Abstract: In some examples, systems, methods, and devices are described that generate contextual suggestions for patients. Generation of the contextual suggestions is triggered by certain events performed by a medical professional with respect to a patient (e.g., updating a patient record). The contextual suggestions are related to addressing health conditions of the patient and represent tasks or considerations which the medical professional should be made aware. The contextual suggestions are generated in a way that is considerate of patient context, medical professional context, and contexts of similar patients. The contextual suggestions can be presented to the medical professional for selection and execution.

Abstract: In some examples, user models are generated to represent experience of users with respect to their roles. Models that represent conditions are also generated. As part of generating a support element for a particular user, a triggering event is detected (e.g., an update to a user record), a condition is identified, a condition model and a user model are compared to identify a condition preparedness score. This score is used to generate the support element, which is shared with a user device of the user.

Abstract: In some examples, unstructured data is evaluated using a natural language processing model to output a set of subjective indicators. These subjective indicators are scored using a predictive model to determine whether a dependent user has or is likely to develop a particular condition such as a cellular abnormality.

Abstract: A method and system for initiating message listening and routing message content to authorized user devices is disclosed. For a second user device to receive notifications regarding records of a first user, the second user device provides information identifying the first user to a notification service. The notification service verifies the identifying information. The notification service initiates one or more listeners to listen for messages flowing over a messaging bus that are relating to the first user. Once a message is identified, at least a portion of the message is used to generate a notification that may be sent to the second user device.

Abstract: In some examples, authorized user profiles and generic models are generated using historical interaction data of authorized users. The authorized user profiles provide an experience profile for authorized users. The generic models are data models used to model authorized user actions. The authorized user profiles and generic models are used to generate support elements. The support elements are presented to authorized users and feedback is collected. Based on the feedback, the authorized user profiles and generic models are updated.

Abstract: In some examples, systems, methods, and devices are described that generate contextual suggestions for patients. Generation of the contextual suggestions is triggered by certain events performed by a medical professional with respect to a patient (e.g., updating a patient record). The contextual suggestions are related to addressing health conditions of the patient and represent tasks or considerations which the medical professional should be made aware. The contextual suggestions are generated in a way that is considerate of patient context, medical professional context, and contexts of similar patients. The contextual suggestions can be presented to the medical professional for selection and execution.

Abstract: In some examples, unstructured data is evaluated using a natural language processing model to output a set of subjective indicators. These subjective indicators are scored using a predictive model to determine whether a dependent user has or is likely to develop a particular condition such as a cellular abnormality.

Abstract: In some examples, structured and unstructured data is evaluated using one or more predictive models to determine whether a dependent user is at risk for a certain condition. In other examples, structured and unstructured data is evaluated using one or more predictive models to determine a contact plan for contacting dependent users regarding follow-up appointments related to release of the dependent user.

Abstract: In some examples, user models are generated to represent experience of users with respect to their roles. Models that represent conditions are also generated. As part of generating a support element for a particular user, a triggering event is detected (e.g., an update to a user record), a condition is identified, a condition model and a user model are compared to identify a condition preparedness score. This score is used to generate the support element, which is shared with a user device of the user.

Abstract: In some examples, unstructured data is evaluated using a natural language processing model to output a set of subjective indicators. These subjective indicators are scored using a predictive model to determine whether a dependent user has or is likely to develop a particular condition such as a cellular abnormality.

Abstract: In some examples, systems, methods, and devices are described that generate contextual suggestions for patients. Generation of the contextual suggestions is triggered by certain events performed by a medical professional with respect to a patient (e.g., updating a patient record). The contextual suggestions are related to addressing health conditions of the patient and represent tasks or considerations which the medical professional should be made aware. The contextual suggestions are generated in a way that is considerate of patient context, medical professional context, and contexts of similar patients. The contextual suggestions can be presented to the medical professional for selection and execution.

Abstract: In some examples, structured and unstructured data is analyzed to determine whether a user is at risk for a certain condition. The results of this analysis can be included in a risk assessment. In some examples, structured and unstructured data is analyzed to determine whether a particular event has taken place. Once detection of occurrence of the event has taken place, a notification is generated that a correct recipient is identified. In addition, it is determined if and how the notification should be escalated.

Abstract: In some examples, a suggestion engine is provided to generate decision support output (DSO) based on data collected generation devices within a network. The DSO may be based on dependent users details, profile information for an authorized user associated with the dependent user, one or more knowledge artifacts, and/or other information related to a relationship between the dependent user and the authorized user. The DSO may include one or more suggestions, tasks, considerations, or the like. These may be presented in a customizable contextual user interface which presents DSO in a manner that considers the underlying context in which the dependent user exists. Based on a selection of a task or suggestion, a populator engine may populate one or more forms, which may be approved and provided to other authorized users. In addition, certain analytics data may be organized according to a performance indicator and included within a dashboard, which may be included in the contextual user interface.

Abstract: A method and system for initiating message listening and routing message content to authorized user devices is disclosed. For a second user device to receive notifications regarding records of a first user, the second user device provides information identifying the first user to a notification service. The notification service verifies the identifying information. The notification service initiates one or more listeners to listen for messages flowing over a messaging bus that are relating to the first user. Once a message is identified, at least a portion of the message is used to generate a notification that may be sent to the second user device.

Abstract: In some examples, user models are generated to represent experience of users with respect to their roles. Models that represent conditions are also generated. As part of generating a support element for a particular user, a triggering event is detected (e.g., an update to a user record), a condition is identified, a condition model and a user model are compared to identify a condition preparedness score. This score is used to generate the support element, which is shared with a user device of the user.

Abstract: In some examples, systems, methods, and devices are described that generate contextual suggestions for patients. Generation of the contextual suggestions is triggered by certain events performed by a medical professional with respect to a patient (e.g., updating a patient record). The contextual suggestions are related to addressing health conditions of the patient and represent tasks or considerations which the medical professional should be made aware. The contextual suggestions are generated in a way that is considerate of patient context, medical professional context, and contexts of similar patients. The contextual suggestions can be presented to the medical professional for selection and execution.

Abstract: In some examples, a documentation service is provided in which documentation may be entered and process actions presented. The process actions can be determined by processing the documentation using a natural language processing technique and suggested to a professional user for performance or validation.

Abstract: In some examples, a service-based system for managing message routing and customization is provided. The service-based system includes an information exchange service bus and an information exchange engine. The system is configured to manage inbound and outbound messages. The system is also configured to tailor messages based on source entities, destination entities, and other users such as dependent users.

Abstract: In some examples, systems, methods, and devices are described that generate contextual suggestions for patients. Generation of the contextual suggestions is triggered by certain events performed by a medical professional with respect to a patient (e.g., updating a patient record). The contextual suggestions are related to addressing health conditions of the patient and represent tasks or considerations which the medical professional should be made aware. The contextual suggestions are generated in a way that is considerate of patient context, medical professional context, and contexts of similar patients. The contextual suggestions can be presented to the medical professional for selection and execution.

Abstract: In some examples, systems, methods, and devices are described that generate exposure evaluations for dependent users. First data associated with a user and including objective values is accessed. Second data associated with the user and including subjective observations is accessed. The first data and the second data is used as input to evaluate one or more evaluation rules. Based on the evaluation, an exposure evaluation is generated. A notification is generated to include the exposure evaluation, which is provided to one or more destination entities.