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| | | News and views you ought to know about:- AI could appreciably improve the delivery of healthcare services to patients—if only people trusted it. For many, the difference-maker would be nicely crafted federal regulations. A compelling case for Congress to get on the ball was made at a hearing before the House Energy & Commerce Committee’s subcommittee on health Sept. 3. The individual giving the reasoned yet impassioned testimony was Michelle Mello, JD, PhD, a professor of law and health policy at Stanford. “[P]oorly designed regulation can hinder innovation,” Mello acknowledged, but “the government has a critical role to play in ensuring the conditions for innovation to translate into greater adoption of healthcare AI. … By taking practical steps now, Congress can help make healthcare providers and the public more excited to receive the products coming out of industry, ensuring that innovation truly reaches the bedside.” Mello offered some specific policy recommendations for juicing AI adoption by building trust in its contributions. Among her suggestions: “Modify healthcare reimbursement policies to better support adoption and monitoring of effective AI tools” and “Address shortcomings in the FDA’s statutory framework to make the agency a more constructive partner in AI development and adoption.”
- The hearing was convened and moderated by Energy & Commerce chair Brett Guthrie (R-KY) along with health subcommittee chair Morgan Griffith (R-VA) and ranking member Diana DeGette (D-CO). The official exchange of ideas brought full focus to AI’s potential to upgrade U.S. healthcare. Mello was one of five witnesses who brought expert insights to the assembled lawmakers, aides and onlookers. Other notable quotes from the session (spoken before the requisite questioning began):
- “AI should be a powerful force for good. We must use it responsibly. While we acknowledge the need for thoughtful guardrails, we also have a shared responsibility to ensure that this technology democratizes healthcare and makes the best care available to all Americans, while also saving taxpayer money. … AI should be a tool that enhances care quality and access to care. AI should not replace those who provide care. This is … the model we should champion as a nation.”—Andrew Toy, chief executive of Clover Health
- “[T]he direct-to-consumer market is flooded with unregulated chatbots that make deceptive claims. Certain entertainment-based chatbots have been utilized for therapy. ... These unregulated products can provide dangerous advice. … To harness the benefits of AI while mitigating its risks, we must move forward not with blind optimism but with cautious, informed and ethical stewardship. This requires a fundamental commitment to a human-centered approach.”—C. Vaile Wright, PhD, senior director of healthcare innovation at the American Psychological Association
- “Congress can accelerate safe adoption. By creating efficient FDA pathways, aligning reimbursement with clinical outcomes, and ensuring data interoperability and accountability, lawmakers can ensure that every patient—whether in a rural critical access hospital or a major academic center—benefits from responsible, life-saving AI.”—Andrew Ibrahim, MD, MSc, chief clinical officer at Viz.ai
- To watch a video of the three-hour hearing or read the witnesses’ prepared remarks, go here.
- Patients aren’t the only healthcare VIPs whose instinct toward AI is to hope for its best while preparing for its worst. This comes through in a new survey of 101 hospital executives conducted by the vendor consultancy Sage Growth Partners. Just 1 in 10 respondents said their organizations are pursuing AI “aggressively.” Similarly, only 12% believe AI algorithms are highly reliable and 13% report having a clear strategy for integrating AI into clinical workflows. In an interview video posted Sept. 4, Sage CEO Dan D’Orazio tells Chief Healthcare Executive he’s not surprised by the widespread caution. “We’re at the very, very front end of what some people have called … the fourth industrial revolution,” D’Orazio says. “On the promising side, what we’ve seen with ambient [AI scribes] is that, finally, the human is not working for the machine. The machine is now working for the human.”
- More than half the 1,200+ AI-equipped medical devices approved by the FDA, 53%, are made and/or marketed by publicly traded companies. Yet nearly 99% of all units recalled for problems were brought to market by 92% of these companies. Further, among devices recalled for lack of clinical validation, 97% came from smaller public companies and 78% from established public companies. By comparison, the substantial ratio of devices lacking such documentation from privately held companies—4 in 10—looks pretty good. The findings are from a Johns Hopkins-led study published Aug. 22 in JAMA Health Forum. The authors remark that the strong association between public company status and higher recalls “may reflect investor-driven pressure for faster launches.” They also surmise the FDA’s 510(k) approval process “may overlook early performance failures of AI technologies.” Read the whole thing.
- Two players in the health insurance space have merged over a unified eye on AI. HealthEdge and UST HealthProof announced their consolidation Sept. 4, saying their combined strengths will bring health plans AI-enabled means for lowering costs and boosting efficiencies. The combined entity will operate as HealthEdge. Noting that the development follows Bain Capital’s recent acquisition of UST HealthProof from UST Global, HealthEdge CEO Kevin Adams says the merger “brings together best-in-class payer technology with seamless integration capabilities and experienced operational teams.” He adds that HealthEdge will look to streamline “the administrative complexity that health plans deal with every day.” Announcement.
- Develop an oversight and monitoring plan. Assign a multidisciplinary team. Review guidelines and regulatory changes. These are three of five steps the American Medical Association urges provider organizations to take so they can track AI performance as algorithm performance changes with age. “Technology is changing very quickly, clinical guidelines are changing, the way we do our work is going to shift because of these new tools,” reminds Margaret Lozovatsky, MD, the AMA’s chief medical information officer and VP of digital health innovations. “There has to be a way to continue to measure the success of these implementations over time.” More AMA thoughts and links here.
- AI-powered stethoscopes are expensive. Are they worth it? They can be—for clinicians who are open to training on them, monitoring their digital security settings and willing to part with up to $500 to get one. “Normal stethoscopes rely solely on the doctor’s ability to hear things, and even the most seasoned professional is capable of missing things fairly easily,” explains tech enthusiast Anurag Reddy at India-based Analytics Insight. “Artificial Intelligence can identify those minor variations in sound that may be an early indication that [the patient] may be getting sick.”
- From AIin.Healthcare’s sibling news outlets:
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| | | The ambient AI playbook: Lessons from two leading health systems At the recent CompassionIT Summit, leaders from Akron Children’s Hospital and Denver Health shared powerful lessons from rolling out ambient documentation to over 1,500 clinicians. Their biggest takeaway? Stories, not stats, drive adoption. Whether it was a heartfelt testimonial that swayed an entire department or a 60-second Nabla demo that eliminated training anxiety, the common thread was simplicity, authenticity, and clinician-centered design. Read more about the way these health systems are navigating ambient AI implementation: https://dhinsights.org/news/the-ambient-ai-playbook-lessons-from-two-leading-health-systems |
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| | | It’s too soon to characterize the economic impact of AI across Western healthcare with anything more than rough guesstimates. This is so for two reasons. One, direct research into the matter is thin bordering on skeletal. And two, the issues confronting those who set out to assess AI’s effect on healthcare economics are knotty. How knotty? Enough to preclude the degree of clarity needed to aid cost-conscious decisionmakers even with broad strokes. A review of the relevant English-language literature produced these conclusions. The research was conducted in Finland and is slated for publication in the January 2026 edition of the International Journal of Medical Informatics. “In line with past literature covering the economic evaluation of AI technologies in healthcare, the findings of this review identified significant gaps in reporting essential details related to economic evaluations,” write Hanna von Gerich, MNSc, of the University of Turku and colleagues. That shouldn’t be surprising, though, since “half of the included articles did not recognize the study as an economic evaluation in the title, nor was economic evaluation the main aim of the studies.” In their discussion section, the team offers observations that may guide the needs, aims and designs of future studies into the economic impact of AI on healthcare. Here are three. 1. As the significance of AI in healthcare grows, so does the need for consistent economic evaluations.This need “urges the development or complementation of existing technology development frameworks with unified and established economic evaluation and reporting guidelines,” von Gerich and co-authors remark. “Likewise, previous reviews have reported insufficient and superficial reporting of technological details of the evaluated systems.” ‘More than half (n = 10, 55.6 %) of the studies included in this review reported the methods used as simply ML or deep learning, and the comparison of the studies to the CHEERS-AI reporting standards indicated a lack of key details related to specific characteristics or impacts of AI.’
2. AI-based systems are not isolated or standalone tools. They involve complex interdependencies between technologies, healthcare providers and healthcare users.The performance of learning systems progressively changes over time, the researchers point out. The resulting inconstancy affects “not only the outcomes provided by [such systems] but also the behavior of the healthcare professionals using them.” Meanwhile, “complementing the economic evaluations with specific features and characteristics of the used systems is vital to assess and truly understand their real impacts.” ‘AI is commonly framed as a cost and resource-saving tool, overlooking the support and maintenance it requires to operate successfully, including regular evaluations of the system’s performance or the associated costs for integrating it into novel or existing healthcare systems. Most studies in this review did not cover the purchase or other cost components related to the technologies.’
3. Issues in reporting AI-specific details in economic evaluations might partly stem from a lack of comprehensive understanding of the potential impacts of these systems. “This could reflect a lack of engagement between the system developers, who understand the technical details and requirements of the AI systems,” von Gerich and colleagues surmise, “and the system users and evaluators, who understand the healthcare environments and specific features related to the favorable outcomes of the systems.” ‘Future research could further investigate how to support better preparation for and account for more in-depth system evaluations, starting from the early planning phases of the system development life cycle.’
Read the rest. - Other newsworthy research:
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