Abstract: A computer implemented method includes receiving an indication of a medical order for a patient, accessing insurance information of the patient, such insurance information identifying a payor, accessing an electronic medical record corresponding to the patient, using a machine learning program trained on properly filled out payor specific prior authorization request to generate a prior authorization request corresponding to the medical order based on data in the electronic medical record, and submitting the generated prior authorization request to the payor for processing.

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 prepopulated 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 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: 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 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: 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 prepopulated 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 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: 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: 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 teleradiology image processing system configured to process radiology read requests and digital radiology image data is disclosed. 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: 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 process radiology read requests and digital radiology image data is disclosed. 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. 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: 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.