Letting doctors
be doctors
Current ambient AI assistants, which gained mainstream traction in 2023, are already capable of record, structure, and summarize patient encounters in real time. This liberates clinicians from the time-consuming exercise of writing notes, allowing them to totally engage with their patients. “For complex patients, it could take me as much as 45 minutes to finish the documentation. Nabla makes that task infinitely higher and allows me to provide each patient my full, undivided attention. At the tip of the visit, I click, and Nabla produces a thoughtfully crafted, concise record of what happened,” says Lee, who puts the accuracy of Nabla’s system within the “high 90s” by way of percentage, with the clinician all the time answerable for reviewing and signing off on the ultimate record.
“For complex patients, it could take me as much as 45 minutes to finish the documentation. Nabla makes that task infinitely higher and allows me to provide each patient my full, undivided attention. At the tip of the visit, I click, and Nabla produces a thoughtfully crafted, concise record of what happened.”
Dr. Ed Lee, Chief Medical Officer, Nabla
This type of uninterrupted patient engagement can lead to raised eye contact and the next quality interaction. As an example, clinicians are likely to verbalize their thought process more when there’s alternative notetaking during a patient evaluation. “We originally thought that patients can be frightened about an AI device listening, but actually they’re very excited,” says Alexandre LeBrun, co-founder and chief executive officer of Nabla. “They get the total attention of their physician throughout the visit, they usually love once they hear technical language as they sense they improve care.”
Based on LeBrun, Nabla’s system can further support clinicians by automating pre-charting, reviewing and organizing a patient’s information of their EHR before an appointment, and coding medical data to be used in areas like billing. Nabla has also expanded its platform with a built-in dictation capability, bringing clinicians closer to a unified experience. These sorts of AI assistant tasks may also help to streamline and enhance clinical workflows and contribute to a discount in institutional administrative costs.
The promise of
agentic AI
Agentic AI, which firms like Nabla are currently working to integrate into their systems, guarantees to take the success of existing AI assistants a step further. LeBrun is seeking to a future wherein clinicians interact with an agentic platform that links to all of the tools they already use and simplifies multi-step interactions, like reading patient data, acting throughout the EHR, and adapting to workflows in real time.
“Quite than forcing doctors and nurses to click through a dozen separate systems, our platform will provide specialized, customizable, and composable agents that turn disconnected tools right into a single, continuous workflow,” LeBrun says.
“Imagine a cardiologist preparing for his or her morning clinic. After just a few voice commands to instruct the system, one agent pulls the newest vitals, lab results, and imaging reports from the EHR, one other generates a transparent patient summary, and a 3rd flags a missed follow-up echocardiogram. All before the patient even walks into the room,” LeBrun explains.
“Quite than forcing doctors and nurses to click through a dozen separate systems, our platform will provide specialized, customizable, and composable AI agents that turn disconnected tools right into a single, continuous workflow.”
Alexandre LeBrun, Co-founder and Chief Executive Officer, Nabla
Lee says that agentic AI’s near-term scope includes standardized and protocolized non-clinical tasks, but he sees promise in areas like treatment options and other forms of clinical decision support, where AI can safely operate with clinicians all the time “within the loop.”
To get up to now, education is important, says Lee. “The great thing about medicine is that it’s a lifelong learning process. It’s not only learning in regards to the science behind medications, diagnoses, and coverings; it’s about adapting to the usage of latest tools that can ultimately improve the care of the patients you treat,” he explains.
“We’d like to begin with the fundamentals of AI, ensuring everyone understands what it’s and the way it really works. Not how the programming takes place but more around what it may do, what it may’t do, the risks and pitfalls, after which really understanding where it matches best within the care of patients,” says Lee.
Leadership must look ahead strategically and ensure the complete organization is moving forward with its use and understanding of AI, he adds. “A part of that journey is involving frontline users to be a part of the method, co-designing every time possible and conducting pilots of latest solutions so the organization can learn,” Lee says. Moreover, “a culture of inclusivity, authenticity, and transparency must be in place so you possibly can be in the most effective position to achieve success with transformative efforts equivalent to incorporating and integrating agentic AI into the ecosystem,” he says.
“A part of that journey is involving frontline users to be a part of the method, co-designing every time possible and conducting pilots of latest solutions so the organization can learn.”
Dr. Ed Lee, Chief Medical Officer, Nabla
Safely integrating
into workflows
Applying AI to high-stakes sectors like health care requires a careful balance between productivity on the one hand, and accuracy on the opposite. “Trust is every little thing in medicine,” says LeBrun. “Earning that trust means giving clinicians confidence through accuracy, transparency, and respect for his or her expertise.” Nabla uses techniques like adversarial training models to examine outputs, and it defaults to conservative responses. “We optimize precision. If we’ve got a slight doubt, we prefer to remove something from the output by default,” says LeBrun
“Trust is every little thing in medicine. Earning that trust means giving clinicians confidence through accuracy, transparency, and respect for his or her expertise.”
Alexandre LeBrun, Co-founder and Chief Executive Officer, Nabla
Latest tools must also interweave with existing workflows and platforms to avoid adding more complexity for clinicians. “Any product can look great, but when it doesn’t fit well into your existing workflows, it’s almost useless,” says LeBrun.
In sectors like customer support, it is easy to construct a brand new interface or platform, but that approach isn’t feasible—or desirable—in health care. “It’s a fancy network of dependencies with so many workflows and processes,” says LeBrun. “Everybody would really like to eliminate this stuff, nevertheless it’s impossible since you would wish to alter every little thing directly.” Agentic AI approaches offer great promise to sectors like health care because they will “improve the method without eliminating the legacy infrastructure,“ LeBrun explains.
By simplifying complex systems, automating routine tasks, and continuing to tackle more of the time-consuming burden of administrative work, agentic AI holds great promise in further augmenting ambient AI assistants. Ultimately, the technology’s potential isn’t in making medical decisions or replacing clinicians, but in supporting health care staff to dedicate more of their time and a focus to their primary priority: their patients. “AI should give attention to supporting decisions and automating every little thing downstream,” says LeBrun. “The primary role of AI is to get physicians back to the state where they make medical decisions.”
Discover more insights from Nabla here.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of information for surveys. AI tools that will have been used were limited to secondary production processes that passed thorough human review.
By MIT Technology Review Insights
