AI & ML Intelligence

These networks started impacting healthcare in the last 20 years, but it's only today that reliable prospective studies are convincing clinicians to use these artificial intelligence tools in their daily work. A Mayo Clinic researcher offers some insights into their applications and use.

Integrating data and AI to improve decision-making and the patient experience; training staff to lead AI adoption; creating trustworthy AI; and much more, presented by Accenture's Global Health Technology Lead Andy Truscott.

Dr. Bernard Schayes, a New York City-based physician in private practice, successfully deployed artificial intelligence-powered care coordination technology, saving him hours of tedious typing and data entry, among other gains.

In the test, 74% of clinicians said the ambient and genAI technology reduced their levels of burnout and 95% wanted to continue using the technology. Further, clinicians used 25 non-English languages.

The CAIO should not just understand both of those disciplines, but intuit what AI is good and bad, what's scalable and whether it's the right fit, says Sameer Sethi, the New Jersey health system's chief AI and insights officer.

Chief Data and AI Officer Mouneer Odeh says work on artificial intelligence is like a snowball going downhill – over the next five to 10 years it will build and build until the scale of the impact meets what healthcare requires.

The University of California at San Francisco's overarching goal was not to replace human judgment but to enhance it – allowing oncologists to focus on personalized treatment rather than spending valuable time retrieving and verifying information.

The kidney care provider's build-not-buy effort has shown great results: 30-day readmissions are down 36%, hospitalizations among high-risk patients are down 49% and optimal starts are up 67%.

Tom Kiesau, chief innovation officer and leader, digital and technology transformation, at healthcare consulting firm Chartis, discusses his vision of AI that includes the aging population, clinical and operational AI, AI operating models and much more.

But barely 1 in 10 are properly investing in the infrastructure necessary to support enterprisewide deployments, according to its new research report. An Accenture health analyst unpacks its findings.