Artificial Intelligence
Tina Manoharan, VP of Data & AI Center of Excellence at Philips, previews her HIMSS23 discussion, showing how health systems need real-world data to gain insights with artificial intelligence.
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01:32 pm |
April 10, 2023
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01:32 pm |
April 10, 2023
Healthcare insiders make the case for adopting a data analytics platform.
By being aware of certain tactics and red flags, IT and security teams can take proactive steps to detect misuse of former employees' access credentials before they cause harm, says Imprivata's Joel Burleson-Davis.
In a preview of his HIMSS23 session, Christopher Girardo, fellow, clinical informatics/PRN staff, pathology, at LSU Health Shreveport and Ochsner Health, dives into health IT's role in fighting sepsis.
The Coalition for Health AI's guide takes a patient-centric approach and aims to address, among other challenges, barriers to trust in AI and machine learning. It builds on the White House's AI Bill of Rights and NIST's' AI Risk Management Framework.
A HIMSS23 panel on biosurveillance to help public health officials in pandemics will feature Scott McKeithen, Marleen Welsh and Charles Baschnagel, who work at the Department of Defense Chief Digital and Artificial Intelligence Office.
The largest health system in New York State will roll out an enterprise-wide platform to help standardize and scale secure monitoring and interoperability, enabling more unified visibility into what's happening at each patient's bedside.
By analyzing crowdsourced patient experience data with NLP, it's possible to enhance care quality measurement and reporting, says Jennifer Gaudet Hefele, senior scientist at Booz Allen Hamilton, a speaker at HIMSS23.
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10:00 am |
April 04, 2023
The role of AI and data in enabling the journey from precision medicine to personalized care “micro…
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10:00 am |
April 04, 2023
Enhancing genomic discoveries with AI and machine learning.
In a preview of his HIMSS23 discussion on artificial intelligence in healthcare, Jeremy Petch, director of digital health innovation at Hamilton Health Sciences, describes the uses and limitations of explainable AI in different clinical settings.