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| | | News and views you ought to know about:- The old Oracle—founded 1977—may be the new Nvidia. That’s what some investment experts are saying after Oracle, newly focused on AI and cloud infrastructure, posted eye-popping results for the first fiscal quarter of 2026—and projected cloud business revenues to top $144B by 2030. That would crush its expected 2026 forecast of $18B. After reporting the 2026 Q1 results Sept. 9, Oracle’s stocks skyrocketed 36%. That’s when Wall Street’s eyes got wide. “Of course, Oracle faces competition from other cloud providers, as Nvidia faces competition from other chip designers,” The Motley Fool acknowledges today. “But Oracle, thanks to the pairing of its database strengths with AI and its ecosystem of related services, may, like Nvidia, stand out from the crowd … and deliver explosive gains to investors over the long run.”
- AI is doing great things for healthcare. Making it more affordable isn’t one of them. That’s the view of thinkers at the American Enterprise Institute in Washington, D.C. “AI does not solve the basic problem in healthcare, which is that there are weak incentives for cost control,” write senior AEI fellow James Capretta and research associate Jack Rowing in a Sept. 10 post. “If AI is to lead to lower costs, the government and employers will need to deploy it aggressively to identify unnecessary spending and then incentivize patients to migrate toward lower-cost insurance and care options.” Hear them out.
- Give it a little more time, will you? Over at the financial services firm Morgan Stanley in Midtown Manhattan, two money minds see things a little differently from their think-tank peers. Pointing out that hospitals’ biggest single expense by far is labor, healthcare financial analyst Erin Wright suggests AI could be just what the doctor ordered for achieving payroll moderation. “AI can optimize staffing, reduce burnout and [facilitate] more efficient healthcare record-keeping,” Wright says. “I mean, this can really help to drive meaningful cost savings. We think AI can drive meaningful efficiencies across healthcare delivery, with estimated savings of about $300 billion to $900 billion by 2050.” Her colleague Terence Flynn, a biopharma analyst, concurs. “The challenge is adoption,” Flynn says, “but the potential is transformative.” Audio and transcript here.
- If it weren’t for the intransigence of the American Medical Association, we’d be well on our way to fleets of AI doctors diagnosing diseases and prescribing treatments. That’s pretty much verbatim from the billionaire businessman and venture capitalist Vinod Khosla. Speaking at the Deus Ex Medicina conference in San Francisco Sept. 9, Khosla drew some crossfire with the comment. “Healthcare is about people,” Shiv Rao of Abridge said before stressing that conversations between patients and providers “continue to be a core signal.” Summary coverage here.
- What say ye, AMA? Short answer: We’ll let one of our constituents do the talking this time. “Healthcare for us is both high tech and high touch. There’s a lot of things we do today with robots, but [quality care] requires demonstrating empathy and caring, like touching someone’s hand when they’re sick or helping someone navigate social determinants of health issues. You just can’t do that through a machine.” The AMA ally is Baligh Yehia, MD, MPP, MSc, president of Jefferson Health in Philadelphia. More here.
- Sam Altman wants healthcare workers to know: Your jobs may be among the very few not at risk of replacement by AI. If, that is, you work directly with patients. “A job that I’m confident will not be that impacted is nurses,” Altman recently said when asked for an example of a “AI-safe” occupation. “I think people really want that deep human connection with a person in that time, no matter how good the advice of the AI is or the robot or whatever. Like, you’ll really want that.”
- He’ll get no argument from two advanced practice providers who state their opinion for public consumption. “Based on our experience as APPs, AI performs many helpful functions, but it doesn’t replace the human component,” write Ashley Campbell, MSN, and Cassidee Kuehner, RN, of The Christ Hospital in Cincinnati. “Instead, AI gives providers the time to focus on what we were trained to do: comfort patients with empathetic touch, explain test results in an individualized way that is easy to understand, and work with the medical team, patients and their families to develop the most effective plan of care.” Read the whole piece.
- Helpful, understandable, correct and complete. Oh, and transparent with potential clinical harms. These are the descriptors any healthcare AI model must demonstrably deserve to be worthy of adoption in clinical settings. Details on the source of the view and the ramifications of its rightness here.
- Also worth your while:
- From AIin.Healthcare’s sibling 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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| | | AI software embedded in video devices, wearables and sensors—not to mention actual patient monitors—can continuously track post-surgery patients in real time, sending predictive insights to care teams regardless of where they’re stationed. “These technologies can be used to detect post-operative complications, provide personalized recovery protocols, and assist in shared decision-making for tailored treatment options,” explain two orthopedic surgeons at Mayo Clinic Arizona in a scientific paper published Sept. 10. AI-enabled remote patient monitoring, they add, “analyzes massive quantities of data and utilizes technologies to allow [surgery and rehabilitation teams] to track patient progress, post-operative symptoms and recovery in real time—with the capacity to augment or replace in-person visits.” Eugenia Lin, MD, and Kevin Renfree, MD, aim their overview of AI’s growing role in remote patient monitoring at peers in their field who subspecialize in musculoskeletal care of the hand. But their guidance is broad enough to generalize across many if not most clinical specialties that use remote patient monitoring. In a section offering practical implementation tips, the authors break down AI adoption into six steps. 1. Workflow assessment.Identifying tasks that can be automated informs further decision-making, Lin and Renfree state. ‘Through mapping of systems processes, interviews of various stakeholders including clinicians and administrators, and analysis of existing electronic health record data, an intervention that can be automated is targeted.’
2. Technology and vendor selection. As platforms for technologies and vendors are quite formative, selecting platforms that offer monitoring capabilities is important, the authors point out. “Iterative platforms with transparent modeling offer the capacity to consider growth in the future,” they add. “Furthermore, technology and vendors that work with existing systems, such as the electronic medical record, provide seamless data flow and have likely met validated safety requirements.” ‘Considerations for types of technologies, such as wearables, patient communication, or clinician facing tools should be made.’
3. Clinical integration. Clinical integration of AI-enabled remote patient monitoring includes the interface with not only existing EMR systems but also with the clinical workforce, the researchers note. “Training and educating staff about the use, maintenance and continued evolution is imperative for a successful implementation,” they write. ‘Creating dedicated roles and teams ensures operational improvements, from patient communication protocols to data analysis and interpretation.’
4. Patient onboarding.Patients are key stakeholders in the success of AI-enabled remote patient monitoring, Lin and Renfree emphasize. It’s essential, they maintain, to identify suitable candidates in appropriate settings from consultation to post-operative visits. Equally key is “ensuring the appropriate consent, privacy compliance and instructions for use of these technologies.” ‘Consent for both the use of these technologies and future device usage and data sharing should be clearly outlined.’
5. Sustainable data collection and processing. “As integration advances, considerations for sustainable data collection and processing should be made, including automation of data analysis for current trends or outcomes to allow for regular review of collected data,” the authors advise. ‘Continuing to qualitatively and quantitatively monitor key performance indicators—including patient adherence, outcomes and satisfaction—is important.’
6. Reimbursement and compliance. “Finally, implement processes to document AI-enabled remote patient monitoring activities for reimbursement purposes,” Lin and Renfree urge. ‘Be familiar with the most up-to-date procedural terminology codes for therapeutic monitoring and intervention—and stay informed about evolving regulations.’
The paper posted Sept. 10 in Hand Clinics and can be found here. - Other newsworthy research:
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