Clinicians need one main thing: More time

As the EHR and data collection have become more robust, clinicians are spending more time on paperwork and administration. The American Medical Association conducted surveys in 2024 and found that physicians spent an average of 13 hours on indirect patient care (order entry, documentation, lab interpretation) and over seven hours on administrative tasks (prior authorization, insurance forms, meetings). On top of patient care, this meant a 57.8-hour workweek.

Ultimately, clinicians need more time with their patients and less time taking notes. They need more time to understand complex cases and less time spent searching for information. Information overload is also a challenge: Medical knowledge is doubling every 73 days, and patients are increasingly relying on multiple medications. It also takes an average of 17 years between clinical discovery and changing practice based on evidence—clinicians need efficient ways to stay updated in their area of expertise.

AI can produce time savings that add up

We’re seeing a revolution in how artificial intelligence (AI) can support them. As AI is introduced further into healthcare administrative work and clinical settings, there are opportunities for clinicians to be more productive and meaningful with their time.

When we look at how AI-enabled features can save time for clinicians, the amazing thing is that it’s not massive blocks of time—like 5 or 10 minutes. It’s 10 seconds on a task, or 30 seconds here, or 45 seconds there. And the clinicians we speak with are so happy about it. AI can help speed up the little things—the couple of clicks saved—and over time, that can make a huge difference. It’s multiple moments of small savings that add up to these meaningful productivity gains.

So, as we find ways to further integrate UpToDate into the workflow, this is what we think about: Finding those extra moments that matter. Getting clinical information closer to the provider so they don’t have to open extra applications for decision-making. We’re looking for multiple ways to get evidence and clinical intelligence streamlined throughout the care experience and into the EHR, presenting tremendous opportunities for time savings.

The opportunities are plentiful. How can ambient and note-taking technology link to the relevant evidence-based clinical content for quick reference? How could patient interactions with chatbots ahead of a clinic visit prep the provider with relevant evidence in advance? Identifying innovative partners that can work alongside us in ambient solutions, documentation, chatbots, and more can help bring content and evidence closer to clinicians and save those seconds over time.

Time savings can bring new clinical opportunities

What can clinicians do with that saved time? Some have been concerned that GenAI tools will deteriorate clinical decision-making skills—our recent Future Ready Healthcare report showed that 57% of respondents share these concerns. But I like to think about the opportunities created through those time savings: How can AI help open up space for deeper critical thinking?

With AI saving time and supporting smaller tasks, the first thing it can do is alleviate some of the administrative burden, which is already happening. It can also expand critical thinking opportunities and provide space to consider challenges in healthcare that historically we haven’t had time to solve. It can “re-humanize medical practice” in a way that provides professional fulfillment and allows clinicians to spend more time as caregivers, rather than note-takers. When these efforts are scaled across the workforce, it can result in productivity gains and operational efficiencies across an enterprise.

AI tools need to be grounded in expert-driven evidence

As we rapidly move into the AI era, it’s easy to find tools that seem to give faster answers, especially among generative AI (GenAI) tools. But are they grounded in evidence and industry recommendations?

Keeping expert clinicians in the loop is critical—if you’ve trusted UpToDate for a while, you’ll know this is our position. Our clinical decision support is grounded not just in evidence but in the recommendations of over 7,600 clinical practitioners and experts who curate content as new evidence emerges, and provide graded recommendations to help guide decision-making, even when the conditions are gray. Relying on clinical recommendations curated by human experts keeps the information and care guidance current and relevant. As AI is layered on top of these human-generated recommendations, clinicians can start finding information more efficiently—saving precious seconds with each patient.

We know this expertise matters. A 2024 Wolters Kluwer Health survey of US physicians showed they were overall positive about the prospects of GenAI in clinical settings; however, 91% said they would have to know the materials the AI was trained on were created by doctors and medical experts in order to trust it. They also overwhelmingly wanted (89%) the technology vendor to be transparent about where the information came from, who created it, and how it was sourced.

The UpToDate, you know and trust, is entering a new era, which is in line with Bud Rose’s vision for a consultative conversation with clinical experts. And we’re just getting started—join us in helping shape the next wave of healthcare innovation.

Read our vision for the future of healthcare and explore our perspectives on AI in clinical content.

The global messaging system collaborated with 13 banks on experiments using privacy-enhancing technologies (PETs) to let institutions securely share fraud insights across borders, according to a Monday (Sept. 15) press release.

In one instance, the PETs allowed participants to verify intelligence on suspicious accounts in real time, “a development which could speed up the time taken to identify complex international financial crime networks and avoid fraudulent transactions being executed,” the release said.

In another case, participants employed a combination of PETs and federated learning, or an AI model that “visits” institutions to train on their data locally and lets them work together without sharing customer information, to spot anomalous transactions, per the release.

Trained using synthetic data from 10 million artificial transactions, the model was twice as effective in identifying fraud than a model trained using a single institution’s dataset, the release said.

“These experiments demonstrate the convening power of Swift as a trusted cooperative at the heart of global finance,” Rachel Levi, head of AI for Swift, said in the release. “A united, industry-wide fraud defense will always be stronger than one put up by a single institution acting alone. The industry loses billions [of dollars] to fraud each year, but by enabling the secure sharing of intelligence across borders, we’re paving the way for this figure to be significantly reduced and allowing fraud to be stopped in a matter of minutes, not hours or days.”

In the wake of these experiments, Swift plans to widen participation before beginning a second round of tests, which will use real transaction data in hopes of demonstrating the technologies’ effect on real-world fraud, the release said.

When it comes to preserving trust in financial transactions, sharing data is important.

“It’s a team sport,” Entersekt Chief Product Officer Pradheep Sampath told PYMNTS in August. “And the thread that binds us all together is data that’s actionable, shared in good faith, and governed responsibly.”

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