Abstract: A system and method for generating a total health index and recommending an actionable intervention for managing a health risk of a patient is disclosed. The method includes receiving user-generated data, clinical data, and pharmacy data in association with a patient, determining a social health index score based on the user-generated data, determining a clinical risk index score based on the clinical data, determining a medication adherence index score based on the pharmacy data, determining, using a machine learning model on one or more of the social health index score, the clinical risk index score, and the medication adherence index score, a total health index score and an actionable intervention for managing a health risk in association with the patient based on the total health index score, and automatically initiating the actionable intervention.

Abstract: Technologies and techniques for processing drug data in a specialized medical computer system. Autonomous moving code may be received via a communications interface from a computer network, where the autonomous movable code includes patient data, lab test data, demographic data, and diagnosis data. The autonomous movable code is executed in a system agent manager and processed in a learning module, where the learning module performs predictive processing to determine if an opioid abuse condition exists. New autonomous movable code may be generated that includes information and/or instructions regarding the opioid abuse condition. The new autonomous movable code is then transmitted back to the computer network.

Abstract: A computer-implemented system includes one or more processing devices configured to receive comorbidity information that includes a plurality of comorbidities or comorbidity-related conditions associated with a user, generate a selected set of the comorbidity information, determine, based on the selected set of the comorbidity information, respective probabilities of a plurality of different outcomes related to the comorbidity information, and generate, based on the respective probabilities and the selected set of the comorbidity information, a treatment plan comprising one or more exercises directed to changing the respective probabilities. A treatment apparatus is configured to implement the treatment plan while the treatment apparatus is being manipulated by the user.

Abstract: Method, apparatus, and computer-readable media providing medical personnel artificial intelligence (AI)-derived data regarding a patient presenting neurological indications includes a guidance server. Preferably, the guidance server receives three or more of: (i) natural language input from medical personnel, (ii) real-time vital signs telemetry data, (iii) neurosurgical treatment database information, (iv) neurosurgical treatment guideline information, (v) clinical laboratory testing results, (vi) patient historical data, and (vii) patient imaging information. An imaging post-processor receives (i) patient imaging history information, (ii) and real-time patient imaging data, and provides to the guidance server the patient imaging information. The guidance server then provides one or more alerts (or other guidance) to the medical personnel.

Abstract: Pulmonary function estimation can include detecting one or more cough events from a time series of audio signals generated by an electronic device of a user. Based on the one or more cough events, one or more lung function metrics of the user can be determined.

Abstract: An application system capable of providing a continuation of care for a user. This is achieved through the assessing of medication compliance, assessing medical testing compliance and providing appropriate care suggestions. The system incorporates medication reminder, medication compliance analytics and the capacity to provide responses to user specific medication inquiries. The system also incorporates medical test assessment capabilities and the capacity to provide basic medical testing protocols. Additionally, as medication and medical testing data are collected, processed and analyzed, they are stored in a zero-click fashion with results being rapidly presented back to the user via an interactive avatar. These data can also be provided in standard of care summaries to users, HIPAA compliant third parties and/or medical care providers.

Abstract: Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Abstract: A patient monitoring system includes a physiological sensor to sense light after it has passed through tissue of a patient and generate a signal indicative a physiological parameters in response to the sensed light, and a patient monitoring device in communication with the physiological sensor to receive the signal and determine measurements of the physiological parameters from the received signal. The patient monitoring device includes a processor and memory having multiple system images. The patient monitoring device downloads an image upgrade to one system image that the not latest used or tested system image. The patient monitoring device boots the processor from the system image that includes the image upgrade. If the upgraded system image fails, the patient monitoring device boots the processor from another system image of the multiple system images. The patient monitoring device repairs the failed system image.

Abstract: A healthcare delivery system, a wearable computing device and a method are disclosed. The method includes capturing, by the wearable computing device worn by a medical practitioner, medical multimedia data of a patient during a medical procedure. The method further includes displaying one or more selectable options associated with the medical multimedia data on a display screen of the wearable computing device to the medical practitioner. Further, the method includes receiving a selection of a selectable option from among the one or more selectable options. Thereafter, at least one action is performed on the medical multimedia data based on the selection of the selectable option. An example of the at least one action includes sharing at least a part of the medical multimedia data with a third party device.

Abstract: A system and method for generating a total health index and recommending an actionable intervention for managing a health risk of a patient is disclosed. The method includes receiving user-generated data, clinical data, and pharmacy data in association with a patient, determining a social health index score based on the user-generated data, determining a clinical risk index score based on the clinical data, determining a medication adherence index score based on the pharmacy data, determining, using a machine learning model on one or more of the social health index score, the clinical risk index score, and the medication adherence index score, a total health index score and an actionable intervention for managing a health risk in association with the patient based on the total health index score, and automatically initiating the actionable intervention.

Abstract: A system and method for facilitating employee health screening using an application on a mobile computing device are provided. The system and method can provide a centralized platform for providing a health screening questionnaire for employees to fill out before visiting an employer's facility. The system and method can perform an employee badge activation/deactivation function based on one or more indicators relating to employee health information.

Abstract: The present invention pertains to a process for providing failure safety for a medical monitoring procedure (40) of patient data (41), wherein the medical monitoring procedure (40) is carried out by a standard computer (10), wherein the patient data (41) are sent, furthermore, to the standard computer (10) and are compared with threshold values there. The present invention further pertains to a control device (20), to a computer program product (30) as well as to a system (1) for providing failure safety for a medical monitoring procedure (40) of patient data (41), wherein the medical monitoring procedure (40) is carried out by a standard computer (10).

Abstract: An electronic payment system, a computer-implemented method, and a computer readable medium having stored thereon a computer executable program code for processing of the electronic medical invoices and reimbursement of medical costs is disclosed herein. The electronic payment system comprises a server operable for execution of an application management component and a set of server applications comprising receiver applications and processing applications, wherein the server comprises a processing unit and a memory storing computer executable code which when executed by the processing unit cases the server to execute the computer-implemented method.

Abstract: A computer-implemented method for expediting processing of a payment for a patient's healthcare service with a healthcare provider includes displaying an out-of-pocket cost for a patient for a healthcare service at a display interface of a computer device prior to the healthcare service being rendered. After patient selection of the healthcare service and payment of any out-of-pocket costs, a health token is issued to the patient as cryptocurrency for payment of the healthcare service. The health token is received from the healthcare provider after the healthcare provider renders the healthcare service, receives the health token from the patient for payment of the healthcare service, and embeds data pertaining to the healthcare service into the health token. Based on the data embedded in the health token, the healthcare service is approved or disapproved. If the healthcare service is approved, a payment for the healthcare service is issued to the healthcare provider.

Abstract: A computing system can implement a health verification and prediction service by generating an interactive user interface through which users can provide responses to health assertions in one or more trivia sessions. Each of the health assertions can include a correlation value to actual health outcomes based on responses received from individuals a control group and the known health outcomes of those individuals. Based on the responses from the user, the computing system can generate a health profile for the user.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a morbidity risk profile that is used to classify the user in an underwriting class.

Abstract: A system and method are disclosed for computing a Health Score. Health data and extrinsic data are received that are parameters for computation of the Health Score. The received data can be combined using an algorithm being implemented as code executing in a processor so as to compute the Health Score of the individual wherein parameters comprising one portion of the data interacts with parameters comprising another portion of the data. Further, the computed Health Score is output to an interface of the user device. Information concerning the parameters' interaction are selectively output to the interface that explain which changes in the parameters are significant drivers of the change in the Health Score.

Abstract: A system for simulating a vitality metric, the system comprising a computing device, wherein the computing device is configured to retrieve, from a user, a biotic extraction, generate a vitality metric, using a machine-learning model, wherein generating a vitality metric further comprises training a machine-learning model with training data corresponding to measuring biotic parameters present in the biotic extraction data and determining a metric that is a summation of all individual biotic parameters present in the biotic extraction data. Computing device determines a simulated metric, using a simulation machine-learning process, wherein the simulation perturbs a biotic parameter present in the vitality metric, wherein a biotic parameter is an element of numerical data relating to an element of data present in the at least a user biotic extraction. Computing device provides, to a user, a vitality metric and at least a user effort that resulted in a simulated metric.

Abstract: Disclosed is a system and a method for monitoring patient's health. Data associated with multiple questions nudged to a patient may be received. A plurality of symptoms may be extracted. The plurality of symptoms may be analyzed using to generate a health assessment score. The health assessment score may be compared with a predefined threshold to determine a health condition. A deviation in the health condition may be determined by comparing the health condition and previous health data. A risk level may be identified using an artificial intelligence technique. A course of action may be recommended to the patient in real-time based on the risk level and the health condition. A healthcare practitioner may be identified in real-time. The healthcare practitioner and the patient may be notified in real-time. A follow-up check for the patient may be scheduled based on the risk level and the health condition.

Abstract: A drug library management system facilitates centralized management of the drug libraries that are used by various infusion pumps, including in clinical environments that have different types and/or versions of infusion pumps. Medications, administration rules, critical care area rules, and the like can be maintained using the drug library management system. The drug library management system can generate and distribute drug library data in pump-specific formats or other customized formats as needed.