Enterprise Imaging

Enterprise imaging brings together all imaging exams, patient data and reports from across a healthcare system into one location to aid efficiency and economy of scale for data storage. This enables immediate access to images and reports any clinical user of the electronic medical record (EMR) across a healthcare system, regardless of location. Enterprise imaging (EI) systems replace the former system of using a variety of disparate, siloed picture archiving and communication systems (PACS), radiology information systems (RIS), and a variety of separate, dedicated workstations and logins to view or post-process different imaging modalities. Often these siloed systems cannot interoperate and cannot easily be connected. Web-based EI systems are becoming the standard across most healthcare systems to incorporate not only radiology, but also cardiology (CVIS), pathology and dozens of other departments to centralize all patient data into one cloud-based data storage and data management system.

University of Rochester Medical Center in the US selects Sectra Enterprise Imaging in the cloud

Linköping, Sweden and Shelton, CT – October 17, 2022– International medical imaging IT and cybersecurity company Sectra (STO: SECT B) will provide enterprise imaging as a cloud subscription service (Sectra One Cloud), throughout the University of Rochester Medical Center (URMC). This will allow the US health system scalability as enterprise imaging volumes grow, in a secure and fully managed cloud environment.

October 19, 2022
An example of commercially available artificial intelligence (AI) automated grading of breast density on mammograms from the vendor Densitas..

VIDEO: Role of AI in breast imaging with radiomics, detection of breast density and lesions

Connie Lehman, MD, chief of breast imaging, co-director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital, discusses how artificial intelligence (AI) is being implemented in breast imaging.

October 18, 2022

What is the radiologist's role in variations of prostate cancer detection?

Prior studies have focused on radiologist performance rather than patient outcomes, leaving the topic of variable diagnoses and what factors impact them—race, ethnicity, age, biopsy type, etc.—open for debate. 

October 17, 2022
Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, explains some of technology trends KLAS researchers have found in enterprise imaging system and radiology artificial intelligence (AI).

VIDEO: KLAS shares trends in enterprise imaging and AI

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, explains some of technology trends KLAS researchers have found in enterprise imaging system and radiology artificial intelligence (AI).

October 13, 2022
Charles E. Kahn, Jr., MD, MS, Editor of the the journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine. He has been heavily involved in radiology informatics and has seen up close the evolution of radiology toward deeper integration with AI. #RSNA

VIDEO: Use cases and implementation strategies for radiology artificial intelligence

Charles Kahn, Jr., MD, editor of the the journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, explains the work involved integrating AI in radiology systems and the role of AI in augmenting patient care.
 

October 12, 2022
Charles E. Kahn, Jr., MD, MS, editor of the the RSNA journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine. He discusses the need to validate artificial intelligence (AI) algorithms with your own patient population to determine if it is accurate for a specific institutions patients. He also explains how bias can be inadvertently added into a algorithm, and how the AI may take learning shortcuts. #AI

VIDEO: Assessing radiology AI and understanding programatic bias 

Charles E. Kahn, Jr., MD, MS, editor of the the RSNA  journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, discusses the need to validate AI algorithms with your own patient population data.  

October 11, 2022
EHR-EMR

Specialty-specific workflow training could increase radiologist satisfaction with EHRs

Clinicians who receive specialty-specific workflow training are 24 times more likely to agree that the EHR meets their functionality needs, according to a new KLAS report.

September 29, 2022
Monique Rasband from KLAS Research shares trends in PACS and radiology informatics.

VIDEO: 6 key trends in PACS and radiology informatics observed by KLAS

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, shares some of technology trends observed in radiology PACS and and imaging informatics since 2019.

September 29, 2022

Around the web

Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans. 

"Gen AI can help tackle repetitive tasks and provide insights into massive datasets, saving valuable time," Thomas Kurian, CEO of Google Cloud, said Tuesday. 

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