Abstract: A computer-implemented system comprising a treatment device, a patient interface, and a processing device is disclosed. The treatment device is configured to be manipulated by a user while the user performs a treatment plan. The patient interface comprises an output device configured to present telemedicine information associated with a telemedicine session. The processing device is configured to receive a treatment plan for a patient; during the telemedicine session, use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the device.

Abstract: A computer-implemented system for a remote examination is disclosed. The computer-implemented system includes a treatment device, a master console, a user interface, and a control system. The treatment device comprises one or more slave sensors and a slave pressure system, the treatment device configured to be manipulated while a patient performs a treatment plan. The master console comprises a master device. The user interface comprises an output device configured to present telemedicine information associated with a telemedicine session. The control system comprises one or more processing devices operatively coupled to the master console and the treatment device.

Abstract: A computer-implemented system for processing medical claims is disclosed. The system includes a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor. During the telemedicine session, the processor is configured to receive information from a medical device. Using the device-generated information, the processor is further configured to determine device-based medical coding information. The processor is further configured to transmit the device-based medical coding information to a claim adjudication server.

Abstract: A method includes receiving data pertaining to a user that uses a treatment apparatus to perform a treatment plan. The data includes characteristics of the user, the treatment plan, and a result of the treatment plan. The method includes assigning the user to a cohort representing people having similarities to the characteristics of the user. The method includes receiving second data pertaining to a second user, the second data comprises characteristics of the second user. The method includes determining whether at least some of the characteristics of the second user match with at least some of the characteristics of the user, assigning the second user to the first cohort, and selecting, via a trained machine learning model, the treatment plan for the second user, and controlling, based on the treatment plan, the treatment apparatus while the second user uses the treatment apparatus.

Abstract: A computer-implemented system includes an electromechanical device configured to be manipulated by a patient while performing an exercise session, and a processor in communication with the electromechanical device. The processor is configured to receive data, generate a map packet, and transmit the map packet. The processor is configured to use the data to generate continuity packets, where each of the continuity packets includes a contiguous portion of the data, and transmit the continuity packets. The processor is configured to use the map packet and the continuity packets to cause an output file to be generated.

Abstract: A computer-implemented system includes a treatment device configured to be manipulated by an individual while the individual performs a treatment plan, a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session, and a computing device configured to: identify two or more types of healthcare providers to participate during the telemedicine session in the treatment plan associated with the individual; identify at least one healthcare provider associated with each of the two or more types of the healthcare providers; identify, based on schedules of the identified at least one healthcare provider, one or more available time slots for each respective healthcare provider; identify a first time slot of the available time slots; generate, based on the first time slot, a schedule event; and communicate the schedule event to each of the identified healthcare providers.

Abstract: Systems, methods, and computer-readable mediums for operating an electromechanical device are disclosed. The system includes, in one example, the electromechanical device, a patient portal, and a computing device. The computing device is configured to receive user data relating to a user, and receive treatment data relating to treatment plans and outcomes. The computing device is also configured to generate a prehabilitation plan by using a machine learning model to process the user data and the treatment data. The computing device is further configured to select, for the electromechanical device, an electromechanical device configuration that enables exercises of the prehabilitation plan to be performed by the user such that performance improves an area of the user's body. The computing device is also configured to enable the electromechanical device to implement the electromechanical device configuration.

Abstract: A computer-implemented system comprising a treatment apparatus, a patient interface, and a processing device is disclosed. The processing device is configured to receive treatment data pertaining to the user during the telemedicine session, wherein the treatment data comprises one or more characteristics of the user; determine, via one or more trained machine learning models, at least one respective measure of benefit one or more exercise regimens provide the user, wherein the determining the respective measure of benefit is based on the treatment data; determine, via the one or more trained machine learning models, one or more probabilities of the user complying with the one or more exercise regimens; and transmit the treatment plan to a computing device, wherein the treatment plan is generated based on the one or more probabilities and the respective measure of benefit the one or more exercise regimens provide the user.

Abstract: A system includes a treatment device configured to be manipulated by a user while the user performs a treatment plan and a patient interface associated with the treatment device. The system also includes a computing device configured to: receive treatment data pertaining to the user who uses the treatment device to perform the treatment plan; write to an associated memory, for access at a computing device of a healthcare provider, treatment information; communicate with an interface, at the computing device of the healthcare provider; and modify at least one of the at least one aspect and any other aspect of the treatment plan in response to receiving treatment plan input including at least one modification to the at least one of the at least one aspect and any other aspect of the treatment plan.

Abstract: A method includes receiving user data for a user capable of operating an electromechanical device. The user data comprises health history data related to one or more health indicators of the user. The method further includes generating a health improvement plan by using a machine learning model to process the user data. The health improvement plan includes an exercise session to be performed on the electromechanical device. The method further includes providing the health improvement plan to one or more user portals. The method further includes selecting, for the electromechanical device, a device configuration that corresponds to the health improvement plan. The device configuration includes mode data related to one or more modes the electromechanical device is capable of operating during the exercise session. The method further includes providing the device configuration to the electromechanical device such that the device configuration may be implemented on the electromechanical device.

Abstract: A computer-implemented system comprising a treatment device, a patient interface, and a processing device is disclosed. The treatment device is configured to be manipulated by a user while the user performs a treatment plan. The patient interface comprises an output device configured to present telemedicine information associated with a telemedicine session. The processing device is configured to receive a treatment plan for a patient; during the telemedicine session, use the treatment plan to generate at least one parameter; and responsive to at least one trigger condition occurring, control at least one operation of the device.

Abstract: A computer-implemented system includes a treatment device configured to be manipulated by a user while the user performs a treatment plan, a patient interface, and a computing device configured to: receive treatment; write to an associated memory, configured to be accessed by an artificial intelligence engine, treatment data, the artificial intelligence engine being configured to use at least one machine learning model to, using the treatment data, generate at least one of a treatment scheduling output prediction and an appointment output; receive, from the artificial intelligence engine, the at least one of the treatment scheduling output prediction and the appointment output; and selectively modify, using the at least one of the treatment scheduling output prediction and the appointment output, the at least one aspect of the treatment plan.

Abstract: A system for rehabilitation is disclosed. The system for rehabilitation includes one or more electronic devices comprising one or more memory devices storing instructions, one or more network interface cards, and one or more sensors, wherein the one or more electronic devices are coupled to a user. The system for rehabilitation further includes one or more processing devices operatively coupled to the one or more memory devices, the one or more network interface cards, and the one or more sensors. The one or more processing devices are configured to execute the instructions to receive information from the one or more sensors. The one or more processing devices are further configured to execute the instructions to transmit the information to a computing device controlling an electromechanical device, via the one or more network interface cards.

Abstract: A system for rehabilitation is disclosed. The system for rehabilitation includes one or more electronic devices comprising one or more memory devices storing instructions, one or more network interface cards, and one or more sensors, wherein the one or more electronic devices are coupled to a user. The system for rehabilitation further includes one or more processing devices operatively coupled to the one or more memory devices, the one or more network interface cards, and the one or more sensors. The one or more processing devices are configured to execute the instructions to receive information from the one or more sensors. The one or more processing devices are further configured to execute the instructions to transmit the information to a computing device controlling an electromechanical device, via the one or more network interface cards.

Abstract: A computer-implemented system includes a treatment apparatus configured to be manipulated by a patient while performing a treatment plan, and a server computing device configured. The server computing device receives treatment plans that, when applied to patients, cause outcomes to be achieved by the patients, receives monetary value amounts associated with the treatment plans, where a respective monetary value amount of the monetary value amounts is associated with a respective treatment plan of the treatment plans. The server computing device receives constraints including rules pertaining to billing codes associated with the treatment plans. An artificial intelligence engine generates optimal treatment plans for a patient based on the treatment plans, the monetary value amounts, and the constraints, wherein each of the optimal treatment plans complies with the constraints and represents a patient outcome and an associated monetary value amount generated.

Abstract: A computer-implemented system for a remote examination is disclosed. The computer-implemented system includes a treatment device, a master console, a user interface, and a control system. The treatment device comprises one or more slave sensors and a slave pressure system, the treatment device configured to be manipulated while a patient performs a treatment plan. The master console comprises a master device. The user interface comprises an output device configured to present telemedicine information associated with a telemedicine session. The control system comprises one or more processing devices operatively coupled to the master console and the treatment device.

Abstract: A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan, a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session, and a first computing device configured to: receive treatment data pertaining to the user while the user uses the treatment device to perform the treatment plan; identify at least one aspect of the at least one measurement pertaining to the user associated with a first treatment device mode of the treatment device; determine whether the at least one aspect of the measurement correlates with a secondary condition of the user; and, in response to a determination that the at least one aspect of the at least one measurement is correlated with the at least one secondary condition of the user, generate secondary condition information indicating at least the secondary condition.

Abstract: A computer-implemented system includes a treatment device configured to be manipulated by a user while the user performs a treatment plan, a patient interface, and a computing device configured to: receive treatment; write to an associated memory, configured to be accessed by an artificial intelligence engine, treatment data, the artificial intelligence engine being configured to use at least one machine learning model to, using the treatment data, generate at least one of a treatment scheduling output prediction and an appointment output; receive, from the artificial intelligence engine, the at least one of the treatment scheduling output prediction and the appointment output; and selectively modify, using the at least one of the treatment scheduling output prediction and the appointment output, the at least one aspect of the treatment plan.

Abstract: In one embodiment, a computer-implemented system includes treatment apparatuses configured to be manipulated by patients while performing an exercise session, patient interfaces associated with the plurality of patients, and a server computing device configured to receive first characteristics pertaining to the patients, and initiate a virtual shared session on the patient interfaces associated with the patients. The virtual shared session includes at least a set of multimedia feeds, and each multimedia feed of the set of multimedia feeds is associated with one or more of the patients. During the virtual shared session, the server computing device may present a first layout including the set of multimedia feeds, the first characteristics, or some combination thereof.