AI and machine learning are dominating the healthcare conversation

  • Posted on May 30, 2023
  • Estimated reading time 4 minutes
Implementing AI machine learning in healthcare

Like most of us, I have come to appreciate all that can be accomplished virtually, but there comes a point when nothing beats a good in-person conference. There’s just something about the combination of ideas and energy that sparks enthusiasm and inspiration.

This year I was able to attend three healthcare conferences and experience firsthand how the world’s most innovative leaders in healthcare are approaching some of the biggest challenges and opportunities ahead.

It was a fast-paced 27 days, from ViVE in late March to HIMSS23 in mid-April, stopping at Becker’s along the way, but it was worth it. I came away with insights I’m looking forward to sharing with my colleagues and clients, ideas for how we can leverage technology to address healthcare challenges in new and creative ways.

One theme dominated all three conferences: AI and machine learning. Sessions and vendor booths that referenced AI were packed. At HIMSS23, one AI session was turning people away, something I have never seen before.

Traditionally, the healthcare industry has not been a technology early adopter, but the sentiment at all three conferences showed that the industry is ready to change. I think health system leaders see this as an opportunity to be pioneers, to leverage AI and machine learning to transform the healthcare industry and even be a role model for other industries.

Balancing technology benefits and risks
Conference content that seemed most on point were sessions and conversations related to using AI in different use cases – and how to use it responsibly. How do you ensure AI models have been trained properly? What standards and guardrails should there be to guide how it is used? How can we be sure we understand any ethical or legal concerns? What liability or level of risk would we be assuming?

Although there are gray areas still to be worked out, inspiring use cases presented included using AI and machine learning to identify patients likely to have Hep C so they could be encouraged to get early testing and treatment, to reduce Medicare and Medicaid fraud, to predict which patients might get sepsis in the hospital, to predict when patients with Sickle Cell disease will have an crisis/inpatient visit, to predict lung cancer, to reduce discharge medication errors, to predict when appointments will be missed so they can be filled, to predict malnutrition in children, improve the employee experience related to benefits selection, and when reviewing images to increase diagnostic confidence.

And this is just the beginning. AI and machine learning was at the heart of almost every conference topic.

One application that caught my attention is using AL and machine learning to bridge the staffing gap. Experts predict the U.S. will be short 122,000 physicians by 2032. How can we use these technologies to enable superior care quality and help bring healthcare to those who otherwise may not have access? At one level, they can make workflows more efficient, allowing clinicians to see more patients. At another, they can help manage burnout by allowing those who deliver patient care to focus on their patients rather than red tape and paperwork.

Technology’s ability to expand access raises new questions related to equity. How do we eliminate bias in data models and provide access to healthcare to those who are not tech-enabled? Not everyone has access to online services or a smart phone. In one example cited, hospital networks are beginning to work with telecom and tech companies to provide patients with access to broadband and devices such as iPads for a limited time to monitor health outcomes.

As the third of my conference visits closed, I couldn’t help thinking about another core theme that ran through all three: today’s emerging technologies are here to stay. They’re evolving and changing all aspects of healthcare faster than one can imagine. Now is the time to dive in, to study and learn, because the greatest benefits will accrue to those systems that can implement carefully and deliberately, then make the best and wisest use of them.

Find out how Data and AI can help make a genuine human impact in healthcare.

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