Abstract: A computing system can implement a health verification and prediction service by generating an interactive user interface through which users can provide responses to health assertions in one or more trivia sessions. Each of the health assertions can include a correlation value to actual health outcomes based on responses received from individuals a control group and the known health outcomes of those individuals. Based on the responses from the user, the computing system can generate a health profile for the user.

Abstract: Systems and methods for formulating a personalized skincare product for a user. Data inputs reflecting dermal information of the user (e.g., hydration level measurements, oil level measurements, and a photograph of the user's skin reflecting a set of skin concerns) are collected by a computing device and used to determine a set of normalized scores. A skin health data set is generated based on the normalized scores and stored in memory. A skin health metric is determined based on the skin health data set and is stored in memory. The computing device determines, using a machine learning framework, one or more first skincare product formulations based on the user skin health data set. The formulation(s) can be used to manufacture one or more customized skincare products for the user and can be iteratively refined over time, e.g., by collecting additional data from the user over time.

Abstract: A computing system can execute a correlation model for a library of health assertions to configure a correlation value for each health assertion. The correlation value comprises a correlation between knowledge associated with the health assertion and known health outcomes of individuals in a control group who have also provided responses to the health assertions. The computing system provides a health trivia session for users and based at least in part on the performance of the user, generates a morbidity risk profile that is used to classify the user in an underwriting class.

Abstract: Systems and methods for evaluating insulin medicament dosage regimen adherence by a subject are provided. A description of metabolic events the subject engaged in is obtained. Each event comprises a timestamp and a classification that is one of insulin regimen adherent and insulin regimen nonadherent. Events are binned into consecutive time windows on the basis of time to obtain a plurality of subsets. Adherence values are computed. Each adherence value is for a subset and is computed by dividing the insulin regimen adherent events by the total number of events in the subset. Adherence values are combined into a composite value by a process that comprises downweighting a first adherence value, representing a first time window, with respect to a second adherence value, representing a second time window, when the first time window occurs in time before the second time window. The composite value is communicated as a single representation.

Abstract: The present invention provides a novel approach for consumer-driven interaction with sequencing data or genomic information. Sequencing data access, for users with a variety of access and permissions, may be mediated by a central hub. The hub may also facilitate access to the sequencing data for third party software applications. The hub may also provide data analysis or may have access to analyzed data to use such data in providing a user interface for a genome owner or for non-owner secondary users of the system.

Abstract: Aspects of the invention include includes identifying a respective estimated clinical risk score for each of a first group of patients and a second group of patients. An alternative probability estimate is generated using a same set of inputs used to determine each respective estimated clinical risk score. An unreliability of a patient's clinical risk score is determined based at least in part on a feature of the patient and on a difference between the alternative probability estimate and the determined respective estimated clinical risk score.

Abstract: To achieve the foregoing and in accordance with the present invention, systems and methods for workflow management are provided. In some embodiments, a set of data elements, which are extracted from medical information, are received. The elements are bundled according to one or more similar attributes to form work items. Work items are bundles of data elements for which value is to be extracted for a particular user objective. Next the workflows are configured according to event history for the work items, current action, and user context. The work items may then be routed through the workflow based upon the work items' “energy”. Work item energy is the probability and degree to which a next action taken in a workflow to that particular work item will further a user objective.

Abstract: Among other things, unstructured text items are processed to identify elements of the unstructured text items relevant to classification rules of quality metrics applicable to services provided by a healthcare provider with respect to diseases, conditions, or interventions of patients. The classification rules define classifications of patients based on diseases, conditions, or interventions of patients or on aspects of the services provided. Through a user interface, the unstructured text items, the identified elements, and user interface controls for classifying the patients with respect to the classification rules, based on the identified elements, are presented to the user.

Abstract: A risk-based patient monitoring system for critical care patients combines data from multiple sources to assess the current and the future risks to the patient, thereby enabling providers to review a current patient risk profile and to continuously track a clinical trajectory. A physiology observer module in the system utilizes multiple measurements to estimate Probability Density Functions (PDF) of a number of Internal State Variables (ISVs) that describe a components of the physiology relevant to the patient treatment and condition. A clinical trajectory interpreter module in the system utilizes the estimated PDFs of ISVs to identify under which probable patient states the patient can be currently categorized and assign a probability value that the patient will be in each of the identified states. The combination of patient states and their probabilities is defined as the clinical risk to the patient.

Abstract: A computer apparatus to assist diagnosis of a patient including: a memory storing instructions for execution and an output for results of a processor that provides a patient builder and a vertex filter. Where the builder inputs patient data including historical clinical; and open data, and to create a patient clinical object, PCO, graph; and a full patient graph PCOs for each patient. The filter includes: a context builder to build a domain based on a specification and open data; a context-based vertex filter ranking vertices in the full patient graph based on domain affiliation, retaining any vertices with high domain affiliation; and computing a similarity between the patient PCO and other PCOs in the full patient graph using the retained vertices; and a patient ranker ranks the PCOs according to t similarity where the output lists patients similar to the patient to suggest patient diagnoses.

Abstract: A payment processor acting on behalf of a third party administrator to make payment on an adjudicated claim makes a straight through payment to an acquirer for a health care provider's merchant account. Via the authorization process, the payment processor generates an authorization code which is returned to the health care provider on its merchant statement for the payment. Concurrently, the remittance advice from the third party administrator is linked to the same authorization code as the payment by the payment processor which sends the advice in an 835-formatted transmission to the health care provider. Thus, the remittance advice may be linked (reconciled) with the payment on the merchant statement by the common authorization code.

Abstract: A system and method are disclosed for computing a Health Score. Health data and extrinsic data are received that are parameters for computation of the Health Score. The received data can be combined using an algorithm being implemented as code executing in a processor so as to compute the Health Score of the individual wherein parameters comprising one portion of the data interacts with parameters comprising another portion of the data. Further, the computed Health Score is output to an interface of the user device. Information concerning the parameters' interaction are selectively output to the interface that explain which changes in the parameters are significant drivers of the change in the Health Score.

Abstract: A system for providing customized treatment information for behavioral control of dental patients. The system may include a management server, a hospital server, a waiting room terminal, a terminal determination unit, and a treatment room server. The management server may include an arithmetic process unit and a management server extraction unit. The hospital server may include a hospital server transmission/reception unit. The waiting room terminal may include a terminal extraction unit, a terminal output unit, and a terminal determination unit. The treatment room server may include a treatment server extraction unit, a treatment room server output unit, and a treatment room server determination unit.

Abstract: A computer system, computer-implemented method, and computer program product include a processor(s) (executing code) that obtains a data set(s) related to a patient population diagnosed with a medical condition a database(s). The processor(s) identifies common features, generates patterns of the common features, and generates machine learning algorithms based on the patterns to identify presence or absence of the given medical condition in an undiagnosed patient. The processor(s) compiles a training set of data and tunes the machine learning algorithms with the training set of data. The processor(s) integrates the machine learning algorithms into a graphical user interface.

Abstract: An interrogation data management system operates to manage implantable cardiac device interrogation data and reports. In one example the system generates a report including a reading physician note, a summary report, and an implantable cardiac device manufacturer's report. Some embodiments include smart routing, to automatically route interrogation data reports to medical professionals based on predetermined rules. The interrogation data management system interacts with the reading physician to formally review the interrogation data reports and to generate a reading physician note. The reading physician note includes the reading physician's findings/interpretations and conclusions. Other aspects are also described in this disclosure.

Abstract: A system for simulating a vitality metric, the system comprising a computing device, wherein the computing device is configured to retrieve, from a user, a biotic extraction, generate a vitality metric, using a machine-learning model, wherein generating a vitality metric further comprises training a machine-learning model with training data corresponding to measuring biotic parameters present in the biotic extraction data and determining a metric that is a summation of all individual biotic parameters present in the biotic extraction data. Computing device determines a simulated metric, using a simulation machine-learning process, wherein the simulation perturbs a biotic parameter present in the vitality metric, wherein a biotic parameter is an element of numerical data relating to an element of data present in the at least a user biotic extraction. Computing device provides, to a user, a vitality metric and at least a user effort that resulted in a simulated metric.

Abstract: Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine learning to predict micropopulation risk scores and use the micropopulation risk scores to predict a composite risk score. The composite risk score and it associated attributes can be used to provide notifications/messages and provide dynamic content to a mobile app.

Abstract: A computer system supplies a health service using body information measured by a measurement instrument. The computer system includes a computer that includes an analysis unit analyzing memory of a user. The analysis unit analyzes an error pattern based on a history of a matching-determination process for body information transmitted by the measurement instrument and body information input by the user, calculates a first evaluation value for evaluating a cognitive function based on an analysis result of the error pattern, analyzes an action pattern related to an input action of body information of the user, calculates a second evaluation value for evaluating the cognitive function based on an analysis result of the action pattern, and evaluates the cognitive function of the user based on the first evaluation value and the second evaluation value.

Abstract: A notation assistant system has a machine learning system, a notation processing system, a scoring system, and a suggestion system. The machine learning system trains a classifier for rating a factor related to a clinical narrative note that describes a patient's health status. The notation processing system processes a clinical narrative note and the scoring system determines a factor rating, such as a completion score or a clarity score. The scoring system provides the factor rating to an end-user device to display to a user. The notation assistant system is configured to perform a method as a user is entering a clinical narrative note to provide real-time feedback, such as the factor rating. The suggestion system is configured to provide suggestions for modifying the clinical narrative note to improve the rating factor. The notation assistant system applies to patient health conditions such as a health status of a patient's heart for a patient receiving care for congestive heart failure.

Abstract: In various formats and various technologies across the health care systems/locations, there is a huge need to integrate and make it easier for the user to apply all these technologies across the board, without the need to become an IT/software expert or hire one in-house, which is very expensive and duplicative effort for other similar organizations in the health care industry to do the same exact function. This document describes a software system that allows our system (ViTelNet) to design, configure, deploy, and run sophisticated custom applications across a variety of hardware platforms, without writing any code. In one example, we describe a method/system for the telehealth platform. In one example, we describe the development tools for such a platform. Many variations and examples are also presented.