Abstract: Methods and devices include identifying a plurality of target users for the digital therapeutic based on one or more target parameters, conducting outreach to one or more of the plurality of target users using an outreach medium, identifying an activation mechanism to optimize use of the digital therapeutic, and encouraging an engagement level of the digital therapeutic by one or more of the plurality of target users.

Abstract: A method includes maintaining sets of values for mutable and immutable attributes. Each outcome model of a set of outcome models generates a predicted likelihood of an outcome in response to at least one immutable attribute value and at least one mutable attribute value. A prediction request specifies a first outcome, values for at least one immutable attribute, and values for at least one mutable attribute. The method includes, in response, selecting a group of the sets, where each has values for the immutable attributes that match those of the prediction request. The method includes determining a conditional covariance matrix for the group of sets and then generating a deviation model. The method includes sampling the deviation model to generate sets of mutable attribute values. The method includes, for each of the sets of mutable attribute values, generating, using a selected outcome model, a likelihood of the first outcome occurring.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for geofencing and location tracking for predicting and limiting disease exposure. In some implementations, location tracking data is received indicating locations of user devices over time. Location tags specifying visits of the user devices to different locations indicated by the location tracking data are defined. A geofence is assigned to each of the location tags to specify a geofenced area corresponding to the location tag. Disease transmission scores are assigned to first location tags representing visits of a first user. A disease exposure score is determined for a second user whose user device is determined, based on the location tracking data, to have entered at least one of the geofenced areas corresponding to the first location tags.

Abstract: Disclosed is a computer controlled dosage system, for dosage adjustment for a mobile, hand held, inhaler for delivering a dosage of a medicine, is provided. The system comprises at least one measuring device for measuring at least one parameter; and a handheld mobile computer separate from the inhaler, the computer being configured to communicate with the at least one measuring device and with a remote memory for sending and receiving information to and from patient medical records of the remote memory for storage in a memory of the computer, and the computer being configured to receive a manual input for storage in the memory of the computer. The computer is configured to create a data set for setting a plurality of levels of different dosages of medicine based on the medicine used by the inhaler, the information from the patient's medical records of the remote memory, and the manual input; and the computer is further configured to store the data set in the memory of the computer.

Abstract: Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients.

Abstract: An interface displaying a plurality of previously determined wellness scores for a plurality of patients may be generated and provided to a display device. The patients may each be associated with a patient characteristic, a treatment, and/or a diagnosis. In some instances, the patients may be associated with a particular treatment provider or group of treatment providers and plurality of previously determined wellness scores may enable a treatment provider to quickly view (via the interface) the current wellness of patients under his or her care. In other instances, the plurality of previously determined wellness scores may be associated with a diagnosis and a corresponding treatment so that the interface provides the wellness scores for patients who have undergone the treatment and a viewer may observe how the treatment impacted the wellness scores for the patients.

Abstract: Intelligent cognitive assistants for decision-making are provided. A first plurality of decisions made by a first healthcare provider during treatment of a first patient is monitored. For each respective decision of the first plurality of decisions, one or more corresponding medical attributes of the first patient that were present at a time when the respective decision was made are determined. A cognitive assistant is trained, using an imitation learning model, based on each of the first plurality of decisions and the corresponding one or more medical attributes of the first patient. Subsequently, one or more medical attributes of a second patient are received, and a first medical decision is generated by processing the one or more medical attributes of the second patient using the cognitive assistant.

Abstract: Methods and devices include identifying a plurality of target users for the digital therapeutic based on one or more target parameters, conducting outreach to one or more of the plurality of target users using an outreach medium, identifying an activation mechanism to optimize use of the digital therapeutic, and encouraging an engagement level of the digital therapeutic by one or more of the plurality of target users.

Abstract: Methods and devices include identifying a plurality of target users for the digital therapeutic based on one or more target parameters, conducting outreach to one or more of the plurality of target users using an outreach medium, identifying an activation mechanism to optimize use of the digital therapeutic, and encouraging an engagement level of the digital therapeutic by one or more of the plurality of target users.

Abstract: A food intake control method, system, and computer program product, includes detecting types of food available to a user, categorizing a list of the types of food available to the user based on a harm of a type of food to the user, and administering a nudge to the user to assist the user in avoiding a type of food having the harm.

Abstract: Mechanisms are provided to implement a medical condition verification system. The medical condition verification system receives patient electronic medical record (EMR) data and parses the patient EMR data to identify an instance of a medical code or medical condition indicator present in the patient EMR data. The medical condition verification system performs cognitive analysis of the patient EMR data to identify evidential data supportive of the instance referencing an associated medical condition. The medical condition verification system generates a measure of risk of the patient having the medical condition based on the identified evidential data and based on a machine learned relationship of medical factors in patient EMR data relevant to generating the measure of risk for the associated medical condition. The medical condition verification system generates an output representing the measure of risk of the patient having the associated medical condition.

Abstract: Computer program products, methods, systems, apparatus, and computing entities are described for identifying significant incidental findings from medical records. In one example embodiment, an example computing device receives a medical report and derives a textual component from the medical report. The computing device then identifies one or more medical findings from the textual component and determines a clinical context for each of the one or more medical findings. The computing device then identifies one or more clinical cues from the one or more medical findings and generates one or more condition signals from the one or more clinical cues. The computing device then generates a condition alert from the one or more condition signals. The condition alert is indicative of a significant incidental finding. Using various embodiments contemplated herein, significant incidental findings can be identified for follow-up by a user.

Abstract: Approaches presented herein enable cognitive oral health management. Specifically, data from at least one internet of things (IoT) sensor and data from at least one visual image sensor are collected over time for a user via one or more sensors operatively coupled with one or more personal daily oral care devices as the user is performing a periodic oral care activity with the one or more personal daily oral care devices. Collected data are subjected to cognitive diagnostics to identify abnormalities in a user's oral healthcare regimen and physiological abnormalities within the oral cavity by categorizing data. A report for a medical professional may be generated providing recommendation of potential diagnoses and treatment options.

Abstract: Approaches presented herein enable cognitive oral health management. Specifically, data from at least one internet of things (IoT) sensor and data from at least one visual image sensor are collected over time for a user via one or more sensors operatively coupled with one or more personal daily oral care devices as the user is performing a periodic oral care activity with the one or more personal daily oral care devices. Collected data are subjected to cognitive diagnostics to identify abnormalities in a user's oral healthcare regimen and physiological abnormalities within the oral cavity by categorizing data. A report for a medical professional may be generated providing recommendation of potential diagnoses and treatment options.

Abstract: Certain examples provide systems and methods to monitor and control hospital operational systems based on occupancy data and medical orders. An example healthcare workflow management and reasoning system includes a workflow engine including a first particularly programmed processor to monitor one or more medical orders from one or more hospital information systems to identify a condition indicating that a first patient in a first room is ready for a clinical activity such as discharge. The example healthcare workflow management and reasoning system includes a sensing component including a second processor to gather occupancy data regarding the first patient in the first room and transmit the occupancy data to the workflow engine. The example workflow engine controls one or more hospital operational systems to trigger cleaning of the first room, lighting settings for the first room, and transportation of a second patient to the first room based on occupancy data from the sensing component.

Abstract: Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and dynamic patient guidance. Embodiments automatically update patient guidance in the patient care plan, based on the effectiveness of the guidance to date, attributes of the patient, other updated information, ongoing experience of the network, and updated predictions of possible patient outcomes and metrics.

Abstract: Systems and methods are provided for identifying and tracking diverters of controlled medications. A system may receive signals indicative of medication dispensing activities by one or more health care providers such as nurses, physicians, or pharmacists. Based on the received signals, the system may determine one or more factor scores for each health care provider. The factor scores may be numerical indicators of potential diversion for corresponding factors related to usage, waste, dosage, or other factors. The factor scores may be combined to determine a total diversion score for each of one or more potential diverters.

Abstract: Triggered sensor data capture in a mobile device environment. A method monitors primary sensor data obtained from first wearable sensor device(s) to determine whether trigger condition(s) are met for triggering supplemental sensor data capture. Based on recognizing a health event, the method obtains health status input from a user, configures second wearable sensor device(s) to obtain supplemental sensor data that includes additional data in addition to the primary sensor data, and obtains the supplemental sensor data. The method provides the health status input and the obtained supplemental sensor data as correlated health event data of the health event for analysis. Based on the analysis, the method tunes at least one trigger condition of the trigger condition(s) to adjust a scope of supplemental sensor data capture.

Abstract: A method, computing system and computer program product are provided. A group of connected devices are formed from at least some multiple connected devices. A computing system receives current usage data for a person from the group of connected devices with respect to activities. The computing system compares the current usage data with a baseline for the person to produce change data and applies an analytic analysis to the change data to determine a health concern for the person. A notification is provided when the change data exceeds a predetermined threshold with respect to the baseline. In another embodiment, a group of connected devices is managed by excluding, from the group, a connected device that contributes to false positive data.

Abstract: A MSP platform provides contingent healthcare worker recruiting and shift assignation in a multilayered process of job order broadcasting, competency matching, proposals from healthcare agencies aka vendors, screening, compliance management, and onboard of each candidate. Each staff profile submitted has to go through multilayered review, approval, and orientation process. Additionally, each healthcare worker's calendar, credential, and compliance have to be managed across multiple employers to prevent scheduling conflict and compliance violations, and guaranteeing full visibility of all healthcare workers across the entire supply chain. MSPs (Managed Service Providers) have the ability to service a large number of facilities on whose behalf the MSPs generate job orders for contingent workforce, and manage fulfillment using suppliers (aka vendors) mapped to the facility being serviced.