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| | | As applications of AI spread rapidly across healthcare, ambient scribes are poised to become one of the fastest technology adoptions in the history of the sector, according to discussions conducted and analyzed by the Peterson Health Technology Institute in New York City. Today, approximately 60 ambient scribes are being implemented in practice, according to the PHTI market research report describing the project’s findings. “In an industry with notoriously long sales cycles and implementation timelines,” the authors state, “there is no technology in recent memory that has been adopted more enthusiastically by clinicians or has scaled so uncharacteristically fast, absent a regulatory mandate.” In a section on early uptake and adoption, the analysts list five observations they made by reviewing the material generated by the discussions. 1. Interest in ambient scribe technology is uneven across specialties. Some organizations report strong uptake beyond primary care, including in emergency medicine and surgical and procedural specialties, the authors report. “Interestingly, several organizations observed that those who benefited the most were not their tech-savvy early adopters, as those individuals had typically already optimized their documentation processes with dot phrases and templates,” they add. More: ‘The clinicians experiencing the greatest benefits were those who had not yet optimized their current EHR-based clinical documentation workflows, were consistently behind in notes, spent more time in conversation with their patients or typically had longer summary notes.’
2. Adoption has been slower in certain subspecialties. When ambient scribe is widely available, adoption rates are typically 20–50%, PHTI reports. “However, one organization achieved 75–80% adoption in the clinical areas where it has been offered, which they attributed to a deliberate emphasis on note customization followed by hands-on training.” ‘Mass General Brigham shared that approximately 90% of its ambulatory primary care physicians have requested access to ambient scribe.’
3. Among those using ambient scribes, consistency of use is variable. “Typically, there is a cohort of ambient scribe superusers; a cohort using it for some but not all visits; and a cohort of low- or no-use clinicians, including those who tried it but stopped,” the analysts write. Meanwhile, PHTI found, those who have stopped using ambient scribe cited several reasons: the generated notes did not reflect their personal style or voice, they had minimal time or bandwidth to fully engage with the adoption process, they had already optimized their note-taking process and saw minimal efficiency gain, or the tool did not adequately support the languages spoken by their patients. ‘One organization shared that, in the ambulatory setting, if a provider is an active user of ambient scribe, they use it for 30–40% of visits.’
4. While effective at documenting patient-clinician interactions, the technology is still imperfect.Feedback suggests that some ambient scribes have difficulty summarizing complex interactions—like case conferences or discussions with patients and multiple caregivers—as the technology is not always able to accurately discern different voices. “From a clinical perspective, errors in documentation, such as attributing notes to the wrong person or ignoring critical details, pose risks,” the authors write. ‘There is also the potential for technology hallucinations, whereby incorrect information is included as part of the visit summary.’
5. Some organizations are eager to experiment with applications of ambient scribe beyond ambulatory clinicians.Yale New Haven Health, for example, is piloting ambient scribe with other types of providers who spend a lot of time with patients, such as residents, physical and occupational therapists, pharmacists, and social workers, PHTI reports. “Another organization suggested an efficiency opportunity for clinicians who need to reference each other’s notes in the inpatient setting,” the authors add. “For example, a nutritionist can review a note that was written by the attending clinician in a format that is relevant and applicable to their work.” ‘Several are leveraging ambient scribe in the emergency department and looking forward to expanding to other hospital-based clinicians.’
There’s a good deal more in the 38-page report, which is posted here. |
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| | | Are You Prioritizing Clinician Well-Being? In recognition of Health Workforce Well-Being Day, Nabla recently hosted a thought-provoking conversation on what it truly takes to foster a culture of clinician well-being. From going beyond surface-level wellness initiatives to embedding well-being into organizational leadership and strategy, the discussion offered practical, actionable insights for healthcare leaders. Read the key takeaways and watch the full recording: https://www.nabla.com/blog/webinar_clinician_well_being/ |
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| | | Buzzworthy developments of the past few days. - Beware AI watching and listening from digital platforms. Not the usual-suspect applications with ‘AI’ written all over them. The ones that are used ubiquitously all around your enterprise. The apps with supposedly safe names like Microsoft Office, Adobe Acrobat, Bing, Grammarly and LinkedIn—to name just a few among 70 or so in wide use across healthcare. Stealthy AI inside such familiar software could send confidential patient data, whether inadvertently or otherwise, to a third-party large language model. Then the LLM might use the data to further train itself. And “once the information is embedded in the model’s brain, it’s a lost battle,” a cybersecurity expert tells Newsweek. “Now everyone who is interacting with the model potentially can get the sensitive data that was leaked.”
- Everyone was wowed when large language AI passed a medical licensing exam. Remember? It happened only two years ago. But expectations may be rising faster than what the models can deliver: real assistance with real-world patient care. A muscular effort to match the potential with the deliverable is underway at Stanford. There researchers have developed a framework for the goal. The team’s MedHELM project looks to put Stanford’s RAISE Health Initiative to work in daily episodes of care. HELM stand for holistic evaluation of language models, RAISE for responsible AI for safe and equitable health. That hints at where they’re going with MedHELM. “About 95% of LLM evaluations that are reported in the literature are not done using electronic health record data—and that context is really important,” explains Nigam Shah, MBBS, PhD, chief data scientist at Stanford Health Care. “MedHELM does the back-end work that pulls in relevant datasets and executes hypothetical but common use cases for how people in health and medicine might use an LLM to inform their work.” Then it shows which of six commonly available models perform best for a given task. Learn more straight from Stanford.
- Some AI decision-support models have a proclivity for recommending aggressive care pathways. And doing so based not on medical necessity but on patient demographics. The same models, or others, tend to advise advanced imaging for wealthy patients and “no further testing” for the less well-off. The researchers who uncovered the undesirable behaviors call for modifications informed by their findings. “By identifying when AI shifts its recommendations based on background rather than medical need, we inform better model training, prompt design and oversight,” says Eyal Klang, MD, chief of generative AI at the Icahn School of Medicine at Mount Sinai. A digital process the team developed to gauge AI outputs against clinical standards incorporates expert feedback to refine model performance, he adds. “This proactive approach not only enhances trust in AI-driven care but also helps shape policies for better healthcare for all.”
- AI is commonly looked to for curing diseases. But it’s also pretty good for sustaining wellness. Think of front-end strategies for warding off dementia, maintaining mental health and improving fertility. In the U.K., researchers at the University of Cambridge offer a patient-friendly rundown of what they’re up to in six such realms. “If we get things right, the possibilities for AI to transform health and medicine are endless,” state three professors at the institution’s Centre for AI in Medicine. “It can be of massive public benefit. But more than that, it has to be.” More here.
- An AI chatbot trained in clinical best practices for talk therapy may finally get that assignment right. Developed at Dartmouth College and recently described in the New England Journal of Medicine, the model seems to be producing some notably positive outcomes. The work is newsworthy because some prior attempts have been iffy. A few have been even worse, leading patients to harm themselves. By contrast, the effects the Dartmouth team is seeing “strongly mirror what you would see in the best evidence-based trials of psychotherapy,” Nicholas Jacobson, PhD, tells NPR. In fact, he adds, the results have been “comparable to studies with folks given a gold standard dose of the best treatment we have available.”
- Healthcare AI can supplement exercise regimens, fine-tune physical therapy and refine business strategy. TechTarget includes these up-and-coming use cases in a list of 10, most of which aren’t all that new but will continue to rise in profile over the coming months and years. AI is “redefining” healthcare, the piece reminds, “with hospitals, health systems and large medical practices incorporating AI technologies into administrative as well as clinical workflows.”
- Mark your calendar. Polish your paper. Or do both. This year’s Machine Learning for Healthcare Conference will unfold at the Mayo Clinic in August. The organizers are already accepting submissions. Researchers, clinicians and all-around innovators interested in advancing the art and science of AI in healthcare are invited to showcase work that combines cutting-edge research with real-world impact, says Shauna Overgaard, PhD, senior director of AI enablement at Mayo’s Center for Digital Health. “This is a unique opportunity,” she emphasizes, “to advance the field, collaborate globally and reinforce our shared commitment to patient-centered AI.” Details.
- For some, maple syrup from Canada is happy medicine in the morning. But it has to be pristine, unadulterated, 100% true maple syrup. From Canada. For them, there’s AI for food purity. “With the increased risk of food fraud due to threats of increased U.S. import tariffs on Canadian products, combining AI and maple syrup fingerprinting can detect maple syrup fraud,” write a trio of subject matter experts in The Conversation. “Food fraud, or economically motivated adulteration, is the deliberate misrepresentation of food for economic gain.” And from the “I Did Not Know That” Department, Canada produces more than 70% of the world’s maple syrup. I probably would have foolishly bet on Vermont.
- Recent research in the news:
- Notable FDA approval activity:
- Funding news of note:
- From AIin.Healthcare’s news partners:
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