Abstract: The present disclosure relates to a method for validating a healthcare review. The method includes: obtaining from an author, a request to post a healthcare review on a staff experience record; analyzing the healthcare review to identify a subject of the healthcare review; ascertaining identity of the author as to how the author is related with the subject; validating the healthcare review based on the identity of the author, at least one of personal health records and electronic medical records corresponding to the author or the subject, and the staff experience record; and determining a review validity score of the healthcare review based on results from the validating of the healthcare review.

Abstract: A non-transitory computer readable medium (26) stores instructions readable and executable by at least one electronic processor (20) to perform a radiology report analysis method (100). The method includes: identifying occurrences of radiology findings (36) and associated uncertainty indicators (38) in a plurality of radiology reports (32); assigning uncertainty scores (40) on a numerical scale to the identified occurrences of radiology findings based on the associated uncertainty indicators; and providing a user interface (UI) (28) on a display device (24) operatively connected with the at least one electronic processor that displays a representation (44) of the uncertainty scores assigned to the occurrences of radiology findings.

Abstract: A method (100) of generating and using one or more radiology analysis tools comprising: providing a labeling user interface (28, 40) on the workstation via which a user creates a labeled dataset by defining label types; receiving a user selection of desired output as at least one of the defined label types; identifying a proposed machine learning (ML) model based on the defined label types and the desired output; providing one or more GUI dialogs (40) presenting the proposed ML model and allowing the user to generate a user-designed proposed ML model (38) from the proposed ML model; training the user-designed ML model using training data comprising at least a portion of the labeled dataset, thereby generating a trained ML model (44); and deploying the trained ML model for an analysis process applied to at least a portion of radiology images and/or radiology reports in.

Abstract: A method (100) of training a machine-learned (ML) image classifier (14) to classify images (30) respective to a set of labels includes: generating image-based labels for the images from the set of labels and image-based label confidence values for the image-based labels by applying the ML image classifier to the images; generating report-based labels for the images from the set of labels and report-based label confidence values for the report-based labels by applying a report classifier (16) to corresponding radiology reports; selecting a training subset of the set of images based on the image-based labels, the report-based labels, the image-based label confidence values, and the report-based label confidence values; assigning a pseudo-label for each image of the training subset which is one of the image-based label or the report-based label for the image; and training the ML image classifier using at least the selected training subset and the assigned pseudo-labels.

Abstract: A non-transitory computer readable medium (26) stores instructions readable and executable by at least one electronic processor (20) to provide statistical analysis on one or more radiology databases (30, 32) in conjunction with a workstation (18) having a display device (24) and at least one user input device (22).

Abstract: An apparatus (10) for generating benchmarking metrics of current operational workflow performance of a hospital department includes at least one electronic processor (20) programmed to: generate a department profile (34) identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile; retrieve current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show, and emergency department (ED) arrival statistics; compute values of one or more key performance indicator (KPI) metrics (40) for the current statistics; generate an executable workflow model (44) for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having variables representing at least patient arrival timeliness, patient no-show, and ED arrival; simulate a best case scenario (50) by executing the workflow model on inputs including the department profile and best case va

Abstract: Methods and systems for correcting an examination report. The methods described herein extract examination and semantic data from an examination report, and identify any discrepancies between the extracted examination data and the extracted semantic data. The methods described herein then receive a resolution strategy regarding how to resolve any identified discrepancies and then resolve any identified discrepancies based on the resolution strategy.

Abstract: A non-transitory computer readable medium (26) stores instructions executable by at least one electronic processor (20) to perform a request resolution method (100). The method includes: intercepting (102) communication requests (31) directed to a radiology department; classifying (104) the communication requests; assigning (106) the communication requests to agent queues of a plurality of agent queues (40) based on at least the classifications of the communication requests; and routing (108) the communication requests assigned to each agent queue to a request resolution agent (50) corresponding to the agent queue.

Abstract: Embodiments described herein relate to generating and using mappings between entities and entity features extracted from pathology and radiology reports. In various embodiments, a radiology report (102) associated with a subject (127) may be analyzed (602) to identify lesion(s); each of the lesion(s) may have lesion attribute(s). Likewise, a pathology report (112) associated with the subject may also be analyzed (604) to identify sample(s) extracted from the subject; each of the sample(s) may have sample attribute(s). Mapping(s) may be generated (606) between the one or more lesions and the one or more samples based at least in part on correlation between the lesion attributes and the sample attributes. Visual output about the radiology report and the pathology report may then be provided (608) simultaneously, and may visually emphasize a correlation between at least one of the lesions and at least one of the samples.

Abstract: Systems and methods for magnetic resonance (MR) examination are provided.