Informatics

The goal of health informatics systems is to enable smooth transfer of data and cybersecurity across the healthcare enterprise. This includes patient information, images, subspecialty reporting systems, lab results, scheduling, revenue management, hospital inventory, and many other health IT systems. These systems include the electronic medical record (EMR) admission discharge and transfer (ADT) system, hospital information system (HIS), radiology picture archiving and communication systems (PACS), cardiovascular information systems (CVIS), archive solutions including cloud storage and vendor neutral archives (VNA), and other medical informatics systems.

Example of a cardiovascular information system (CVIS) cath lab reporting module with a coronary tree model that will auto complete sections of the report based on how the cardiologist modifies the model. Image from the ScImage booth at ACC 2022. Photo by Dave Fornell

VIDEO: 4 key trends in cardiovascular information systems, according to Signify Reseach

Signify Research shares the latest big trends in cardiovascular IT systems, including the role of EMR cardiology modules vs. third-party CVIS, structured reporting, integration into enterprise imaging and inclusion of ambulatory surgical centers. 

February 1, 2023
I stack of more than 300 computers with cyberattack infected hard drives at Sky Lakes Medical Center, Oregon, discusses how the hospitals IT team overcame a ransomware attack in 2020 during the height of COVID that took down their entire network and how radiology recovered within two weeks.. 

VIDEO: How radiology was restored after a ransomware attack at Sky Lakes Medical Center in Oregon

John Gaede, director of information systems, Sky Lakes Medical Center, Oregon, discusses how the hospital's IT team overcame a ransomware attack in 2020 and restored radiology in about two weeks.

January 23, 2023
EHR-EMR

How EHR 'choice architecture' for imaging could be wasting time and money

When choosing and implementing an electronic health record system, it is important to consider how the system’s architecture might affect providers’ decision-making. 

January 10, 2023
Example of AI automated detection and highlighting of critical lung findings on a chest X-ray for a possible lung cancer nodule and fibrosis. Example shown by AI vendor Lunit.

VIDEO: Radiology AI trends at RSNA 2022

Sanjay Parekh, PhD, senior market analyst with Signify Research, discusses trends in radiology AI seen on the expo floor and in sessions at RSNA 2022.

December 12, 2022
RIS/PACS: Driving Standardization for a Large Hospital System

PocketHealth launches network-free image sharing and storage platform

PocketHealth has launched a platform that lets patients and providers securely request, share and store medical images without the use of CD-ROMS or networks.

November 14, 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

Around the web

The newly approved AI models are designed to improve the detection of pulmonary embolisms and strokes in patients who undergo CT scans.

"I see, at least for the next decade, this being a SPECT and PET world, not one or the other," explained Tim Bateman, MD.

The FDA-approved technology developed by HeartFlow can predict a patient's long-term risk of target vessel failure as well as more invasive treatments performed inside a cath lab. 

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