A method for autonomously generating a care plan personalized for a patient is disclosed. The method includes receiving a selection of a type of the care plan to implement for the patient, generating the care plan based on the type selected, wherein the care plan includes an action instruction based on a patient graph of the patient and a knowledge graph including ontological medical data, receiving patient data that indicates health related information associated with the patient, modifying the care plan to generate a modified care plan in real-time or near real-time based on the patient data, and causing the modified care plan to be presented on a computing device of a medical personnel.

Certain aspects of the present disclosure relate to methods of updating patient scheduling information. In one aspect, the method includes receiving patient data for a patient having a scheduled appointment on a future date, the patient data including a metric value for a biomarker and time and date information associated with the scheduled appointment. The method further includes comparing the metric value with one or more conditions established based at least in part on a patient history of the patient or population health data. The method also includes, after determining that the metric value satisfies at least one of the one or more conditions, rescheduling the scheduled appointment.

Certain aspects of the present disclosure relate to methods of updating patient scheduling information. In one aspect, the method includes receiving patient data for a patient having a scheduled appointment on a future date, the patient data including a metric value for a biomarker and time and date information associated with the scheduled appointment. The method further includes comparing the metric value with one or more conditions established based at least in part on a patient history of the patient or population health data. The method also includes, after determining that the metric value satisfies at least one of the one or more conditions, rescheduling the scheduled appointment.

Certain aspects of the present disclosure relate to electronically sharing patient data. One aspect includes a method comprising capturing a computer readable code comprising an authentication code and clinic identifying information using an image capture component of a patient mobile device. The method also comprises authenticating the identified clinic with an information provider. The method further comprises displaying the clinic identifying information for confirmation and authenticating the patient. The method additionally comprises receiving a request to provide the clinic with patient data from the clinic. The method then comprises determining that the clinic is authorized to receive access to the patient data based on authentication of the clinic, authentication of the patient, and confirmation to share the patient data. The method also comprises transmitting the patient data to the clinic based on the determination that the clinic is authorized.

A system and method for providing sensory relief from distractibility, inattention, anxiety, fatigue, and/or sensory issues to a user in need. The user can be autistic/neurodiverse, or neurotypical. The system can be configured to connect to a datastore storing one or more sensory thresholds specific to a user of a wearable device of the system, the sensory thresholds selected from auditory, visual or physiological sensory thresholds; record, using one or more sensors of the wearable device, a sensory input stimulus to the user; compare the sensory input stimulus with the sensory thresholds to determine an intervention to be provided to the user, the intervention configured to provide the user relief from distractibility, inattention, anxiety, fatigue, or sensory issues; and provide the intervention to the user, the intervention comprising filtering, in real-time, an audio signal presented to the user or an optical signal presented to the user.

A computerized method for transforming an interactive graphical user interface according to machine learning includes selecting a persona, loading a data structure associated with the selected persona, and generating the interactive graphical user interface. The method includes, in response to a user selecting a first selectable element, inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric, and transforming the user interface according to the selected persona and the first metric. The method includes, in response to the user selecting a second selectable element, inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric, and transforming the user interface according to the selected persona and the second metric. In various implementations, first metric is a first probability of the persona being approved for a first prior authorization prescription.

A method and system for generating real time workload balancing recommendations comprising receiving transition data, medical data, and staffing data; determining a transition probability for each of a plurality of patients; determining a predicted workload to be generated by each of the plurality of patients; simulating the predicted workload to be generated by each of the plurality of patients, the future workload for each of a plurality of units in the hospital; generating staffing recommendations; and displaying the generated staffing recommendations on a user display of the workload balancing system.

A method and system for generating real time workload balancing recommendations comprising receiving transition data, medical data, and staffing data; determining a transition probability for each of a plurality of patients; determining a predicted workload to be generated by each of the plurality of patients; simulating the predicted workload to be generated by each of the plurality of patients, the future workload for each of a plurality of units in the hospital; generating staffing recommendations; and displaying the generated staffing recommendations on a user display of the workload balancing system.

The instant invention relates to a system that in one form is a referral system for use in various healthcare sectors, which utilizes patient profiles, created using patient trackers, to monitor patient follow-up care. The instant invention allows physicians and providers to communicate about patient’s care in its entirety, beginning with the originating provider creating a patient tracker, sharing that patient tracker with referred physicians, and discharging the patient once the patient tracker’s plans have concluded. This uninterrupted flow of communication amongst all providers and physicians allows for a secured method of tracking each patient’s healthcare plan, provides for efficient healthcare referral follow-ups, and updates the originating HCPs records to ensure there are no gaps in terms of patient treatment.

Methods and systems for training a model using machine learning for automatically distributing medical imaging studies to radiologists. One method includes receiving one or more medical images included in a medical study, each of the one or more medical images including image metadata defining characteristics of the corresponding medical image. The method further includes receiving radiologist metadata for each one of the plurality of radiologists, generating a state representation of the image metadata and the radiologist metadata, and providing the state representation to the model. The method further includes assigning, with the model, at least one of the one or more medical images to one of the plurality of radiologists, calculating feedback based on a change in the state representation after the at least one of the one or more medical images is assigned to one of the plurality of radiologists, and adjusting the model based on the feedback.