Abstract: Provided herein are systems, methods, and apparatuses for providing a real-time global bio-surveillance and response solution. The systems and apparatuses may include one or more sentinel subsystems and one or more surge subsystems that each include a real-time, cloud-based, distributed, interconnected set of hardware, software, and/or firmware for comprehensive surveillance, complete detection, and immediate response various to biological, chemical, and/or bio-chemical anomalies, for example, a new or emerging pathogen or other health condition.

Abstract: A cloud-based, query management and education system enables provider organizations to query and educate clinicians on how to clarify clinical documentation, resulting in more accurate reimbursement, public reporting, research and policy decisions. Query authors create compliant queries from stored templates which may be supplemented by attaching and/or annotating selected documentation from the patient record. Clinician responses to such queries automatically generate addenda in the relevant patient record. All events around a query are tracked and reportable through a graphical performance scorecards and reporting dashboards.

Abstract: A clinical documentation improvement (CDI) smart scoring method may include predicting, via per-condition diagnosis machine learning (ML) models and based on clinical evidence received by a system, a probability that a medical condition is under-documented and, via per-condition documentation ML models and based on documentation received by the system, a probability that a medical condition is over-documented. The under- and over-documentation scores are combined in view of special indicators and queryability factors, which can also be evaluated using ML query prediction models, to generate an initial CDI score. This CDI score can be further adjusted, if necessary or desired, to account for factors such as length of stay, payer, patient location, CDI review timing, etc. The final CDI score can be used to prioritize patient cases for review by CDI specialists to quickly and efficiently identify meaningful CDI opportunities.

Abstract: A system and process for dynamically managing a procedure. The system includes a data collection device(s) that objectively monitor a subject and transmit such information to the system and real time position components that report on the availability of an individual(s) to perform the procedure. The system reviews the information, provides a subject diagnosis and recommends equipment. The system can also schedule and monitor the procedure. The individual(s) performing the procedure have the option of consulting with relevant experts and visually interacting with the experts.

Abstract: Point of care ultrasound examinations can be routed and processed in a computer network via the platforms, systems, and methods disclosed herein. The platforms, systems, and/or methods can use computing devices that are configured to provide a client application and at least one server processor for enabling use of an application. The examinations can be managed using an interface that allows an administrator user to assign user roles to other users, which can enable interactions within the network for processing and serving an ultrasound exam object. The ultrasound exam object can include patient data, images, and/or other relevant information.

Abstract: A computer-implemented method for determining risk scores using machine learning based on sensor data. The method includes receiving sensor data and demographic data for a user, determining a pattern of behavior based on the sensor data and demographic data, identifying a plurality of test groups based on other users that exhibit similar patterns of behavior to each other where sensor data from other users' electronic devices are received, identifying a test group corresponding to the user from among the plurality of test groups, determining a risk score using a trained risk analyzer machine learning model trained with historical sensor data, historical demographic data, and feedback data indicative of incidents, identifying risk mitigation strategies based on the risk score, refining the risk mitigation strategies using an iterative machine learning process as additional feedback data is collected, and retrieving risk mitigation communications from a database when the risk score exceeds a threshold.

Abstract: A wireless locating system and method include receiving first messages at a server. The first messages include bed location data entered manually on a user interface of a first patient bed. The first messages also include signal strength data from a plurality of wireless access points in communication with the first patient bed. A first signal strength profile for the first patient bed at a first location is built by the server. The first patient bed is removed from the first location and a second bed is moved into the first location. The second patient bed does not have manual location data entry capability. A second signal strength profile is built by the server for the second patient bed. The server compares the second signal strength profile with the first signal strength profile and determines that the second patient bed is at the first location if the second signal strength profile matches the first signal strength profile.

Abstract: Methods for communicating risk of skin conditions due to exposure to sunlight and environmental conditions to individuals. Methods utilize geolocation data and environmental data to provide guidance and enable consumers to make informed decisions regarding outdoor activities, healthcare, and skincare. A photopollution metric is a function of ultraviolet index (UVI) and the level of a pollutant such as particulate matter (PM) and enables communication of risk of exposome-induced skin conditions to an individual based on environmental exposure.

Abstract: A system may receive radiology data, outcome data for one or more exams, and elapsed time data for the one or more exams. The system may train a first machine learning algorithm to detect exams that are candidates for cherry-picking using the set of radiology data, outcome, and elapsed time data. The system may further determine, using the radiology data and the first machine learning algorithm, a likelihood that an exam was rejected due to cherry-picking.

Abstract: A system, method and wireless earpieces for populating an electronic medical record utilizing wireless earpieces. The sensor measurements are analyzed. The sensor measurements are associated with the electronic medical record of the user. The electronic medical record of the user is populated with the sensor measurements. Communications including the electronic medical record are communicated.

Abstract: A patient portal system may include a patient database module to interpret a patient profile value and a clinical database module to interpret a clinical study description. The patient database module may also interpret patient clinical data in response to the patient profile value and the clinical study description, and include at least a portion of the patient clinical data as longitudinal data of a patient corresponding to the patient profile value. The system may also include a patient interface module to implement a patient engagement interface and to interpret a patient medical event value in response to patient interactions with the patient engagement interface. The patient database may also adjust the longitudinal data in response to the patient medical event value.

Abstract: The present disclosure provides a data processing method and device, the method includes: acquiring exercise data of a user in a preset time period and a physical parameter preset by the user, wherein the exercise data comprises exercise data of a head, a hand and a leg of the user; and inputting the exercise data and the physical parameter into a pre-trained energy determination model for processing to obtain energy consumed by the user in the preset time period.

Abstract: Examples in the present disclosure relate to multi-dimensional reinforcement learning in a digital platform. The digital platform maintains and updates user data associated with tracked users. One or more machine learning models process the user data to generate recommendation data that includes automatically generated exploratory program elements for known activity programs of respective users of the digital platform. The exploratory program elements are validated by supervising users before implementation. The digital platform continuously updates the one or more machine learning models using dual reinforcement signals: one based on supervising user-validated recommendations and another based on activity program results. Further recommendation data is generated after updating the one or more machine learning models.

Abstract: A surgical computing system may provide first information about a surgical procedure to a recommendation model. The first information may include the identity of an observation point of a surgical device to be used during the surgical procedure. The surgical device may collect data descriptive of an object of the observation point. The surgical computing system may receive second information from the recommendation model. The second information may be indicative of a recommended schema for the observation point. The recommended schema may define a timing via which the surgical device is to collect data. The surgical computing device may send, to the surgical device, an instruction to collect, during the surgical procedure, data descriptive of an object of the observation point according to the timing defined by the recommended schema.

Abstract: The present invention relates to methods for predicting the clinical outcome or the response of a patient suffering from cancer to an anti-cancer therapy based on novel gene signatures.

Abstract: An approach to supplementing or replacing at least one of a prescribed pharmaceutical can include receiving a usage agreement, executed by at least one of the subject or a medical professional, to use an inert substance according to a treatment regimen. A name or description of at least one health improvement product or protocol can be received, as well as an indication of a downtitration of the pharmaceutical. A treatment regimen can be determined, including an administration of the inert substance to the subject following the downtitration, including notifying the subject that the inert substance is not pharmacologically active.

Abstract: A system and method providing automated voice-based healthcare plan delivery. The system interfaces with the patient through a voice-controlled personal assistant appliance via the internet. Some embodiments are implemented as voice applications or skills within the eco-system of an existing voice-controlled personal assistant device/appliance (hereinafter VCPAD). The system and method allow a clinician to create, digitally input, and modify a healthcare plan for a patient using a simple computer/web interface and without the need of any software development or computer programming skills, and allows a patient to interface with and query that healthcare plan using a largely conventional voice-controlled digital assistant appliance simply and efficiently to extract information about his/her individual healthcare plan in the patient's own language.

Abstract: A learning apparatus, a walking training apparatus, a method, a system, a program, and a trained model for performing walking training at appropriate setting parameters are provided. A learning apparatus according to an embodiment includes: a data acquisition unit configured to acquire rehabilitation data from a walking training apparatus including an actuator configured to assist a walking motion of a trainee and a control unit configured to control the actuator in accordance with a setting parameter; and a data generation unit configured to generate an index indicating a degree of recovery of the trainee and the setting parameter as learning data; and a learning unit configured to generate a learning model that receives the index and outputs a recommended value of the setting parameter by performing machine learning using the learning data.

Abstract: A computer-implemented method is disclosed. The method includes receiving, at a computing device, a first treatment plan designed to treat an invasive surgical-related health issue of a user. The first treatment plan comprises at least two exercise sessions that, based on the invasive surgical-related health issue, enable the user to perform an exercise at different exertion levels. Next, while the user uses the electromechanical machine to perform the first treatment plan, receiving, at the computing device, data from sensors configured to measure the data associated with the invasive surgical-related health issue and transmitting the data. One or more machine learning models are used to generate a second treatment plan. The second treatment plan modifies at least one exertion level, and the modification is based on a standardized measure comprising perceived exertion, the data, and the invasive surgical-related health issue. The method additionally includes receiving the second treatment plan.

Abstract: Systems and methods are provided for workload management in a healthcare setting. In one example, a method for determining a workload demand of a medical facility includes automatically determining a workload score for a care team including plurality of clinicians over a shift based on shift data and event data received from the medical facility, including calculating a cumulative workload over the shift based on the event data and a workload reduction over time based on the shift data, generating a graphical workload score tile including a visual representation of the workload score, arranging the graphical workload score tile in a workload graphical user interface (GUI), and displaying the GUI on a display device.