Abstract: A system and method for processing a plurality of medical images using a plurality of clinical applications. A current study is received by a first server, the current study having first image series metadata. The first server can determine, based on several different techniques, that the current study is in progress. The system and method generates an assembled study set comprising the current study that is processed using a clinical application, the current study having a first series and a second series.

Abstract: Systems and methods for medication fillings management are disclosed. For example, user information and/or refill schedules associated with users may be utilized to predict which users are likely to miss a medication refill. The presently disclosed systems and methods may determine health indicators associated with users and, by utilizing one or more machine learning techniques, determine which health indicators, alone or in combination, are associated with users prone to missing refills. As such, the system may predict users that are likely to miss a refill and may generate preemptive, targeted refill reminders in order to prevent the missed refill before it occurs.

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: In an epidemiology transmission probability analysis for a medical facility, ray origin points are distributed over a medical facility floor map. Rays are cast from the ray origin points. The cast rays stop upon encountering a physical barrier mapped in the medical facility floor map. An infectious transmission probability map is computed from intersections of the cast rays. At least a portion of the medical facility floor map is displayed on a display, overlaid with the infectious transmission probability map. The distributing, the casting, and the computing are suitably performed by one or more electronic processors.

Abstract: Methods and systems are provided for an online system for patient control of a medical prescription. In one example, a method is provided for transmitting a prescription to a first pharmacy in response to a patient selecting the first pharmacy for a first fill of the prescription. The method may further include transmitting the prescription to a second pharmacy in response to the patient selecting the second pharmacy, different than the first pharmacy, for a second fill of the prescription.

Abstract: Introduced here is a method, a system, a device, etc. for displaying health-related data (e.g., blood glucose measurements) relevant to user's selection. The method, the system, the device, etc. can be based on: receiving healthcare-related data from data streams associated with a profile of a patient; identifying, based on a least a subset of the data streams, a time window corresponding to a contextual event indicative of a patient activity that has a potential of affecting metabolism of the patient; determining a graphical representation of the contextual event; generating a graph of blood glucose level over time for display; detecting a user indication of a user interest in a time point along the graph of the blood glucose level; and highlighting the graphical representation of the contextual event in response to the time point falling within the identified time window.

Abstract: A term base can be used to index a raw medical term (e.g., a text string from an electronic health record) to generate a computational expression of the raw medical term. The computational expression of the raw medical term can be used to generate a uniform medical term for the raw medical term. The computational expression can be compared with other computational expressions to organize the electronic health record, filter the electronic health record, and provide a uniform presentation of raw medical terms from different events in the electronic health record.

Abstract: Provided is a method, computer program product, and system for hashing electronic health records. A processor may collect a set of electronic health records (EHRs). The processor may perform an encounter analysis on the set of EHRs to determine a set of attributes associated to the set of EHRs. The processor may hash the set of attributes to generate one or more hashing indexes that correspond to the set of EHRs. The processor may store the one or more hashing indexes in a list used for document retrieval.

Abstract: A computer-implemented method includes displaying a number of questions on a stack of overlapping graphical objects that dynamically indicates a status of the number of remaining questions, determining an output based on answers input by a user, and displaying an indication of the output.

Abstract: A patient support system for providing improved guidance and/or troubleshooting with respect to a patient support apparatus. A user interface is configured to receive inputs from a user, and an information output device is configured to provide instructions to the user. A controller determines a guidance protocol for the user based on the inputs. The guidance protocol comprises user-performed actions to be performed by the user in response to the instructions provided to the user with the information output device. The guidance protocol may be initiated and/or determined based on a troubleshooting request and/or an uncorrelated sequence of user inputs. At least one of the user-performed actions may be configured to control operational functions of a patient support apparatus. Methods for improving patient care by providing the guidance and/or troubleshooting are also disclosed.

Abstract: A method may include identifying a shift associated with a clinician by applying a machine learning model trained to identify, based on a series of transaction records associated with the clinician, one or more shifts associated with the clinician. The clinician may be identified as likely to engage in a hazardous behavior based at least on the shift associated with the clinician. In response to determining that the clinician as likely to engage in the hazardous behavior, activating a protective workflow. The protective workflow may be configured to prevent the clinician from engaging in the hazardous behavior as well as to collect evidence associated with the hazardous behavior. Related methods and articles of manufacture are also disclosed.

Abstract: Methods, systems, apparatus, including computer programs encoded on computer storage media for generating psychiatric treatment recommendations. In one aspect, the method includes actions of receiving, by a server and from a first user device, one or more data structures that collectively include fields structuring data that represents (i) current patient symptoms and (ii) current patient diagnoses, generating, by the server, rendering data structure that includes fields structuring data that represents rendering data that, when rendered on a display device, presents a patient dashboard based on the data that is received from the mobile device, and providing, by the server, the rendering data structure to a second user device that is different than the first user device, wherein the second user device is configured to render the rendering data structured by the fields of the rendering data structure to output the patient dashboard on the display of the second user device.

Abstract: A system, method, and device for medical data collection are disclosed. An example portable device includes a camera configured to record images, a memory storing the recorded images, and a display interface for displaying the images. The portable device also includes a processor configured to instruct the camera to record at least one of the images and extract medical data from the at least one image using an optical character recognition routine. The example processor is further configured to establish a connection with a clinician computer such that the portable device is recognized by the clinician computer as a keyboard. Moreover, the example processor is configured to transmit at least some of the extracted medical data to the clinician computer as a keyboard input to the clinician computer.

Abstract: Computer-implemented methods and systems are provided for managing one or more images. An example method for indexing involves operating a processor to, divide an image portion of an image of the one or more images into a plurality of patches, for each patch of the image: detect one or more features of the patch; and assign the patch to at least one cluster of a plurality of clusters of the image, based on the one or more features of the patch. The processor is operable to, for each cluster of the image, select at least one patch from the cluster as at least one representative patch for the cluster; for each representative patch, generate an encoded representation based on one or more features of the representative patch; and generate an index identifier for the image based on the encoded representations of the representative patches of the image.

Abstract: A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

Abstract: A medical system according to an embodiment includes a medical apparatus and a first terminal. The medical apparatus is configured to have medical information therein. The first terminal is configured to be communicably connected to the medical apparatus so as to be able to transmit and receive the medical information thereto and therefrom, to have a function of an access point, and to cause a display to display a first code containing identification information of the access point. The first terminal is configured to be communicably connected to a second terminal having read the first code so as to be able to transmit and receive medical information thereto and therefrom.

Abstract: Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.

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: 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: Embodiments of the present disclosure provide a method and apparatus for determining a psychological counseling training scheme. The method includes: obtaining training feeling data of a user after each session of psychological counseling training through an interactive inquiry with the user; inputting the training feeling data into a first classification model, identifying a training result of the user after each session of psychological counseling training by using the first classification model, and collecting statistics about a training result of the user in a current training period; and determining a training scheme of the user for a next training period based on the training result in the current training period of the user. The invention resolves the problem that a counseling result is not ideal because individual demands of different users cannot be satisfied if a psychological robot performs psychological counseling on the user according to a preset counseling procedure.