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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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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VIDEO: Use of AI to address health equity and health consumerization

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explains the use of artificial intelligence (AI) algorithms to help address health disparities and the rise of healthcare consumerism.

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VIDEO: AI can help prevent clinician burnout

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, discusses how artificial intelligence (AI) can help combat clinician burnout.

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explains several key artificial intelligence (AI) trends he sees across healthcare.

VIDEO: 9 key areas where AI is being implemented in healthcare

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explains several key artificial intelligence (AI) trends he sees across healthcare.

women burnout

AI model explores EHR data to predict physician burnout

A new AI tool from Washington University in St. Louis researchers aims to help identify burnout among physicians and could potentially prevent it in the future.

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explains considerations for healthcare system information technology (IT) management teams on the implementation of artificial intelligence (AI). He also discusses ideally how AI should be integrated into medical IT systems, and some of the issues AI presents in the complex environment of real-world patient care." #AI #HIMSS

VIDEO: How hospital IT teams should manage implementation of AI algorithms

Julius Bogdan, vice president and general manager of the HIMSS Digital Health Advisory Team for North America, explains considerations for healthcare IT teams on the implementation of artificial intelligence (AI).

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Concerns raised over how hospitals can validate radiology AI algorithms

As artificial intelligence (AI) adoption expands in radiology, there is growing concern that AI algorithms need to undergo quality assurance (QA) reviews.

Bibb Allen, MD, FACR, chief medical officer of the American College of Radiology (ACR) Data Science Institute, and former ACR president, explains how hospitals or radiology departments can conduct quality assurance (QA) assessments on artificial intelligence (AI) algorithms they adopt to ensure they are accurate. The ACR established the Assess-AI Registry and AI-Lab to help with validating and tracking AI QA for FDA-cleared algorithms.

VIDEO: Validation monitoring for radiology AI to ensure accuracy

Bibb Allen, MD, FACR, Chief Medical Officer of the American College of Radiology (ACR) Data Science Institute, and former ACR president, explains how hospitals or radiology departments can conduct quality assurance assessments on artificial intelligence algorithms they adopt to ensure they are accurate. 

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