3 newsy must-reads on healthcare AI (plus 3 bonus picks)

1.) Some clinical AI has a fuzzy ROI. That doesn’t mean it shouldn’t be reimbursed. 2.) What makes biomedical research different from other sectors where AI has been quickly adopted? 3.) Unmoved by enthusiasm, health execs are making AI decisions around organizational priorities. 

1. Some clinical AI has a fuzzy ROI. That doesn’t mean it shouldn’t be reimbursed. 

The trick is tying payments to results and making sure the money rewards appropriate use of the technology. That’s one bit of guidance to shake out of a workshop on clinical AI payment models. Convened by the Peterson Health Technology Institute in May, the meetup brought together senior leaders from hospitals and insurance companies, along with tech developers, academicians, investors and federal officials. Participants worked through ideas for reimbursing autonomous as well as assistive AI. And they batted around an interesting case study: How provider orgs should get reimbursed for applying AI to hypertension management as AI advances to play a bigger role in care delivery. 

This month Peterson published a report drawing from the workshop. “Clinical AI fundamentally changes the relationship between utilization and value,” the authors write. “Payment for clinical AI should reflect realized clinical or economic value rather than time, effort or resource inputs.” And payment models “should be deflationary, outcome-based and adaptive to evidence and an evolving clinician role.” That’s but a taste. Read a summary or download the full report: Payment for Clinical AI 

2. What makes biomedical research different from other sectors where AI has been adopted more quickly?

A distinguished professor in two distinct disciplines is glad someone asked. “In many commercial settings, an AI error may lead to inconvenience, inefficiency or financial loss,” replies the accomplished scholar, Dajiang Liu, PhD, of PennState College of Medicine. “In biomedical research, errors can affect scientific conclusions, drug development decisions or even patient care, making the stakes higher. … For AI to be useful in this space, it not only has to be technically impressive but also scientifically rigorous, reproducible and trustworthy.” 

The above question and others are posed by Tim Schley, a senior research writer at the institution. Read the whole interesting exchange: Q&A: What can AI realistically do for medical research right now?

3. Health execs are looking past enthusiasm to make AI decisions around organizational priorities. 

Not that they weren’t doing that before, but large-language AI entered the scene in late 2022 with so much hype that its value propositions were easily obscured. Coming up on four years into the era, a new survey finds healthcare AI increasingly consequential. Of 70 executive-level respondents, some 86% said they expect their workforce size or composition to look fundamentally different in two to three years because of AI. And just under 80% expect to spend more on AI in 2027 than in 2026. 

The survey and its report are from the global consultancy RSM. When asked what would signal AI success in the coming year, 46% said “improved employee efficiency.” Not far behind were better decision-making (41%), improved patient experience (40%) and clear AI governance (40%). 

“Healthcare AI is moving from pilots to enterprise-wide adoption and accountability,” the report authors state. Meanwhile ROI optimism is “strong—but measuring financial impact remains a challenge.” Download the report: AI in healthcare: From wide adoption to operational accountability

Got a few minutes more to get additionally informed—maybe even edified? Click these: 

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