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.

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AI model identifies radiologist-recommended follow-up imaging in reports, has potential for widespread use

New data published in the American Journal of Roentgenology details the performance of a deep learning model known as BERT, short for Bidirectional Encoder Representations from Transformers.

Decreasing energy consumption in radiology: How one hospital reduced use and saved big

Energy consumption reduction tactics could decrease greenhouse gas emissions owed to radiology while also saving departments tens of thousands of dollars every year. 

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EHR a key contributing factor to diagnostic errors in radiology, legal claims analysis finds

About 20% of the small sample of EHR-related errors occurred in radiology, informatics experts wrote in JAMA Network Open. 

radiology reporting EHR health record CDS AUC

Lessons learned from 7 years of structured radiology reporting at 1 institution

The University Medical Center Mainz recently surveyed radiologists and referrers to gather feedback on the change. 

Example of the four types of breast tissue density. The density of fibroglandular tissue inside the breast impacts the ability to easily see cancers. Cancers are very easy to spot in fatty breasts, but are almost impossible to find in extremely dense breasts. These examples show craniocaudal mammogram findings characterized as almost entirely fatty (far left), scattered areas of fibroglandular density (second from left), heterogeneously dense (second from right), and extremely dense (far right). RSNA

Experts developed a deep learning model that can estimate breast density

When tested, the model achieved a performance comparable to that of human experts.

Brain imaging artificial intelligence is a primary area of concentration for AI because oif the critical nature of fast detection and treatment for patients. This is an example of the AI applications displayed by third-party advanced visualization vendor TeraRecon at RSNA 2022.

What is the ROI on AI adoption in radiology?

Radiology makes up the vast majority of FDA-cleared AI algorithms, but with minimal or no reimbursement, hospital administrators may ask whether AI’s value justifies its expense.

pulmonary embolism on CT pulmonary angiography

AI work list prioritization tool significantly decreases PE turnaround times

The FDA-approved tool works by reprioritizing CTPA exams to the top of a radiologist’s work list when the scan is positive for PE.

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Follow-up adherence affected by how and when imaging orders are placed

These are factors that healthcare systems can and should control, experts recently suggested in a new JACR paper.

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