Abstract: Managing healthcare information includes a first healthcare professional subscribing to a subset of medical information for a patient in a healthcare information framework having a datastore containing electronic health records for the patient, the first healthcare professional being a member of a life-long care team for the patient that includes all care teams encountered by the patient at any time, a second healthcare professional posting medical information about the patient in the healthcare information framework, pushing, in real time or near real time, the medical information to at least one server that is coupled to the datastore, and the at least one server causing the first healthcare professional to automatically receive the subset of the medical information based on the first healthcare professional subscribing to the subset of the medical information. The second healthcare professional may be invited based on analysis of data in the datastore.

Abstract: Systems and methods are provided for identifying therapeutic zones where there is glycemic dysfunction of a specific type that can be addressed by making strategic changes to behavior and/or therapy parameters. Systems and methods described herein evaluate large historical data sets to: identify a therapeutic zone or zones with glycemic dysfunction that are most readily addressable; quantify the glycemic impact of a plurality of different therapeutic adjustments in terms of either adjustments to historical doses or the parameters of a prospective dosing strategy to determine the highest possible improvement; and/or identify patient dosing strategies to provide therapy recommendations adapted for the patient's preferred behavioral dosing strategy.

Abstract: A system and method for automatically calculating an accurate recommended dosage for hormone replacement therapy and automating the life cycle of a patient's treatment over time. The system and method can automatically acquire relevant patient parameters and apply a consistent formulaic approach to help reduce incorrect dosage determinations. A pellet insertion size may be determined and documented based on a calculated dosage, and an insertion side and lot numbers may be tracked and managed. In addition, corresponding revenues may be tracked and profitability may be reported for hormone replacement therapy practices.

Abstract: A system for selection of a medical provider (MP) by a requesting patient based on preferences for the MP includes a first model modeling the preferences of a plurality of patients for select MPs and trained on a first training data set comprised of patients and MPs. The patients in the first training data set have rated at least one of the MPs in the first data set by a predetermined set of patient determined preferences. A second model is provided for modeling the preferences of a plurality of MPs for select patients and trained on a second training data set comprised of MPs and patients. The MPs in the second training data set have rated at least one of the patients in the second data set by a predetermined set of MP determined preferences. An available MP data set is provided containing information representative of available MPs for selection thereof by the requesting patient.

Abstract: Systems and methods for calculating health care treatment statistics are provided. In one embodiment, a system comprises a data-holding subsystem storing raw medical claims data in non-transitory memory, and a logic subsystem configured to partition the raw medical claims data into a plurality of data sets based on one or more of insurance type, episode grouping, member key, date-of-service key, or a combination thereof, wherein the logic subsystem is further configured to calculate statistics based on each of the plurality of data sets and output correlated calculation results for display on a user device, and wherein the statistics include a frequency of patients receiving a health care service and an average cost of the health care service. In this way, a health care consumer may know what to expect for different treatment options for a given medical condition.

Abstract: Disclosed methodologies provide improved predictors of patient treatment adherence by using person-specific subjective experience and social-environmental factors. Methodologies combine emotion and data sciences. Advanced tools capture, measure, store, and analyze self-report of subjective experiences using digital applications and platforms. Patient-specific data is obtained regarding emotional or affective determinants and social determinants for generating a calculated composite score of the patient's probability of adherence or achievement relative to target outcomes, e.g. adherence to treatment plans, wellness activities, etc. for a subject individual. Internal/subjective factors are judged by self-report measures designed to validly judge tested factors based on a patient adjusting continuously-variable graphical interfaces to capture and measure subjective experiences.

Abstract: Embodiments of a method and system for improving care determination for care providers in relation to a condition of a user associated with a mobile device can include: collecting a log of use dataset associated with user digital communication behavior at the mobile device; collecting a mobility supplementary dataset corresponding to a mobility-related sensor of the mobile device; determining a medical status analysis for a condition of the user based on at least one of the log of use dataset and the mobility supplementary dataset, the medical status analysis including at least one of a diagnosis and a therapeutic intervention associated with the condition; and promoting the at least one of the diagnosis and the therapeutic intervention to a care provider.

Abstract: A system and method are provided for use in evaluating a clinical guideline which is represented in a machine readable version by a decision tree comprising at least one node and a decision rule associated with the node. The decision rule comprises at least one variable representing a biomedical quantity. The biomedical quantity is extracted from the patient data using an ontology which defines concepts and their relationships in a medical domain of the clinical guideline and which thereby relates the variable of the decision rule to the patient data. If said extraction is not possible, a view of the patient data is presented to the user to enable the user to determine the biomedical quantity from the view. Advantageously, the user is assisted in evaluating the clinical guideline even when it is not possible to automatically extract the biomedical quantity from the patient data.

Abstract: This description provides risk notifications for a geographic region to inform patients and effect behavior changes to prevent rescue events and exacerbations from occurring. A risk analysis for a geographic region is based on data gathered from a population of patients who may experience respiratory events within the geographic region. Rescue medication events, environmental conditions relevant to the rescue medication events, and other contextually relevant information are detected by sensors associated with a population of patients' medicament device/s and are collected from other sources, respectively, to provide a basis to determine a risk score. This data is analyzed to determine the severity of the risk of a respiratory event occurring within a geographical region and accordingly sending a notification to patients within that geographical region.

Abstract: A data processing system for insurance claims analysis and adjudication implements obtaining policy coverage information for each of a plurality of insurance policies and insurance claim information associated with a plurality of insurance claims associated with an insured user, analyzing the insurance claim information using a first machine learning to obtain event-related claim grouping information; analyzing the event-related claim grouping information and the standardized policy information using the second machine learning model to obtain coverage prediction information comprising a prediction, for each event of the one or more events, identifying a respective insurance policy of the plurality of insurance policies likely to cover the one or more claims associated with each event, the second machine learning model being trained using second training data formatted according to the standard schema; and providing, via a network connection, the coverage prediction information to a computing device associate

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a mental health prediction for a patient using a centrality ranking of the brain of the patient. One of the methods includes obtaining brain data of a patient, wherein the brain data comprises, for each of a plurality of pairs of parcellations formed from a set of parcellations where each pair comprises a first parcellation and a second parcellation, data characterizing a number of tracts connecting the first parcellation and the second parcellation; determining a network graph from the brain data; generating, for each of a plurality of nodes in the network graph, a measure of centrality of the node; determining a centrality ranking of the plurality of nodes of the network graph according to the respective measures of centrality; generating a mental health prediction for the patient using the determined centrality ranking.

Abstract: This disclosure relates to a system and method for custom made medical devices life cycle management, the life cycle management of the medical devices include tracking of material and procedure to be used in additive manufacturing of the medical device and analyzing design risk as per the raw material and the design specification. Further, it includes to identify potential hazards based upon the materials, their interactions and effect on process to be followed. An artificial intelligence (AI) enabled model is trained with one or more sample datasets pertaining to a plurality of materials to be used in the additive manufacturing to audit design, to determine materials to be used, and to generate a unique code to be used to label the manufactured medical device. A smart contract of blockchain is configured to record received input and update with new points for regulatory compliance.

Abstract: The present invention provides a hand hygiene system implemented on a computer comprising: a health management program including a cleaning hand module implemented on said computer; and a sanitizer dispenser equipped with a sensor that is connected to said computer; wherein every time the dispenser is used, the computer receives a signal indicating that the dispenser was used, and wherein said health management program entails access to a new/consequent patient file only when receiving said signal, whereby said hand hygiene system makes the performance of hand hygiene an obligatory action in between patients.

Abstract: Systems and methods for awareness machines and artificial intelligence (AI) based community wide care that may monitor one or more event in one or more domain are described. In an embodiment, a community wide care may monitor one or more event in real-time. In another embodiment, a multifaceted community wide care may enhance intelligence and awareness regarding one or more event. A functional performance measure (FPM) is described for the community wide care and the multifaceted community wide care. In an embodiment, one or more FPM for a community wide care may be evaluated to analyze performance and viability of the community wide care. The one or more FPM may be formulated with one or more metric, which may be computed to derive one or more performance indicator for the community wide care.

Abstract: A system for administering a status check to a medical device. The system includes a medical device unit comprising a control system, a writing device, and one or more components, the control system configured to transmit to the one or more components a request for status data regarding a status of the one or more components, receive status data from the one or more components, and write, using the writing device, the status data to a transmitting device, the transmitting device configured to store and transmit the status data.

Abstract: A medical information processing apparatus according to an embodiment includes a processing circuitry. The processing circuitry is configured: to receive an input of first transition data indicating a transition up to the present time of a biological index value exhibiting a condition of a patient and second transition data indicating a transition up to the present time of a treatment means implemented on the patient; to set a goal value of the biological index value as a treatment index value; and to predict a time at which the biological index value of the patient will reach the treatment index value, on the basis of such first transition data and second transition data of one or more past patients different from the patient that have characteristics similar to characteristics of the first transition data and the second transition data of the patient.

Abstract: A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.

Abstract: A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform encodes the biosignals into a vector representation and inputs the vector representation into a patient-specific metabolic model to determine a metabolic state of the patient at a conclusion of the current time period. The patient-specific metabolic model comprises a set of parameter values determined based on labels assigned to the previous metabolic states and a function representing one or more effects of the plurality of biosignals of the personalized metabolic profile. The platform compares the determined metabolic state of the patient to a threshold metabolic state representing a target metabolism.

Abstract: A system and method for recording healthcare information of an individual under care without deleting data, includes a device for capturing person centered data and transmitting an identification signal, a memory for storing device rules, a buffer, a database; and a processor. The processor receives the signal and data, and retrieves the rules from the memory. Based on the rules, the processor determines whether the data is to be stored in the database or the buffer, and based on the data content, it determines the device location, the data recordation time, whether the individual is identified, and the individual's activity, and based on the rules and the location, time, individual identification, and activity, it determines whether the data is to be stored in the database or eliminated from the buffer. The person-centered data may include goal, outcome, medication error data, non-person centered public data, or protected health information (PHI).

Abstract: Described herein are assistant robots that observe signs of core health, health dangers, and/or signs of medical distress in a home or at work. As such, the assistant robots can take actions to prevent dangerous situations, diagnose health problems, respond to requests for help, and provide regular treatments or analysis of a person's medical state. The assistant robots can learn users' habits or be provided with knowledge regarding humans in its environment. The assistant robots develop a schedule and contextual understanding of the persons' behavior and needs. The assistant robots may interact, understand, and communicate with people before, during, or after providing assistance. The robot can combine gesture, clothing, emotional aspect, time, pose recognition, action recognition, and other observational data to understand people's medical condition, current activity, and future intended activities and intents.