Generally, described herein are computer-implemented systems and methods for self-guided education in various aspects of health care. The described systems and methods effect behavior change through a functional relationship between the content of educational case packages comprising a plurality of displays, each of which has one or more completion conditions which must be completed by the user in accordance with a health care procedure to be practiced. The performance of the case by the user habituates behavior change through practice of a health care procedure.

Generally, described herein are computer-implemented systems and methods for self-guided education in various aspects of health care. The described systems and methods effect behavior change through a functional relationship between the content of educational case packages comprising a plurality of displays, each of which has one or more completion conditions which must be completed by the user in accordance with a health care procedure to be practiced. The performance of the case by the user habituates behavior change through practice of a health care procedure.

A Digital Family Care Tracking and Coordination (DFCTC) computing system associates a member with one or more care managers. The system stores member data on a cloud-based storage device. The one or more care managers are given access to the personal care data associated with the member via a mobile application. Member data includes medical information and relevant notes pertaining to a member's healthcare journey.

Processing patient information and other treatment plan information may require access to a patient profile and other third parties involved in the patient treatment plan. One example method includes selecting a treatment plan for a patient comprising a set of treatment information, linking an application identifier and a T-code identifier to the treatment plan, launching a treatment plan application, retrieving the set of treatment information, populating the treatment plan application with the set of treatment information, triggering a message dispatch in accordance with the treatment plan, the message dispatch including a query to a health related issue to determine a patient status and receiving a patient response to the message.

The present solution covers identifying a recommended treatment for a patient based on records of similar patients, wherein the similarities are non-obvious and non-linear. The solution generates a similarity map that minimizes the variance of elements records among a curated group of patients, and this similarity map is used to find the patients who are most similar to an untreated patient.

Disclosed herein are systems and techniques for evaluating prescribed optical devices during use. A method can include matching a user profile with a prescribed optical device, matching the prescribed optical device with a plurality of members of a distribution system of the prescribed optical device, requesting information about the prescribed optical device through a user interface, receiving information in response to requesting the information, and sending feedback based on the received information to one of the members of the distribution system. One or more network devices can generate a user interface including information associated with the prescribed optical device and the user profile. The user interface can be adapted based on a primary or secondary user of the network device. The user interface can also be adapted as the user progresses in age, treatment schedule, and/or other factors that support evaluation of the prescribed optical device.

Disclosed herein are systems and techniques for evaluating prescribed optical devices during use. A method can include matching a user profile with a prescribed optical device, matching the prescribed optical device with a plurality of members of a distribution system of the prescribed optical device, requesting information about the prescribed optical device through a user interface, receiving information in response to requesting the information, and sending feedback based on the received information to one of the members of the distribution system. One or more network devices can generate a user interface including information associated with the prescribed optical device and the user profile. The user interface can be adapted based on a primary or secondary user of the network device. The user interface can also be adapted as the user progresses in age, treatment schedule, and/or other factors that support evaluation of the prescribed optical device.

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

A system and method for analyzing risk and providing risk mitigation instructions. The system receives analyzes sensor data and other data corresponding to a user to determine a test group. The system uses the test group to determine a risk score, and, subsequently, a risk mitigation strategy. Machine learning techniques are implemented to refine how the test group, risk score, and mitigation are each selected.