SYSTEMS AND METHODS FOR OPTIMIZING TREATMENT PLANS TO SUPPORT USER PROGRESSION DURING REHABILITATION FOR THE PURPOSE OF ASSISTING IN DETERMINING AI-DRIVEN INTERVENTIONS BY USING MACHINE LEARNING TO GENERATE AT LEAST ONE DATA SIGNATURE ASSOCIATED WITH A TREATMENT GAP

1 . A computer-implemented method comprising:

receiving, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan;

correlating, using an artificial intelligence engine, at least two of a pain measurement, an indication of medication utilization, and a measurement of revolutions per minute to generate a unique data signature associated with a treatment gap associated with the treatment plan performed by the user, wherein the at least two of the pain measurement, the indication of medication utilization, and the measurement of revolutions per minute each satisfies a respective threshold level;

based on the unique data signature associated with the treatment gap associated with the treatment plan performed by the user, generating, using the artificial intelligence engine, one or more alternative treatment plans for the user;

based on a threshold compliance level associated with each of the one or more alternative treatment plans, selecting an alternative treatment plan from the one or more alternative treatment plans; and

controlling, while the user uses the electromechanical machine and based on the alternative treatment plan, the electromechanical machine.

2 . The computer-implemented method of claim 1 , wherein the artificial intelligence engine selects the alternative treatment plan based on one or more available resources, psychographics of the user, demographics of the user, geographic data associated with the user, or some combination thereof.

3 . The computer-implemented method of claim 1 , wherein the treatment gap represents a difference between what is prescribed in the treatment plan and a characteristic of the user, wherein the characteristic comprises performance, physical condition, medical condition or both.

4 . The computer-implemented method of claim 1 , wherein the computing device transmits, using an input peripheral of the computing device, the pain measurement that is input by the user.

5 . The computer-implemented method of claim 1 , wherein the artificial intelligence engine uses one or more trained computer-implemented models to generate the unique data signature associated with the treatment gap.

6 . The computer-implemented method of claim 1 , wherein the pain measurement comprises pain after a final session of the treatment plan, average pain after all of one or more sessions of the treatment plan, or some combination thereof.

7 . The computer-implemented method of claim 1 , further comprising converting a format of the data to a standardized or canonical format and generating training data from the converted data, wherein the training data is used to train one or more computer-implemented models executed by the artificial intelligence engine.

8 . The computer-implemented method of claim 1 , wherein the measurement of revolutions per minute comprises an average revolutions per minute of one or more sessions of the treatment plan.

9 . The computer-implemented method of claim 1 , further comprising, based on the unique data signature associated with the treatment gap, initiating a telehealth session between the computing device and a second computing device associated with a second user.

10 . A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause one or more processing devices to:

receive, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan;

correlate, using an artificial intelligence engine, at least two of a pain measurement, an indication of medication utilization, and a measurement of revolutions per minute to generate a unique data signature associated with a treatment gap associated with the treatment plan performed by the user, wherein the at least two of the pain measurement, the indication of medication utilization, and the measurement of revolutions per minute each satisfies a respective threshold level;

based on the unique data signature associated with the treatment gap associated with the treatment plan performed by the user, generate, using the artificial intelligence engine, one or more alternative treatment plans for the user;

based on a threshold compliance level associated with each of the one or more alternative treatment plans, select an alternative treatment plan from the one or more alternative treatment plans; and

control, while the user uses the electromechanical machine and based on the alternative treatment plan, the electromechanical machine.

11 . The computer-readable medium of claim 10 , wherein the artificial intelligence engine selects the alternative treatment plan based on one or more available resources, psychographics of the user, demographics of the user, geographic data associated with the user, or some combination thereof.

12 . The computer-readable medium of claim 10 , wherein the treatment gap represents a difference between what is prescribed in the treatment plan and a characteristic of the user, wherein the characteristic comprises performance, physical condition, medical condition or both.

13 . The computer-readable medium of claim 10 , wherein the computing device transmits, using an input peripheral of the computing device, the pain measurement that is input by the user.

14 . The computer-readable medium of claim 10 , wherein the artificial intelligence engine uses one or more trained computer-implemented models to generate the unique data signature associated with the treatment gap.

15 . The computer-readable medium of claim 10 , wherein the pain measurement comprises pain after a final session of the treatment plan, average pain after all of one or more sessions of the treatment plan, or some combination thereof.

16 . The computer-readable medium of claim 10 , further comprising converting a format of the data to a standardized or canonical format and generating training data from the converted data, wherein the training data is used to train one or more computer-implemented models executed by the artificial intelligence engine.

17 . The computer-readable medium of claim 10 , wherein the measurement of revolutions per minute comprises an average revolutions per minute of one or more sessions of the treatment plan.

18 . The computer-readable medium of claim 10 , wherein, based on the unique data signature associated with the treatment gap, the one or more processing devices initiates a telehealth session between the computing device and a second computing device associated with a second user.

19 . A system comprising:

one or more memory devices storing instructions; and

one or more processing devices communicatively coupled to the one or more memory devices, wherein the one or more processing devices executes the instructions to:

receive, from one or more of an electromechanical machine, a sensor, and a computing device, data associated with a user that uses the electromechanical machine to perform a treatment plan;

correlate, using an artificial intelligence engine, at least two of a pain measurement, an indication of medication utilization, and a measurement of revolutions per minute to generate a unique data signature associated with a treatment gap associated with the treatment plan performed by the user, wherein the at least two of the pain measurement, the indication of medication utilization, and the measurement of revolutions per minute each satisfies a respective threshold level;

based on the unique data signature associated with the treatment gap associated with the treatment plan performed by the user, generate, using the artificial intelligence engine, one or more alternative treatment plans for the user;

based on a threshold compliance level associated with each of the one or more alternative treatment plans, select an alternative treatment plan from the one or more alternative treatment plans; and

control, while the user uses the electromechanical machine and based on the alternative treatment plan, the electromechanical machine.

20 . The system of claim 19 , wherein the artificial intelligence engine selects the alternative treatment plan based on one or more available resources, psychographics of the user, demographics of the user, geographic data associated with the user, or some combination thereof.