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 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 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.

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 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 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: 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 apparatus configured to be manipulated by a patient while performing a treatment plan and a server computing device configured to execute an artificial intelligence engine to generate the treatment plan and a billing sequence associated with the treatment plan. The server computing device receives information pertaining to the patient, generates, based on the information, the treatment plan including instructions for the patient to follow, and receives a set of billing procedures associated with the instructions. The set of billing procedures includes rules pertaining to billing codes, timing, constraints, or some combination thereof. The server computing device generates, based on the set of billing procedures, the billing sequence for at least a portion of the instructions. The billing sequence is tailored according to a certain parameter. The server computing device transmits the treatment plan and the billing sequence to a computing device.

Abstract: A method is disclosed for using an artificial intelligence engine to modify resistance of pedals of an exercise device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive measurements as input, and outputting, based on the measurements, a control instruction that causes the exercise device to modify, independently from each other, the resistance of the pedals. While a user performs an exercise using the exercise device, the method includes receiving the measurements from sensors associated with the pedals. The method includes determining, based on the measurements, a quantifiable or qualitative modification to the resistance provided by a pedal of the pedals. The resistance provided by another pedal of the pedals is not modified. The method includes transmitting the control instruction to the exercise device to cause the resistance provided by the pedal to be modified.

Abstract: Systems, program storage devices, and methods for improving data privacy/trust/anonymity/pseudonymity and data value, wherein data related to a Data Subject can be used and stored, while minimizing re-identification risk by unauthorized parties and enabling data related to the Data Subject to be disclosed to an authorized party by granting access only to the data relevant to that authorized party's purpose, time, place, and/or other criterion via the obfuscation of specific data values. The techniques described herein maintain this level of privacy/trust/anonymity/pseudonymity, while empowering Data Subjects, e.g., consumers or customers of such authorized parties, by enabling protection of data at the desired level of engagement with various business entities. The techniques described herein also allow Data Controllers to perform General Data Protection Regulation (GDPR) and Schrems II-compliant (and surveillance-proof) data processing, via the functional separation of heterogeneous data (e.g.

Abstract: A method is disclosed for generating treatment plans for optimizing patient outcome and monetary value amount generated. The method includes receiving a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients, receiving a set of monetary value amounts associated with the set of treatment plans, receiving a set of constraints, where the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans. The method includes generating, by the artificial intelligence engine, optimal treatment plans for a patient, where the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints. Each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated. The method includes transmitting the optimal treatment plans to a computing device.

Abstract: A method for generating, by an artificial intelligence engine, a treatment plan and a billing sequence associated with the treatment plan is disclosed. The method includes receiving information pertaining to a patient. The information includes a medical diagnosis code of the patient. The method includes generating, based on the information, the treatment plan for the patient. The treatment plan includes instructions for the patient to follow. The method includes receiving a set of billing procedures associated with the instructions. The set of billing procedures includes rules pertaining to billing codes, timing, constraints, or some combination thereof. The method includes generating, based on the set of billing procedures, the billing sequence for at least a portion of the instructions. The billing sequence is tailored according to a certain parameter. The method includes transmitting the treatment plan and the billing sequence to a computing device.

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.

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 to execute an artificial intelligence engine to generate the treatment plan and a billing sequence associated with the treatment plan. The server computing device receives information pertaining to the patient, generates, based on the information, the treatment plan including instructions for the patient to follow, and receives a set of billing procedures associated with the instructions. The set of billing procedures includes rules pertaining to billing codes, timing, constraints, or some combination thereof. The server computing device generates, based on the set of billing procedures, the billing sequence for at least a portion of the instructions. The billing sequence is tailored according to a certain parameter. The server computing device transmits the treatment plan and the billing sequence to a computing 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. and a computing device configured to: receive treatment data pertaining to the user who uses the treatment device to perform the treatment plan; identify at least one enhanced component using the treatment data; generate an enhanced environment using the at least one enhanced component and the treatment plan; output at least one aspect of the enhanced environment to at least one of the patient interface and another interface; receive subsequent treatment data pertaining to the user; and selectively modify at least one of the enhanced environment and at least one of the at least one aspect of the treatment plan and any other aspect of the treatment plan using the subsequent treatment data.

Abstract: A method is disclosed for using an artificial intelligence engine to interact with a user of an exercise device during an exercise session. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive data as input, and based on the data, providing an output. While a user performs an exercise using the exercise device, the method includes receiving the data from an input peripheral of a computing device associated with the user. Based on the data being received from the input peripheral, the method includes determining, via the machine learning model, the output to control an aspect of the exercise device.

Abstract: A method is disclosed for using an artificial intelligence engine to perform a control action. The control action is based on one or more measurements from a wearable device. The method includes generating, by the artificial intelligence engine, a machine learning model trained to receive the one or more measurements as input, and outputting, based on the one or more measurements, a control instruction that causes the control action to be performed. The method includes receiving the one or more measurements from the wearable device being worn by a user, determining whether the one or more measurements indicate, during an interval training session, that one or more characteristics of the user are within a desired target zone, and responsive to determining that the one or more measurements indicate the one or more characteristics of the user are not within the desired target zone during the interval training session, performing the control action.