Abstract: Methods of extracting bone and/or cellular material from a patient for possible use in a later surgical procedure, which involves that patient or even a different patient, are provided. An embodiment of the method generally comprises extracting bone and/or cellular material from a patient undergoing a routine surgical procedure, saving the material, and then sending it to a storage, separation, and processing facility (e.g., a “biobank”) for use in a later surgical procedure involving that patient, or a different patient. The material may be cancellous bone, cortical bone chips, or bone marrow. The extracted bone and/or cellular material can be processed at the biobank and used in a multitude of different medical procedures (e.g., as an allograft infused with stem cells for use in a fusion procedure).

Abstract: An integrated management server for a dental chart includes a memory storing instructions, and a processor. The instructions cause the processor to obtain a first script for speech of sounds generated during measurement of a patient's periodontal status to reflect the first script in a periodontal chart, provide features extracted from the periodontal chart to a pre-trained failure rate inference model to infer a failure rate for each implant type, obtain a second script for speech of sounds generated during discussions regarding a patient's implant type determined based on the inferred failure rate to reflect at least a portion of the first and second scripts in an implant chart, and obtain a third script for speech of sounds generated during discussions regarding an implant product to be used for the determined patient's implant type to reflect at least a portion of the first to third scripts in a laboratory chart.

Abstract: A system and method for managing defibrillators and for coordinating their use during the SCA event to increase the probability of survival for the person experiencing sudden cardiac arrest. Defibrillator monitoring units and direction prompters are installed at registered geographical locations. A defibrillator monitoring unit closest to a cardiac arrest event is identified and a subset of the one or more client devices proximate event are sent an assistance request. The defibrillator monitoring unit may be within a defibrillator cabinet and configured to monitor the status of the defibrillator. The defibrillator cabinet may be within a private residence, such as incorporated into a home appliance. The system may determine whether the defibrillator will be made available and, if so, direct a responder to the private residence to obtain the defibrillator. Information regarding the responder may be sent to the defibrillator monitoring unit.

Abstract: Methods, non-transitory computer-readable media, and devices are disclosed that identify and validate a signature for predicting relative benefit of two therapies for frontline and therapy sequencing for patients. Described and illustrated by way of the example herein are machine learning approaches for gaining data-driven insights from the mutational landscape in metastatic pancreatic adenocarcinoma (mPDAC) and validating the signature in predicting relative benefit from FOLFIRINOX (FFX) and Gemcitabine/nab-Paclitaxel (GA) therapies. This technology inputs a patient's genomics findings and clinical data and generates predictions of relative effectiveness for the two distinct FFA and GA chemotherapy options. The predictions for an individual patient provide personalized guidance on treatment sequencing to improve patient health outcomes.

Abstract: One example method includes receiving, from a DCF (data confidence fabric) including a node that comprises a data source of an edge environment, a confidence score concerning a data stream associated with the data source, correlating the confidence score to a compliance level of the edge environment with regard to a specified requirement for the edge environment, generating, based on the confidence score and compliance information, an audit report that identifies a connection between the confidence score and the compliance level, and for a value of a performance gap between the compliance level and a required compliance level, associated with the specified requirement, that meets or exceeds a threshold, identifying a remedial action which, when implemented in the edge environment, causes a reduction of the performance gap to a value below the threshold.

Abstract: Techniques for generating and updating electronic records of rescue events are described. In a rescue scene, a medical device monitors and/or treats a patient. An example tracking device determines an identifier of the medical device by analyzing a signal corresponding to a medical device. In addition, the tracking device identifies a physiological parameter of the patient. The tracking device generates an electronic record of the patient that includes the identifier and the physiological parameter. The tracking device further outputs the electronic record.

Abstract: Endovascular variable aortic control catheters (EVACC) are provided that are adapted to augment upstream blood pressure and regulate downstream blood flow for patients in shock. The EVACC devices provide improved treatment for truncal wounds, which may be used for example on a battlefield, thereby increasing survivability of injured soldiers. The devices are a catheter-based system having a proximal hand piece for controlled deployment of the device through a delivery sheath. A collapsible, wire framework supports an expandable and collapsible occlusion barrier. The wire basket and occlusion barrier expand to fit within the lumen of the aorta. Various movable elements are used to adjust an adjustable passageway to regulate controlled anterograde blood flow.

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: 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 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: 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: 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.