Abstract: Systems and methods are provided for monitoring prescription relationships using patient data received from a plurality of patient data providers, healthcare services data relating to the patient data, and data relating to physician prescription records to determine relationships among the parties and aggregate de-identified data for presentation of a visual representation of patient data and related prescription relationships and behaviors.

Abstract: Systems and methods are provided for monitoring consistency in a drug prescription system using prescription drug data and prescription drug treatment plan data relating to a plurality of patients received from a plurality of patient data providers, and using a machine learning module to compare treatment plans among the prescription drug treatment plan data, using simulations of one of one or more drugs and one or more treatment plans, among the prescription drug data and prescription drug treatment plan data.

Abstract: Systems and methods are provided for characterizing the activities of one or more patients in a health care system using an interception module for retrieving prescription drug data relating to the one or more patients, a correlation module that ensures that the prescription drug data is associated with the correct records of the one or more patients, and an analytics module that determines whether prescription ordering patterns for the one or more patients and indicates whether a subset of the ordering patterns is anomalous as compared with a stored ordering criterion.

Abstract: Systems and methods are provided for characterizing the activities of one or more physicians in a drug prescription system using an interception module for retrieving prescription drug data, an interaction module identifying data associated with each sales and service representative with whom the one or more physicians have interacted, identifying prescription orders, and using an analytic engine that associates the prescription drug data, data associated with each sales and service representative, and the orders with correct records for the plurality of physicians.

Abstract: Systems and methods are provided for simulating a patient health state by determining one or more relationships within patient data, creating enriched data elements based on the determined relationships, and using a machine learning module to compute a current health state for a patient and classifying the patient within a population of patients, wherein the population of patients is determined based on one or more of lifestyle, diagnosis and/or prognosis, and present or previous healthcare treatments as categorized using the machine learning module.

Abstract: Systems and methods are provided for simulating a patient health state by determining one or more relationships within patient data, creating enriched data elements based on the determined relationships, and using a machine learning module to simulate a future health state of the patient and predict a future medical service need.

Abstract: Systems and methods are provided for detecting a patient's misuse of a controlled medication of a patient using lab test results and patient attributes to generate a usage profile that is used to determine whether the usage profile is indicative of potential misuse of the controlled medication based on one or more features of the usage profile and, in response to determining potential misuse of the controlled medication, transmitting a notification that indicates the potential misuse by the patient.

Abstract: Systems and methods are provided for identifying patient overdose risk using patient data including at least one of drug toxicology data, metabolite data, patient-reported symptoms, patient prescriptions, and patient adverse events, and using a machine learning module to compute a patient drug profile based on the patient data and at least one patient population drug profile and calculating an overdose risk score for a patient within the ingested patient data, wherein the overdose risk score is based at least in part on an association between a patient drug profile and a population drug profile.

Abstract: Systems and methods are provided for creating and using healthcare patients' digital twins based on data related to a patient, and a plurality of patient populations, to monitor and manage healthcare services by forming a digital twin of individual patients based on their health information, where the digital twin of a patient is a digital representation of at least one health state of the patient.

Abstract: Systems and methods are provided for healthcare data management using a machine learning module to determine patterns related to effects of health behaviors and treatments, forming digital twins of a patient and a population of patients, and presenting the digital twin of said individual patient, the digital twin of said population of patients, and data based on associations among the patient and health data and the patient population health data.

Abstract: Systems and methods are provided for simulating patient risk using patient data relating to a plurality of patients received from patient data providers, determining relationships among the data, receiving simulation instructions that are indicative of one or more drug treatment plans, and using a machine learning module to simulate the one or more drug treatment plans and evaluate the efficacy of the plans.

Abstract: Systems and methods are provided for simulating a patient health state by determining one or more relationships between patient data and historical data, creating enriched data elements based on the determined relationships, and using a machine learning module to compute a current health state for a patient and to simulate a future health state of the patient.

Abstract: Systems and methods are provided for using a machine learning device to receive demographic records, diagnosis records, prescription records, and testing records from a plurality of healthcare databases, from a plurality of healthcare providers, to identify inconsistencies in the names and/or codes used by the healthcare providers.

Abstract: Systems and methods are provided for monitoring insurance billing events using patient data, healthcare services data, insurance reimbursement criteria and insurance reimbursement records to model and calculate an insurance reimbursement score for a future planned health service event using an analytic engine to calculate an insurance reimbursement score for a future planned health service event based at least in part on a comparison to a plurality of calculated insurance reimbursement scores.

Abstract: Systems and methods are provided for determining drug dispensing consistency based on using patient data from at least one healthcare provider that includes at least one of drug toxicology data, metabolite data, patient-reported symptoms, and patient prescriptions, computing relationships among the data, transmitting an enriched data set to a machine learning module, and using the machine learning module to analyze the enriched data set to determine if a patient metabolite reported in a toxicology test is consistent with a known patient prescription.

Abstract: Systems and methods are provided for relationship discovery of physician prescription behavior using patient data received from a plurality of healthcare data providers and physician data from a plurality of physician practices, determining one or more relationships between the patient data and physician data, wherein an enriched data set includes determined relationships with supplemental reference data, and generating an alert based at least in part on an indicator of aberrant prescription activity among the determined one or more relationships within the enriched data set.

Abstract: The disclosed embodiments relate to a system that processes requests to access data item at an edge device in a network. During operation, the system receives the request to access the data item from a user, wherein the request is received at the edge device, and wherein the edge device communicates with an origin server, which stores data items. If the request includes a token identifier, the system uses the token identifier to look up a token payload in a token cache at the edge device. The system then decorates the request with fields from the token payload, and analyzes the decorated request to determine whether the user is authorized to access the data item. If the user is authorized to access the data item, the system looks up the data item in a content cache at the edge device, and returns the data item to the user to complete the request.

Abstract: A computerized method for healthcare data management generally includes receiving a symptom report from an individual, and receiving, at a healthcare data system computing device, continuous health information including data related to an individual's health state and data related to a health state of a population of patients; calculating, at the healthcare data system computing device, a risk score, wherein the risk score is based on at least the individual's symptom report, on data related to an individual's health state, and on the health state of a population of patients, wherein the population of patients share an attribute with the individual; and sending, from the healthcare data system computing device, a return-to-work recommendation to an entity based at least in part on the calculated risk score.

Abstract: A computerized method for healthcare data management generally includes forming, using the healthcare data system computing device, a digital twin of the individual patient based on the health information related to the individual patient, the digital twin of the individual patient being a digital representation of at least one health state of the individual patient; forming, using the healthcare data system computing device, a digital twin of the population of patients based on the health information related to the population of patients, the digital twin of the population of patients being a digital representation of at least one health attribute of the population of patients; and presenting, at the healthcare data system computing device, to a user of the healthcare data system the digital twin of the patient and the digital twin of the population of patients.

Abstract: The present disclosure generally includes methods and systems for recommending a lab test for a patient is disclosed. The method includes obtaining a proposed prescription for the patient from an external data source. The method further includes obtaining patient attributes for the patient, including a diagnosis of the patient. The method also includes determining whether to recommend one or more different lab tests for the patient prior to the patient beginning the proposed prescription based on the proposed prescription and the patient attributes, and providing a testing recommendation to a customer relationship management system, wherein the testing recommendation indicates the one or more different tests that are recommended for the patient in response to determining to recommend one or more different lab tests for the patient, wherein the customer relationship management system transmits the testing recommendation to a healthcare system of a clinic of the patient.