News You Need to Know Today
View Message in Browser

Expert AI adoption | Partner news | Industry watcher’s digest

Thursday, March 28, 2024
Link to Twitter Link to Facebook Link to Linkedin Link to Vimeo

In cooperation with

Northwestern Logo

Activeloop logo

physician artificial intelligence

5 tips on AI adoption from a frontline physician champion

AI adoption for real-world healthcare settings can’t happen in a vacuum. Behind every successful implementation is a work culture open to innovation, a leadership team supportive of continuous improvement and a PPT framework primed to be built upon.

PPT, of course, is people, process and technology—the building blocks of both optimized operations and change management. All of this comes through, even if between the lines, with many a healthcare technology leader who has lived these concepts through to meaningful AI adoption.

Case in point: Vincent Liu, MD, MS. The critical care physician, senior research scientist and clinical informaticist for the Permanente Medical Group of Kaiser Permanente recently took questions from Todd Unger, chief experience officer for the American Medical Association. The topic of their discussion: What it takes to bring an AI initiative to life. Here are five of Dr. Liu’s pointers, lightly edited for clarity.  

1. Involve physicians in decision-making around AI tools.

“Our physicians are on the front lines [with this technology], and they are leading as well,” Liu tells Unger. “When you have that kind of structure, you find tremendous opportunities to understand the enhancements that [AI] can bring to the everyday work that we have and to our objectives for achieving the Quadruple Aim.” More:

Whether it’s patient outcomes, population management, healthcare costs or provider wellness—all of those [quadruple aim objectives] are more important than they’ve ever been.

2. Speaking of the Quadruple Aim, it’s a good guide for deciding where to start—and prioritizing the AI projects you want to pursue down the line.

Liu uses ambient AI as an example because his group recently published on their deployment of the technology for all Kaiser physicians in Northern California. Those doctors are now using ambient AI to capture and file conversations with patients, he explains.

Ambient AI represents a key step toward reducing the burden of manual documentation, and it frees up doctors to do what they love to do: interact with patients.

3. Let the A in AI stand for augmented. This will help keep people at the center of your AI picture.  

Liu says there are three core competencies for AI understood this way—clinical integration, technology enablement and data science. “It’s all about, what is the method?” he says. “How do we rigorously test and evaluate performance metrics of those methods?” More:

How do we evaluate this to know that the AI technology—integrated with our clinical workforce and our data scientists—will ultimately produce something that’s valuable for patient health or provider wellness?

4. To train your teams on AI appropriate use, extend existing educational efforts.

“We’ve been doing quality and performance improvement for a long time,” Liu says. “And we’ve had a long history of technology implementation.” Those experiences have proven instrumental, he suggests, in coordinating AI training across complex, multidepartmental teams.

What distinguishes AI from what’s come before are uncertainties about how the new methods will work and, more importantly, what their fail points might be. Where can we apply AI with confidence? And where should we be especially cautious with it?

5. Sure, some of the excitement over large language models is hype. Don’t let that keep you from embracing this ‘next big thing’ in AI.

“How do we identify the best use cases, the ones that maximally, again, help us to reach the Quadruple Aim?” Liu says.

Anything that can reduce physicians’ avoidable burden—especially administrative or clerical tasks—has got to be top of mind for every health system and every medical group.

View the AMA video interview.

 

 Share on Facebook Share on Linkedin Send in Mail

The Latest from our Partners

Bayer Radiology uses Activeloop's Database for AI to pioneer medical GenAI workflows - Bayer Radiology collaborated with Activeloop to make their radiological data AI-ready faster. Together, the parties developed a 'chat with biomedical data' solution that allows users to query X-rays with natural language. This collaboration significantly reduced the data preparation time, enabling efficient AI model training. Intel® Rise Program further bolstered Bayer Radiology’s collaboration with Activeloop, with Intel® technology used at multiple stages in the project, including feature extraction and processing large batches of data. For more details on how Bayer Radiology is pioneering GenAI workflows in healthcare, read more.

 Share on Facebook Share on Linkedin Send in Mail
AI for diabetic retinopathy

Industry Watcher’s Digest

Buzzworthy developments of the past few days.

  • For a good while now, AI eye exams for diabetic retinopathy have held steady as a model of AI usefulness in clinical settings. Here’s another reason to applaud the use case and hope it inspires other equally valuable applications. In a multisite clinic system serving minority residents of Southern California, the exams help patients who won’t take time off of work for normal doctor appointments unless they have no other choice. The AI exams are much quicker and offer that choice. “Ninety percent of our patients are blue-collar,” explains the director of operations for the clinics. “They don’t eat if they don’t work.” The director calls the technology “a godsend.” KFF Health News has the story.
     
  • Remember the 2023 class-action suit accusing Humana of using AI to deny care to Medicare Advantage patients? The insurer is asking a federal court to spike it. “Allowing this case to continue would require the Court to apply 23 states’ standards, and risk outcomes that conflict with the federal government’s Medicare rules,” Humana writes in a motion filed last week. “This is exactly why the Medicare Act has such a vast preemption provision.” McKnight’s Long-Term Care News is on it.
     
  • If not properly trained, AI can lead to bias and discrimination. Just as bad if not worse, AI chatbots can generate medical advice that is misleading or false. Happily, those are the worst of the negatives. In a consumer-friendly primer on the technology, Mayo Clinic Press acknowledges the few (already well-known) downers while spending many more words on the good that healthcare AI can do—things like detecting imperceptible conditions, anticipating risk of disease years in advance and matching hopeful patients with clinical trials. Read the item.
     
  • Besides, AI bias is avoidable. When it pops up, it can usually be traced to an avoidable combination of technical errors and human decisions. Marshall Chin, MD, MPH, a professor of healthcare ethics at the University of Chicago, breaks it down for Information Week. “This is something that we have control over,” Chin says. “It’s not just a technical thing that is inevitable.” Other experts echo that observation and describe bias-busting steps to take. Article here.
     
  • The CDC opened its Office of Public Health Data Surveillance and Technology last April. One year on, the Federal News Network looks at how far OPHDST has come. One major thrust right now is figuring out how to apply Gen AI for parsing public health data to beneficial effect. In this endeavor the office is looking to partner with academia and industry to “understand the full spectrum of [available] tools and how they could be used,” OPHDST director Jennifer Layden, MD, PhD, tells the network.
     
  • In introducing a couple dozen healthcare-oriented AI tools last week, Nvidia markedly advanced its long-term designs on the medical sector. “In healthcare, digital biology and generative AI are helping to reinvent drug discovery, surgery, medical imaging and wearable devices,” Nvidia CFO Colette Kress said at the company’s spring GPU technology conference (aka “Nvidia GTC”). “We have built deep domain expertise in healthcare over the past decade.” CNBC coverage here.
     
  • AI database supplier Activeloop has raised a fresh $11M in Series A funding. This round nudges the company’s total funding to right around $20 million. Activeloop says it will use the broadened backing to, for starters, refine its Deep Lake technology. This allows the company’s enterprise clients to readily connect unstructured multimedia data with machine learning models. CEO Davit Buniatyan says Deep Lake lets enterprises “create AI applications that are more accurate, boost AI engineering teams’ productivity by up to 5x, and cost up to 75% less than market offerings.” Announcement here.
     
  • In other funding news of note:
     
  • Research news roundup:
     
  • From AIin.Healthcare’s news partners:
     

 

 Share on Facebook Share on Linkedin Send in Mail

Innovate Healthcare thanks our partners for supporting our newsletters.
Sponsorship has no influence on editorial content.

*|LIST:ADDRESSLINE|*

You received this email because you signed up for newsletters from Innovate Healthcare.
Change your preferences or unsubscribe here

Contact Us  |  Unsubscribe from all  |  Privacy Policy

© Innovate Healthcare, a TriMed Media brand

Innovate Healthcare