Patent Issued for Digital therapeutic systems and methods (USPTO 11735300): WellDoc Inc.

2023 SEP 08 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Hutchins, Carey (Columbia, MD, US), Iyer, Anand (Columbia, MD, US), Peeples, Malinda (Columbia, MD, US), Shenoy, Vinayak (Columbia, MD, US), filed on November 10, 2022, was published online on August 22, 2023.

The assignee for this patent, patent number 11735300, is WellDoc Inc. (Columbia, Maryland, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Increased healthcare costs have limited patient access to appropriate care. At the same time, healthcare companies have increased provider workloads and limited physician-patient interactions. Digital therapeutics can offer a reduction in cost and a novel treatment implementation. However, digital therapeutics have yet to achieve critical mass due to a lack of a standardized value chain, lack of key processes, lack of metrics, and lack of best practices and benchmarking.

“The present disclosure is directed to addressing one or more of the above-referenced challenges. The introduction provided herein is for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art, or suggestions of the prior art, by inclusion in this section.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “This disclosure is directed to a computer-implemented method for deploying a digital therapeutic including identifying a plurality of target users for the digital therapeutic based on one or more target parameters, conducting outreach to one or more of the plurality of target users using an outreach medium, identifying an activation mechanism to optimize use of the digital therapeutic, and encouraging an engagement level of the digital therapeutic by one or more of the plurality of target users.

“Techniques disclosed include generating a report based on one or more of the target users, the outreach, the activation mechanism, or the engagement level. The report may be based on one or more of an informative analysis, discovery analysis, extrapolative analysis, or an adaptive analysis. The report may include a comparison of an N+1 stage score to an N stage score.

“The plurality of target users are identified based on one or more of clinical factors, disease factors, technology factors, social factors, or demographic factors. The outreach is conducted based on one or more of a method, a modality, a frequency, a time, or a level of interaction. The activation mechanism is based on one or more of a modality, data-enablement verses data-entry, or a location. The engagement level is based on one or more of an in-solution versus out-of-solution, a frequency, a length, and a modality. At least one of the identifying the plurality of target users, conducting outreach, identifying an activation mechanism, and encouraging an engagement level is based on an output of a machine learning model. The machine learning model is trained my modifying one of one or more weights or one or more layers based on training data. The training data comprises one or more of stage inputs, known outcomes, and comparison results. The comparison results are a ratio of an N+1 stage score to an N stage score.”

The claims supplied by the inventors are:

“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 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.

“2. The computer-implemented method of claim 1, further comprising generating a report based on one or more of the target users, the outreach, 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, conducting outreach, 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 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.

“11. The system of claim 10, wherein the one or more target parameter is identified based on one or more of attributes of the users or attributes of the digital therapeutic.

“12. The system of claim 10, 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 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, the feedback potential, the target parameters, or the outreach.

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

There are additional claims. Please visit full patent to read further.

For more information, see this patent: Hutchins, Carey. Digital therapeutic systems and methods. U.S. Patent Number 11735300, filed November 10, 2022, and published online on August 22, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11735300)&db=USPAT&type=ids

(Our reports deliver fact-based news of research and discoveries from around the world.)