US-20260128147-A1 - 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 COMORBIDITY

US20260128147A1US 20260128147 A1US20260128147 A1US 20260128147A1US-20260128147-A1

Abstract

Systems, methods, and computer-readable media for and improvement rehabilitation infrastructure. For example, a method may include using, inter alia, objective data to optimize 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. In some embodiments, a method may perform AI-driven interventions via optimization of treatment plans in order to support user progression during rehabilitation by performing, based on data signatures related to medically-associated events, selective data injection. Some other embodiments use evidence-based research to enable AI and machine learning to select and modify treatment plans in real-time at scale. Further, the improved rehabilitation infrastructure enables, via pain management, user compliance with treatment plans.

Inventors

DANIEL CHARLES ALLOWAY
Steven Mason

Assignees

ROM TECHNOLOGIES, INC.

Dates

Publication Date: 20260507
Application Date: 20251231

Claims (20)

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; based on the data, determining, using an artificial intelligence engine, a presence of at least one comorbidity associated with the user, wherein the determining comprises: correlating at least two of a pain measurement, a pedal radius, and an indicator of drug utilization, wherein each of the at least two correlations satisfies a threshold correlation level; based on each of the at least two correlations satisfying the threshold correlation level, generating a unique data signature associated with the at least one comorbidity associated with the user; based on the unique data signature associated with the user, generating, using the artificial intelligence engine, a modified treatment plan for the user; and controlling, while the user uses the electromechanical machine and using the modified treatment plan, the electromechanical machine.
2 . The computer-implemented method of claim 1 , further comprising generating the modified treatment plan by modifying a parameter or activity associated with the at least two of the pain measurement, the pedal radius, the measurement of revolutions per minute, and the indicator of drug utilization.
3 . The computer-implemented method of claim 1 , wherein the computing device transmits, using an input peripheral associated with the computing device, the pain measurement input by the user.
4 . 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 at least one comorbidity associated with the user.
5 . The computer-implemented method of claim 1 , wherein the pain measurement comprises a pain level at onset of a treatment session, an average beginning pain for one or more sessions of the treatment plan, a pain level after a final session, an average pain after the one or more sessions, or some combination thereof.
6 . The computer-implemented method of claim 1 , wherein the pedal radius comprises a pedal radius of a final session of the treatment plan, an average pedal radius of one or more sessions of the treatment plan, or both.
7 . The computer-implemented method of claim 1 , wherein the measurement of revolutions per minute comprises an average revolutions per minute associated with one or more sessions of the treatment plan.
8 . The computer-implemented method of claim 1 , wherein the indicator of drug utilization comprises a medication utilization after a final session of the treatment plan, an average medication utilization for one or more sessions of the treatment plan, or both.
9 . The computer-implemented method of claim 1 , wherein the at least one comorbidity comprises a secondary condition comprising hypertension, sepsis, a mental disorder, a mood disorder, diabetes, lung disease, obesity, heart disease, liver disease, kidney disease, vascular disease, arthritis, sleep apnea, osteoarthritis, anemia, stroke, asthma, any other physiological or anatomical disease, or some combination thereof.
10 . The computer-implemented method of claim 1 , wherein the at least one comorbidity comprises a symptom comprising involvement or disorder of any physiological system in the user, any anatomical part of the user, any particular organ of the user, or some combination thereof.
11 . The computer-implemented method of claim 10 , wherein the symptom is associated with a gastrointestinal system or the disorder and wherein such symptoms comprise nausea, vomiting, diarrhea or any combination thereof.
12 . The computer-implemented method of claim 1 , wherein the at least two correlations comprise one or more indicators of drug utilization.
13 . One or more tangible, non-transitory computer-readable media storing computer 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; based on the data, determine, using an artificial intelligence engine, a presence of at least one comorbidity associated with the user, wherein the determining comprises: correlating at least two of a pain measurement, a pedal radius, and an indicator of drug utilization, wherein each of the at least two correlations satisfies a threshold correlation level; based on each of the at least two correlations satisfying the threshold correlation level, generating a unique data signature associated with the at least one comorbidity associated with the user; based on the unique data signature associated with the user, generate, using the artificial intelligence engine, a modified treatment plan for the user; and control, while the user uses the electromechanical machine and using the modified treatment plan, the electromechanical machine.
14 . The one or more computer-readable media of claim 13 , wherein the one or more processing devices generate the modified treatment plan by modifying a parameter or activity associated with the at least two of the pain measurement, the pedal radius, the measurement of revolutions per minute, and the indicator of drug utilization.
15 . The one or more computer-readable media of claim 13 , wherein the computing device transmits, using an input peripheral associated with the computing device, the pain measurement input by the user.
16 . The one or more computer-readable media of claim 13 , wherein the artificial intelligence engine uses one or more trained computer-implemented models to generate the unique data signature associated with the at least one comorbidity associated with the user.
17 . The one or more computer-readable media of claim 13 , wherein the pain measurement comprises a pain level at onset of a treatment session, an average beginning pain for one or more sessions of the treatment plan, a pain level after a final session, an average pain after the one or more sessions, or some combination thereof.
18 . The one or more computer-readable media of claim 13 , wherein the pedal radius comprises a pedal radius of a final session of the treatment plan, an average pedal radius of one or more sessions of the treatment plan, or both.
19 . The one or more computer-readable media of claim 13 , wherein the measurement of revolutions per minute comprises an average revolutions per minute associated with one or more sessions of the treatment plan.
20 . 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 execute 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; based on the data, determine, using an artificial intelligence engine, a presence of at least one comorbidity associated with the user, wherein the determining comprises: correlating at least two of a pain measurement, a pedal radius, and an indicator of drug utilization, wherein each of the at least two correlations satisfies a threshold correlation level; based on each of the at least two correlations satisfying the threshold correlation level, generating a unique data signature associated with the at least one comorbidity associated with the user; based on the unique data signature associated with the user, generate, using the artificial intelligence engine, a modified treatment plan for the user; and control, while the user uses the electromechanical machine and using the modified treatment plan, the electromechanical machine.

Description

CROSS-REFERENCES TO RELATED APPLICATIONS This application is a continuation-in-part and claims priority to and the benefit of U.S. patent application Ser. No. 19/075,257 (Atty. Docket No. 91346-5503), filed Mar. 10, 2025, titled “System and Method for Use of Treatment Device to Reduce Pain Medication Dependency,” which is a continuation of U.S. patent application Ser. No. 17/397,385 (Atty. Docket No. 91346-5502), filed Aug. 9, 2021, titled “System and Method for Use of Treatment Device to Reduce Pain Medication Dependency”, which is a continuation of and claims priority to and the benefit of U.S. patent application Ser. No. 17/147,295 (Atty. Docket No. 91346-5501), filed Jan. 12, 2021, titled “System and Method for Use of Treatment Device to Reduce Pain Medication Dependency”, which is a continuation-in-part of and claims priority to and the benefit of U.S. patent application Ser. No. 17/021,895 (Atty. Docket No. 91346-1410), filed Sep. 15, 2020, titled “Telemedicine for Orthopedic Treatment,” which claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 62/910,232 (Atty. Docket No. 1400), filed Oct. 3, 2019, titled “Telemedicine for Orthopedic Treatment,” the entire disclosures of which are hereby incorporated by reference for all purposes. This application claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 63/820,697 (Atty. Docket No. 91346-80000) filed Jun. 9, 2025, titled “Systems and Methods for Use of Artificial Intelligence (AI) and Machine Learning to Perform AI-Driven Interventions at Scale” and U.S. Provisional Patent Application Ser. No. 63/860,657 (Att. Docket No. 91346-80001) filed Aug. 8, 2025, titled “Systems and Methods for Use of Artificial Intelligence (AI) and Machine Learning to Perform AI-Driven Interventions at Scale,” the entire disclosures of which are hereby incorporated by reference for all purposes. TECHNICAL FIELD This disclosure relates generally to systems and methods for the use of artificial intelligence (AI) and machine learning to perform AI-driven interventions at scale. BACKGROUND Conventional rehabilitation may consist of different providers (e.g., clinicians, therapists (including physical therapists, kineseologists, occupational therapists and the like), other medical professionals, etc.) and assistants treating patients over a certain number of sessions. The treatment plans generated may be highly varied and assigned based on each provider's independent judgment. The data that may be collected may include, without limitation, findings that the provider entered text in a non-standardized and/or unstructured data format that is non-queryable in a database which is not structured for research. Typically, providers findings (which are often written, transcribed, recorded electronically or the like) may be affected by their amount of engagement time with the patient, personal biases resulting from life experiences, educational background, direct interactions with the patient and personality compatibility or opposition, and/or approach to documentation or system for documentation. As a result, multitudes of varying subjective narrations may be generated that often contradict each other and/or erroneous. The result is that objective methods, suitable for comparison and suitable for measuring progress (or regression) are lacking and not practicable, leaving only unique-to-the-person, subjective assessments of a patient. The opioid epidemic refers to the growing number of hospitalizations and deaths caused by people abusing opioids, including prescription drugs, illicit drugs, and analogous drugs. Annually in the United States, approximately 40,000 people die from an accidental overdose of opioids. Opioids, such as morphine, OxyContin, Vicodin, Percocet, codeine, fentanyl, and the like, are drugs that are often used to relieve severe pain (when properly prescribed by a medical professional), but they are also highly addictive drugs which may further cause biochemical changes in users' brains after continued use. Most people suffering from an opioid addiction initially began taking the drugs after they received, from a doctor, a prescription for pain medication (e.g., opioids) to alleviate pain resulting from an injury or a surgery. As patients engage in rehabilitation, their pain levels increase, which often leads to the patients taking more pain medication, even if this is contrary to the prescription. In addition, the increased pain levels may discourage the patients from diligently following their rehabilitation treatment plans. Such noncompliance may slow down the recovery progresses of the patients, leading to patients taking pain medication for even longer time periods. As the length of time (e.g., days, weeks, months, etc.) increases during which patients take their pain medication, the more likely the patients will become addicted to and/or dependent on opioids. For example, patients may become physically depen