Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a normalization module for normalizing hanging protocols for displaying medical images. The normalization can be executed as a function of similar image characteristics shared between multiple sequences of medical images.

Abstract: Enhanced techniques for the extraction and use of metadata from medical images are disclosed herein. Based on the information in the metadata, specific processing may be performed within an image order management system, radiology information system (RIS), or like system. A radiology read order may be created, pre-populated, and transmitted via a processing system (e.g., a teleradiology image order management system) based on the metadata within the radiology image. For example, this metadata may exist within the header of a DICOM-formatted image data file or a DICOM communication protocol transmission. The processing system may then provide the pre-populated read order back to the source of the medical images for verification and submission. Other processing actions may also occur based on information extracted from the image metadata, such as custom workflows and handling based on an originating facility, or transferring the images to a particular radiologist or location.

Abstract: A system for managing remote doctor medical request workflow may include a workflow module that optimizes assignments of medical requests to remote doctors based on parameterized doctor and scheduling information and may further include a forecasting module that predicts the hospital credentials, state licenses or doctors needed to fulfill a projected volume of future medical requests. In one embodiment, radiologists are parameterized and then matched with requests for radiological readings based on information extracted from DICOM image headers and merged with associated information contained in a medical work order. In this embodiment, the radiologists are parameterized based on their locations, schedules, hospital credentials, state licensing, compensation metrics, and performance metrics and incoming requests for review of CT scans and the like are filtered based on the parameterized radiologist information to identify one or more radiologists who are to fulfill the medical request.

Abstract: A system configuration and techniques for optimizing schedules and associated use predictions of a multiple resource planning workflow are disclosed herein, applicable to environments such as radiologist scheduling in a teleradiology workflow. In one embodiment, a series of computing engines and components are provided to allow detailed forecasting and the generation of customized recommendations for scheduling and other resource usage scenarios. This forecasting can factor resource efficiencies, changes in resource demand volume, resource specialties, resource usage preferences, expected future events such as the removal or addition of resources at future times, and other resource availability or usage changes. The forecasts may be further enhanced through the use of historical data models and estimated future data models. Additionally, a calendar and other tools may be presented through a user interface to allow forecast and scenario customization based on selection of a series of future dates.

Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a manifest generator for generating a manifest of medical images. The manifest can be used to determine an ordering or a layout of the medical images generated by the modalities, optionally as a function of the modality type, anatomical area, and other variables. In some embodiments, the various medical images and metadata may be received at an image order management system that parses the metadata and assembles the metadata into the manifest files that may be transmitted independently to remote interpretation sites, which in turn may be equipped with image viewer applications that analyze the manifest files and determine a rearranged ordering and/or grouping of the medical images, wherein the rearrangement is executed as a function of modality, anatomy, orientation and other variables.

Abstract: Enhanced techniques for the extraction and use of metadata from medical images are disclosed herein. Based on the information in the metadata, specific processing may be performed within an image order management system, radiology information system (RIS), or like system involved with healthcare imaging. In one specific embodiment, a radiology read order may be created, pre-populated, and transmitted via a processing system (e.g., a teleradiology image order management system) based on the metadata within the radiology image. For example, this metadata may exist within the header of a DICOM-formatted image data file or a DICOM communication protocol transmission. The processing system may then provide the pre-populated read order back to the source of the medical images for verification and submission.

Abstract: A system for managing remote doctor medical request workflow may include a workflow module that optimizes assignments of medical requests to remote doctors based on parameterized doctor and scheduling information and may further include a forecasting module that predicts the hospital credentials, state licenses or doctors needed to fulfill a projected volume of future medical requests. In one embodiment, radiologists are parameterized and then matched with requests for radiological readings based on information extracted from DICOM image headers and merged with associated information contained in a medical work order. In this embodiment, the radiologists are parameterized based on their locations, schedules, hospital credentials, state licensing, compensation metrics, and performance metrics and incoming requests for review of CT scans and the like are filtered based on the parameterized radiologist information to identify one or more radiologists who are to fulfill the medical request.

Abstract: In one example, a two points on a medical image can be identified. In this example, three or more anatomical features can be identified and labeled. For example, these labels can be displayed with other medical images that show the same anatomical features. For example, points in the L1 and L5 vertebrae can be identified, and labels for the L1-L5 vertebrae can be displayed.

Abstract: The subject matter of this specification can be implemented in, among other things, a system for interfacing with multiple medical imaging modalities that includes a normalization module for normalizing hanging protocols for displaying medical images. The normalization can be executed as a function of similar image characteristics shared between multiple sequences of medical images.

Abstract: In one embodiment, an image processing system can be configured to generate and adjust a radiological image. Upon receiving input via a user interface control indicating a specified adjustment in slab thickness of the radiological image, and upon receiving input via a user interface control indicating a specified reconstruction technique, the image processing system can generate an adjusted radiological image based on the received input, and can present the image on a display.

Abstract: A system for managing remote doctor medical request workflow may include a workflow module that optimizes assignments of medical requests to remote doctors based on parameterized doctor and scheduling information and may further include a forecasting module that predicts the hospital credentials, state licenses or doctors needed to fulfill a projected volume of future medical requests. In one embodiment, radiologists are parameterized and then matched with requests for radiological readings based on information extracted from DICOM image headers and merged with associated information contained in a medical work order. In this embodiment, the radiologists are parameterized based on their locations, schedules, hospital credentials, state licensing, compensation metrics, and performance metrics and incoming requests for review of CT scans and the like are filtered based on the parameterized radiologist information to identify one or more radiologists who are to fulfill the medical request.

Abstract: A system for managing remote doctor medical request workflow may include a workflow module that optimizes assignments of medical requests to remote doctors based on parameterized doctor and scheduling information and may further include a forecasting module that predicts the hospital credentials, state licenses or doctors needed to fulfill a projected volume of future medical requests. In one embodiment, radiologists are parameterized and then matched with requests for radiological readings based on information extracted from DICOM image headers and merged with associated information contained in a medical work order. In this embodiment, the radiologists are parameterized based on their locations, schedules, hospital credentials, state licensing, compensation metrics, and performance metrics and incoming requests for review of CT scans and the like are filtered based on the parameterized radiologist information to identify one or more radiologists who are to fulfill the medical request.

Abstract: A system configuration and techniques for optimizing schedules and associated use predictions of a multiple resource planning workflow are disclosed herein, applicable to environments such as radiologist scheduling in a teleradiology workflow. In one embodiment, a series of computing engines and components are provided to allow detailed forecasting and the generation of customized recommendations for scheduling and other resource usage scenarios. This forecasting can factor resource efficiencies, changes in resource demand volume, resource specialties, resource usage preferences, expected future events such as the removal or addition of resources at future times, and other resource availability or usage changes. The forecasts may be further enhanced through the use of historical data models and estimated future data models. Additionally, a calendar and other tools may be presented through a user interface to allow forecast and scenario customization based on selection of a series of future dates.

Abstract: A teleradiology image processing system configured to receive, process, and transmit radiology read requests and digital radiology image data is disclosed herein. In one embodiment, a radiology processing system includes a series of processing components configured to receive digital radiology data from a medical provider, extract relevant information and radiology scan images from the digital radiology data, and initiate and control a workflow with a qualified remote radiologist who ultimately performs a read of the radiology scan images.

Abstract: Enhanced techniques for the extraction and use of metadata from medical images are disclosed herein. Based on the information in the metadata, specific processing may be performed within an image order management system, radiology information system (RIS), or like system involved with healthcare imaging. In one specific embodiment, a radiology read order may be created, pre-populated, and transmitted via a processing system (e.g., a teleradiology image order management system) based on the metadata within the radiology image. For example, this metadata may exist within the header of a DICOM-formatted image data file or a DICOM communication protocol transmission. The processing system may then provide the pre-populated read order back to the source of the medical images for verification and submission.

Abstract: A system for managing remote doctor medical request workflow may include a workflow module that optimizes assignments of medical requests to remote doctors based on parameterized doctor and scheduling information and may further include a forecasting module that predicts the hospital credentials, state licenses or doctors needed to fulfill a projected volume of future medical requests. In one embodiment, radiologists are parameterized and then matched with requests for radiological readings based on information extracted from DICOM image headers and merged with associated information contained in a medical work order. In this embodiment, the radiologists are parameterized based on their locations, schedules, hospital credentials, state licensing, compensation metrics, and performance metrics and incoming requests for review of CT scans and the like are filtered based on the parameterized radiologist information to identify one or more radiologists who are to fulfill the medical request.

Abstract: A system for managing remote doctor medical request workflow may include a workflow module that optimizes assignments of medical requests to remote doctors based on parameterized doctor and scheduling information and may further include a forecasting module that predicts the hospital credentials, state licenses or doctors needed to fulfill a projected volume of future medical requests. In one embodiment, radiologists are parameterized and then matched with requests for radiological readings based on information extracted from DICOM image headers and merged with associated information contained in a medical work order. In this embodiment, the radiologists are parameterized based on their locations, schedules, hospital credentials, state licensing, compensation metrics, and performance metrics and incoming requests for review of CT scans and the like are filtered based on the parameterized radiologist information to identify one or more radiologists who are to fulfill the medical request.

Abstract: A system for managing remote doctor medical request workflow may include a workflow module that optimizes assignments of medical requests to remote doctors based on parameterized doctor and scheduling information and may further include a forecasting module that predicts the hospital credentials, state licenses or doctors needed to fulfill a projected volume of future medical requests. In one embodiment, radiologists are parameterized and then matched with requests for radiological readings based on information extracted from DICOM image headers and merged with associated information contained in a medical work order. In this embodiment, the radiologists are parameterized based on their locations, schedules, hospital credentials, state licensing, compensation metrics, and performance metrics and incoming requests for review of CT scans and the like are filtered based on the parameterized radiologist information to identify one or more radiologists who are to fulfill the medical request.