented method of claim 2 , wherein the report is based on one or more of an informative analysis, discovery analysis, extrapolative analysis, or an adaptive analysis.

4 . The computer-implemented method of claim 2 , wherein the report comprises a comparison of an N+1 stage score to an N stage score for each stage except a final stage.

5 . The computer-implemented method of claim 1 , wherein the activation is based on one or more of a modality, data-enablement verses data-entry, or a location.

6 . The computer-implemented method of claim 1 , wherein the engagement level is based on one or more of an in-solution versus out-of-solution, a frequency, a length, or a modality, wherein the in-solution corresponds to the digital therapeutic and the out-of-solution is external to the digital therapeutic.

7 . The computer-implemented method of claim 1 , wherein at least one of the identifying the plurality of target users, identifying an activation, and activating the activation is based on an output of a machine learning model.

8 . The computer-implemented method of claim 7 , wherein the machine learning model is trained using training data that comprises one or more of stage inputs, known outcomes, and comparison results.

9 . The computer-implemented method of claim 8 , wherein the comparison results are a ratio of an N+1 stage score to an N stage score for each stage except a final stage.

10 . 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 each respective user device is configured to received data from a data server and a user interface server, the user interface server configured to receive and process user inputs,

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 access to technology for each of the plurality of users;

identifying a plurality of target users 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, wherein determining the plurality of target users includes determining whether each of the plurality of users is likely to use the digital therapeutic;

identifying optimal outreach for one or more of the plurality of target users using an outreach medium, wherein the optimal outreach is provided to a highest awareness output selected from one or more of a service output, a methodology output, an individual output, a group output, or an entity output, wherein the outreach is identified based on an outreach factor selected from one or more of an outreach method, an outreach modality, an outreach frequency, an outreach time, or an outreach level of interaction the outreach medium being conducted based on a scalability potential, wherein the outreach frequency is based on a balance ratio determined based on an amount of outreach over an outreach time period, the balance ratio output by the machine learning model to prevent oversaturation;

identifying an activation to optimize use of the digital therapeutic using respective target user devices wherein the machine learning model identifies the activation based on the user characteristics, past activation, and past characteristics;

providing medical treatments via the digital therapeutic on respective target user devices, wherein the medical treatments include at least one of an auditory treatment, visual treatment, olfactory treatment, or haptic treatment output by one or more sensors, and further include a message received on a mobile device;

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 and further by a training component based on a comparison result of the plurality of target users to a number of users reached via the optimal outreach.

11 . The system of claim 10 , further comprising identifying the plurality of target users further based on one or more target parameters comprising the likelihood of use of the digital therapeutic, and wherein the one or more target parameters is identified based on one or more of attributes of the plurality of users or attributes of the digital therapeutic.

12 . The system of claim 10 , further comprising identifying the plurality of target users further based on one or more target parameters comprising the likelihood of use of the digital therapeutic, and wherein the one or more target parameters comprises a market segment based factor, wherein the market segment based factor is selected from one or more of a private insurance, a commercial insurance, Medicare, Medicaid, or a concierge coverage.

13 . The system of claim 10 , further comprising identifying the plurality of target users further based on one or more target parameters comprising the likelihood of use of the digital therapeutic, and generating a report based on one or more of cost metrics, effectiveness metrics, time metrics, individual cost metrics, population based cost metrics, individual effectiveness metrics, population based effectiveness metrics, individual time metrics, population based time metrics, a feedback potential, or target parameters.

14 . The system of claim 10 , wherein the machine learning model is further trained by modifying one of one or more weights or one or more layers based on training data.

15 . 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 each respective target user electronic device is 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,

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 access to technology for each of the plurality of users;

determining a technological sophistication for each of the plurality of users;

identifying a plurality of target users 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, wherein determining the plurality of target users includes determining whether each of the plurality of users is likely to use the digital therapeutic;

identifying optimal outreach for one or more of the plurality of target users using an outreach medium, wherein the optimal outreach is provided to a highest awareness output selected from one or more of a service output, a methodology output, an individual output, a group output, or an entity output, wherein the outreach is identified based on an outreach factor selected from one or more of an outreach method, an outreach modality, an outreach frequency, an outreach time, or an outreach level of interaction the outreach medium being conducted based on a scalability potential, wherein the outreach frequency is based on a balance ratio determined based on an amount of outreach over an outreach time period, the balance ratio output by the machine learning model to prevent oversaturation;

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 on the user characteristics, past activation, and past characteristics;

providing medical treatments via the digital therapeutic on respective target user electronic devices, wherein the medical treatments include at least one of an auditory treatment, visual treatment, olfactory treatment, or haptic treatment output by one or more sensors, and further include a message received on a mobile device;

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 and further by a training component based on a comparison result of the plurality of target users to a number of users reached via the optimal outreach.

16 . The non-transitory computer-readable medium of claim 15 , further comprising generating a report based on one or more of the plurality of target users, the activation, or activating the digital therapeutic.

17 . The non-transitory computer-readable medium of claim 16 , wherein the report comprises a comparison of an N+1 stage score to an N stage score for each stage except a final stage.