Abstract: A method of developing a personalized digital model of an anatomy of a person by a computer system comprising a processor arrangement and a communication module. The processor arrangement receives input data on an actual physical condition of the person from the communication module; searches a database of digital models modelling different physical conditions and selects a digital model from said database that closely matches the actual physical condition of the person based on some received input data; personalizes the selected digital model by developing the modelled physical condition of the selected digital model with a physiological development model based on received input data such that the developed modelled physical condition closely resembles said actual physical condition; and generates an output of the personalized digital model of said person on a data storage medium. Also disclosed, a computer program product and computer system for implementing the method.

Abstract: Aspects of this disclosure include a recall notification method, a method for communicating an action to be performed by a device, and a method of operating a relay service by a network device. One embodiment of the recall notification method may comprise periodically transmitting, by a network interface of a medical device implanted in a user's body, a poll request to a recall service endpoint. The method may further comprise receiving, by the network interface in response to the poll request, a recall notification; and in response to receiving the recall notification, directly notifying the user.

Abstract: An artificial intelligence system for generating educational inquiry responses from biological extractions, the system comprising a computing device, the computing device designed and configured to retrieve a biological extraction pertaining to a user, receive, from a third-party device, an educational inquiry, select, based on the educational inquiry, at least a machine-learning process, and generate, using the at least a machine-learning process and the biological extraction, an inquiry response.

Abstract: Disclosed herein are methods for intelligently populating medical compliance forms (MCFs) with at least patient data to meet compliance requirements (e.g., meeting patient data compliance requirements such as HIPAA requirements, as well as compliance requirements concerning patient forms). In particular, methods involve training and deploying machine learning models that can appropriately analyze a wide array of MCFs with varying formats. Advantages of the methods disclosed herein are three-fold: 1) reducing the amount of time and resources that a healthcare provider needs to commit to satisfying compliance requirements and 2) improving patient outcome by more intelligently incorporating data in medical compliance forms, and 3) ensuring meeting of compliance requirements (e.g., HIPAA compliance requirements).

Abstract: The system determines reimbursements to healthcare providers that provide care based on a novel risk-value reimbursement model, wherein the care is based on a patient risk score and the quality of care delivered. The system and method may include the process of obtaining variables of patient health data for a patient; determining a rank order correlation of the variables with an adverse health outcome; determining a risk score for the patient; sorting patient panels based on the risk score for each patient in the patient panel; determining a lower amount of time on a timer for a chart for the patient, in response to the risk score for the patient being higher than other risk scores for other patients in the patient panel; and resetting the timer, in response to an action by a provider.

Abstract: Methods for generating a health management template for a patient condition including retrieving a healthcare template It corresponding to a patient condition, the healthcare template comprising a plurality of data fields, populating the data fields with patient data; and generating an updated healthcare template based on a user input, wherein the user input comprises at least one of a search request or a data option selection are disclosed. Systems are also disclosed.

Abstract: An intravenous (IV) therapy selection system, may include a processor; a memory device to maintain an electronic medical record database comprising a plurality of medical records related to a plurality of patients; a clinician training module to provide training services and credentials to a plurality of clinicians; an IV therapy recommendation module to provide, via a display device, an IV device recommendation based on data descriptive of a first patient and a first clinician; and a placement location recommendation module to provide, via the display device, a recommendation on where to introduce the IV device into the first patient's body based on data descriptive of the first patient and the first clinician.

Abstract: A system for generating an object prioritization list for physical transfer, the system comprising a computing device configured to receive a biological extraction of a user, determine, using the biological extraction, a plurality of urgency metrics, wherein determining the plurality of urgency metrics including training an urgency machine-learning model with training data that includes a plurality of entries wherein each entry correlates biological extraction data to metrics of urgency of object-addressable maladies, and determining the plurality of urgency metrics as a function of the urgency machine-learning model, order, using a first ranking machine-learning process, a plurality of candidate objects as a function of the plurality of urgency metrics, and generate an object prioritization list as a function of the ordered plurality of candidate objects.

Abstract: A model-assisted system for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.

Abstract: A first calculator calculates a first index value quantitatively indicating an imaging failure state for each imaging menu using a first calculation formula having a variable based on the number of times of occurrence of imaging failure for each imaging menu and a variable based on the rate of occurrence of imaging failure for each imaging menu. A first extractor automatically extracts a target menu as an imaging menu to be considered to prevent occurrence of imaging failure based on the first index value. A second extractor extracts a consideration image as a failed image associated with the target menu. A screen output controller generates a conference screen for displaying the target menu and the consideration image. A client terminal displays the conference screen on a display panel.

Abstract: A method, computer program product, and system continually obtain machine-readable data sets related to a patient population diagnosed with a medical condition from one or more databases (different computing nodes in the distributed environment). The processor(s) continually applies a recurrent neural network to the plurality of data sets to machine learn optimal features for classifying patients into multiple categories related to presence or progression of the medical condition. The processor(s) continually generates, based on the machine learned optimal set of features, intermediate features, based on the weightings of a portion of the machine learned optimal set of features, i.e., a model. The processor(s) evaluates the records and classifies records into the categories, based on a current model (model generated in real-time).

Abstract: Devices and methods for mobile monitoring of a drainage catheter are provided. A device may be configured to perform an operation of rendering a user interface. The user interface may be configured to provide an output interface configured to prompt a user to input catheter use information, catheter use information being information related to using the catheter to drain fluid from the patient. The user interface may be configured to provide an input interface for the user to input catheter use information. The device may be further configured to receive catheter use information input by the user through the user interface. The device may be further configured to generate catheter management information based upon the received catheter use information. A method for mobile monitoring of a drainage catheter may include using a processor to perform the steps the device is configured to perform.

Abstract: Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing voice-activated ambulance booking. Initially, a non-medical user initiates a workflow, via a voice command, that enables the user to provide information about a patient that can be utilized to prioritize the booking of ambulances for patients. Upon providing the initial voice command, the workflow is provided, via a user interface of the user device. The workflow comprises a series of questions corresponding to a status of the patient that can easily be answered merely by observing the patient (e.g., is the patient conscious, is the patient breathing, is the patient bleeding). Based on responses provided by the user, an ambulance is requested for the patient. User device vitals including battery charge levels and signal strength can be used to resolve conflicts if multiple requests are made.

Abstract: Described herein are features pertaining to updating patient data based upon input of an EHR user by way of a GUI. The GUI can depict health information that is generated or extracted from the patient data, wherein the health information can include demographic information, vital statistics, etc. of a patient. The GUI receives input with respect to a multidimensional value displayed on the GUI, the input comprising input in a first dimension and input in a second dimension, the multidimensional value comprising a first value and a second value. The first value and the second value are simultaneously modified based upon the input in the first dimension and the input in the second dimension.

Abstract: Personalized treatment plan generation from an inference engine of a system. The system may receive or retrieve data from a plurality of sources and parse the data to generate individual data fields defining characteristics of a patient. The inference engine may process the contents of the individual data fields in relation to rule blocks suggesting certain treatment actions and combinations thereof in response to satisfied conditions. The inference engine may implement machine learning to refine rule blocks and improve functioning of the system to optimize healthcare.

Abstract: Embodiments of the present disclosure relate to the interchange of data in clinically integrated networks (CIN). The interchange system and method described herein relates to the transfer of patient records for switching from one Electronic Health Record (EHR) vendor to another EHR vendor. The interconnection system and method allow for the concatenation of patient records from diverse EHR's for group reporting of clinical quality measures. The interconnection system also allows for transferring of clinical data from any government certified EHR to specific registry. The interconnection system further relates to the enablement of third party products to be used in conjunction with clinic EHR.

Abstract: A method for controlling an information terminal causes a computer of the information terminal to receive, from a case retrieval system, a plurality of similar medical images having a feature quantity of a region of interest and a certain degree of similarity in accordance with the region of interest included in a target medical image, displays a display screen displaying the plurality of received similar medical images on a touch panel display, the display screen including a display region in which at least some of the plurality of received similar medical images are displayed, displays, if selection of a first similar medical image from among the at least some of the plurality of received similar medical images displayed in the display region is detected, the first similar medical image across the display region, and displays, if a swipe operation performed on the first similar medical image is detected, a second similar medical image, which has second highest similarity next to the first similar medical im

Abstract: Provided is a system for the prognostics of the chronic diseases after the medical examination based on the multi-label learning, including a data acquisition module, a data preprocessing module, a basic predicting model constructing module, and a local predicting module. The data acquisition module is configured to acquire physical examination data of a physical examination user. The basic predicting model constructing module is configured to construct a multi-label learning model for a physical examination scenario. The local predicting module includes a local model training unit and a predicting unit. The local model training unit adjusts the basic predicting model into a local predicting model, and solidifies the local predicting model into the local predicting module. The predicting unit outputs a predicted prognostic index for an occurrence of a plurality of chronic diseases, and finally acquires a future expected occurrence time of the chronic diseases.

Abstract: Data that is derived from a medical device connected to or communicating with, a patient monitor mount, is detected by the patient monitor mount. The data is monitored for events associated with the medical device. The patient monitor mount then determines that the monitored event corresponds to an event. The patient monitor mount generates at least one command for a visualization device to change the data displayed on the visualization device. The command is then transmitted to the visualization device. Related apparatus, systems, methods and articles are also described.

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. 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.