Abstract: A health care information system generates information that describes how different inputs to a model affect the output of the model, by creating a localized model for a given entity, and determining how the output of the localized model for the given entity changes in response to different inputs. The computer system builds the localized model for a given entity based on the model and on data values for the given entity. The computer system inputs one or more different data values for selected input features of the localized model, while data values for the remaining input features of the localized model are fixed to data values for the given entity, and obtains corresponding outputs from the localized model. The results from this localized model for the given entity indicate which of the selected input features have the most impact on the output of the model for that entity.

Abstract: The present invention relates to a method, apparatus, and computer program for predicting precocious puberty for each stage of growing children and adolescents using an artificial intelligence model. The method for providing precocious puberty prediction and solution for each growth stage using artificial intelligence includes: receiving time series physical information on an evaluation subject; classifying a plurality of growth stages based on the input physical information on the evaluation subject, and then extracting physical information corresponding to a normal growth stage among the plurality of growth stages; predicting precocious puberty by inputting the extracted physical information to a trained neural network; and providing a precocious puberty management solution based on the physical information on the evaluation subject when the evaluation subject corresponds to the precocious puberty.

Abstract: A system and method that facilitates the automated replication of electronic medical record information between a patient and a health-care provider (HCP), such as a doctor, pharmacy, drug manufacturer, biologic manufacturer, or medical device manufacturer. The system uses: a cloud-based infrastructure that includes databases, mathematical models, and configuration information; a patient's electronic health record system providing personal data around a patient's individual personal electronic medical record (PEMR); and a server used to coordinate and authenticate the replication of data between the cloud-based infrastructure, the PEMR, and the EMR/EHR system of the HCP. The system provides support and security, such as by geographically distributed data fragmentation, for mobile platforms and web-based platforms and sophisticated mechanisms for the transmission of data between these systems, as well as patient interaction modules to promote patient care.

Abstract: A computer implemented method of creating medical orders includes generating a dashboard display comprising one or multiple visible panels having data corresponding to different respective medical services, receiving a request to create an order in response to user interaction with a first one of the multiple panels, retrieving first medical information as a function of information associated with the panel from which the request was received, and populating a place order panel with the retrieved first medical information.

Abstract: Systems and methods for analyzing computer-readable image data to determine deviations from planogram data are disclosed. Image data of at least one physical display unit is obtained. The image data is annotated by an image processing engine to identify at least one item within the image data. Planogram data of a planogram corresponding to the at least one physical display unit is obtained and the at least one item within the image data is compared to an expected aspect of the at least one item within the planogram data. A user interface is generated including the image data and at least one interface indicator at least partially overlapping with the at least one item in the image data. At least one graphical aspect of the interface indicator is selected based on the comparison between the item within the image data and the item within the planogram data.

Abstract: A system and method that facilitates the automated replication of electronic medical record information between a patient and a health-care provider (HCP), such as a doctor, pharmacy, drug manufacturer, biologic manufacturer, or medical device manufacturer. The system uses: a cloud-based infrastructure that includes databases, mathematical models, and configuration information; a patient's electronic health record system providing personal data around a patient's individual personal electronic medical record (PEMR); and a server used to coordinate and authenticate the replication of data between the cloud-based infrastructure, the PEMR, and the EMR/EHR system of the HCP. The system provides support and security, such as by geographically distributed data fragmentation, for mobile platforms and web-based platforms and sophisticated mechanisms for the transmission of data between these systems.

Abstract: Disclosed is a mobile terminal for controlling including a medical product intended for a single patient, at least one container being provided with an electronic tag including a memory storing first data including at least one data item specific to the medical product, the mobile terminal including: a first barcode reader reading second data on an identification bracelet specific to a patient; a second reader reading the first data; a controller comparing the first data with the second data to obtain a comparison result and validating that the medical product is intended for the patient wearing the identification bracelet based on the comparison result.

Abstract: A computer system includes memory hardware configured to store a predictive analyzer module, and a fallout transaction history record database including multiple historical fallout transaction records each associated with a database entity. Processor hardware is configured to execute instructions including receiving a fallout transaction request, determining a database search type according to at least one error field of the fallout transaction request, and searching, via the predictive analyzer module, to identify at least one of the multiple historical fallout transaction records associated with one or more field values of the fallout transaction request and the at least one error field.

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 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: Systems and methods for providing surgical planning are described in this application. In varying aspects, patient-specific instrument, guides or implants used in the surgical procedure may comprise multiple patient-specific surfaces for mating with the patient's anatomy. The methods preferably include precise manipulation of a patient's anatomy that includes orientation, depth, placement and other criteria for instruments, guides and implants used during a surgical procedure.

Abstract: This disclosure relates generally to a method and system for classification of cognitive load (CL) using data obtained from wearable sensors. The disclosed method uses a multi-modal based approach using wrist-worn sensors for real time monitoring of CL in real world scenarios and improves the accuracy of detection of CL. A set of distinguishing features are selected from physiological signals received from the wrist-worn sensors. These features are used for training a classification model for classifying the CL of a patient into a no load or a high load. The set of distinguishing features are selected from domain specific features and signal property based generic features of the physiological signals. The disclosed method is used for classification of CL in scenarios such as to check how the cognitive load of a candidate varies during interviews, to assess the participants workload during online meetings and so on.

Abstract: Described are web-based clinical data stores; mobile clinician applications, web-based patient portals, and aggregated decision engines comprising: a translation module configured to express aggregated electronic medical records in a format that is human-readable and accessible to an inference engine and/ or a machine learning algorithm; a notation module configured to express relationships between treatment steps to generate human-readable diagrams and to transmit the diagrams as instructions to the inference engine or the machine learning algorithm; an encryption-based decentralized zero-trust de-identification and anonymization module to protect private patient information when releasing records for research purposes outside the treating physician's protected network; an inference engine configured to receive inputs from the translated medical records of a patient and a subset of interlinked treatment steps selected by the treating physician to generate outputs comprising predictions and probabilities; and

Abstract: Various aspects described herein relate to a location-based and population-based health metric processes. In one example, a computer-implemented method for generating one or more predicted health metrics for a location includes receiving a request to assess the one or more health metrics associated with the location, and identifying at least one current or future built, social, or natural environment parameter associated with the location. The method may further include calculating one or more predicted health metrics associated with the location based upon at least one of the current or future built, social, or natural environment parameters associated with the location, monetizing the healthcare costs of the predicted health metrics and displaying the one or more predicted health metrics and costs.

Abstract: Techniques are provided for candidate screening for a target therapy. A plurality of medical codes corresponding to patient billing data are identified. A candidate classification model for a target therapy is generated by selecting a set of medical codes from the plurality of medical codes and assigning a weight to each medical code selected. The candidate classification model is applied to patient billing data for a plurality of patients to generate a set of target therapy scores. The patient billing data includes associations between one or more patients of the plurality of patients and one or more medical codes of the plurality of medical codes. Each target therapy score corresponds to a patient of the plurality of patients, and is calculated based on weights for one or more medical codes associated with the patient.

Abstract: Embodiments of the invention are directed towards systems, methods and computer program products for providing improved eye tests. Such tests improve upon current eye tests, such as visual acuity tests, by incorporating virtual reality, software mediated guidance to the patient or practitioner such that more accurate results of the eye tests are obtained. Furthermore, through the use of one or more trained machine learning or predictive analytic systems, multiple signals obtained from sensors of a testing apparatus are evaluated to ensure that the eye test results are less error-prone and provide a more consistent evaluation of a user's vision status. As it will be appreciated, such error reduction and user guidance systems represent technological improvements in eye tests and utilize non-routine and non-conventional approaches to the improvement and reliability of eye tests.

Abstract: The present disclosure relates to systems and methods for managing a medical supply chain. According to one aspect, a remote medical management environment can include a medical management system intermediary to a plurality of patients and a plurality of medical care providers. The medical management system can communicate with medical dispensing devices configured to dispense medication to patients. Further, the medical management system can communicate with provider devices through which medical care providers can provide information regarding services provided to users or instructions associated with medication use. The medical management system can detect medication supply events based on information communicated by the medical dispensing devices and/or provider devices or based on information stored at the medical management system. Upon detecting a supply/refill event, the medical management system can initiate a supply refill process for supplying medication or medical device(s) to a patient.

Abstract: A patient treatment system includes a communications module coupled to a communication network, an agreement signification device for capturing data indicative of a signature of a patient, a sensor for monitoring parameters associated with medical equipment prescribed for treatment of the patient, and a microcontroller coupled with the communications module and the sensor.

Abstract: The disclosure relates to methods and systems of providing secure remote health data routing for diagnostics, treatment, monitoring, and/or other health data. The system may use an anonymized identification (ID) token that may protect privacy and ensure security. The ID token may be attached with additional data such as electronic medical record (EMR) data. As such, the system may digitize and securely transmit EMR data to appropriate constituents. The system may apply routing rules and routing tables to identify the appropriate constituents. The system may also route the EMR data for storage at a user's personal device, which may include a chip card or a user device. As such, the user's personal device may store an EMR based on the EMR data, including proof of health, such as vaccination, and other health data relating to the user.

Abstract: A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer.

Abstract: An example method includes receiving a first order to conduct a first diagnostic test of a sample from a patient, sending the first order to the first diagnostic testing instrument for input into a queue of the first diagnostic testing instrument, receiving alerts and status of the diagnostic test as testing is performed and for indication of completion of the first diagnostic test, sending the alerts and status of the diagnostic test to a second computing device for notice to a technician, receiving an output of the first diagnostic test, generating a recommendation of follow-on testing to perform based on the output of the first diagnostic test, receiving confirmation to perform the follow-on testing, generating a second order to conduct a second diagnostic test of another sample from the patient, and sending the second order to the second diagnostic testing instrument for input into a queue for the second diagnostic testing instrument.