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.

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The American Hospital Association (AMA) is warning healthcare systems the Russians may attempt cyber attacks amid rising tensions of the war in Ukraine and the international community's response. #Ukraine #warinukraine #ukrainewar

VIDEO: How to prepare hospitals for ransomware attacks

John Gaede, director of information systems, Sky Lakes Medical Center, Oregon, discusses how hospitals should prepare for possible cyberattacks.

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.

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.

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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).

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.
 

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.  

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.

Validation and testing of all artificial intelligence (AI) algorithms is needed to eliminate any biases in the data used to train the AI, according to HIMSS.

VIDEO: Understanding biases in healthcare AI

Validation and testing of all algorithms is needed to eliminate any biases in the data used to train the AI, according to Julius Bogdan, vice president and general manager of the HIMSS Digital Health Advisory Team for North America.

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