Letting doctors \u00a0
be doctorsbe\u00a0doctors<\/p>\n
Current ambient AI assistants, which gained mainstream traction in 2023, are already able to record, structure, and summarize patient encounters in real time. This liberates clinicians from the time-consuming exercise of writing notes, allowing them to fully engage with their patients. \u201cFor complex patients, it could take me up to 45 minutes to complete the documentation. Nabla makes that task infinitely better and allows me to give each patient my full, undivided attention. At the end of the visit, I click, and Nabla produces a thoughtfully crafted, concise record of what happened,\u201d says Lee, who puts the accuracy of Nabla\u2019s system in the \u201chigh 90s\u201d in terms of percentage, with the clinician always responsible for reviewing and signing off on the final record.<\/p>\n
\u201cFor complex patients, it could take me up to 45 minutes to complete the documentation. Nabla makes that task infinitely better and allows me to give each patient my full, undivided attention. At the end of the visit, I click, and Nabla produces a thoughtfully crafted, concise record of what happened.\u201d<\/p>\n
Dr. Ed Lee, Chief Medical Officer, Nabla<\/p>\n
This kind of uninterrupted patient engagement can lead to better eye contact and a higher quality interaction. For instance, clinicians tend to verbalize their thought process more when there is alternative notetaking during a patient evaluation. \u201cWe originally thought that patients would be worried about an AI device listening, but actually they are very excited,\u201d says Alexandre LeBrun, co-founder and chief executive officer of Nabla. \u201cThey get the full attention of their physician during the visit, and they love when they hear technical language as they sense they get better care.\u201d<\/p>\n
According to LeBrun, Nabla\u2019s system can further support clinicians by automating pre-charting, reviewing and organizing a patient\u2019s information in their EHR before an appointment, and coding medical data for use in areas like billing. Nabla has also expanded its platform with a built-in dictation capability, bringing clinicians closer to a unified experience. These kinds of AI assistant tasks can help to streamline and enhance clinical workflows and contribute to a reduction in institutional administrative costs.<\/p>\n
The promise of \u00a0
agentic AIagentic\u00a0AI<\/p>\n
Agentic AI, which companies like Nabla are currently working to integrate into their systems, promises to take the success of existing AI assistants a step further. LeBrun is looking to a future in which clinicians interact with an agentic platform that links to all the tools they already use and simplifies multi-step interactions, like reading patient data, acting within the EHR, and adapting to workflows in real time.<\/p>\n
\u201cRather 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 into a single, continuous workflow,\u201d LeBrun says.<\/p>\n
\u201cImagine a cardiologist getting ready for their morning clinic. After a few voice commands to instruct the system, one agent pulls the latest vitals, lab results, and imaging reports from the EHR, another generates a clear patient summary, and a third flags a missed follow-up echocardiogram. All before the patient even walks into the room,\u201d LeBrun explains.<\/p>\n
\u201cRather 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 into a single, continuous workflow.\u201d<\/p>\n
Alexandre LeBrun, Co-founder and Chief Executive Officer, Nabla<\/p>\n
Lee says that agentic AI\u2019s near-term scope includes standardized and protocolized non-clinical tasks, but he sees promise in areas like treatment options and other types of clinical decision support, where AI can safely operate with clinicians always \u201cin the loop.\u201d<\/p>\n
To get to this point, education is essential, says Lee. \u201cThe beauty of medicine is that it\u2019s a lifelong learning process. It\u2019s not just learning about the science behind medications, diagnoses, and treatments; it\u2019s about adapting to the use of new tools that will ultimately improve the care of the patients you treat,\u201d he explains.<\/p>\n
\u201cWe need to start with the basics of AI, making sure everyone understands what it is and how it works. Not how the programming takes place but more around what it can do, what it can\u2019t do, the risks and pitfalls, and then really understanding where it fits best in the care of patients,\u201d says Lee.<\/p>\n
Leadership must look ahead strategically and ensure the entire organization is moving forward with its use and understanding of AI, he adds. \u201cPart of that journey is involving frontline users to be part of the process, co-designing whenever possible and conducting pilots of new solutions so the organization can learn,\u201d Lee says. Additionally, \u201ca culture of inclusivity, authenticity, and transparency needs to be in place so you can be in the best position to be successful with transformative efforts such as incorporating and integrating agentic AI into the ecosystem,\u201d he says.<\/p>\n
\u201cPart of that journey is involving frontline users to be part of the process, co-designing whenever possible and conducting pilots of new solutions so the organization can learn.\u201d<\/p>\n
Dr. Ed Lee, Chief Medical Officer, Nabla<\/p>\n
Safely integrating \u00a0
into workflowsinto\u00a0workflows<\/p>\n
Applying AI to high-stakes sectors like health care requires a careful balance between productivity on the one hand, and accuracy on the other. \u201cTrust is everything in medicine,\u201d says LeBrun. \u201cEarning that trust means giving clinicians confidence through accuracy, transparency, and respect for their expertise.\u201d Nabla uses techniques like adversarial training models to check outputs, and it defaults to conservative responses. \u201cWe optimize precision. If we have a slight doubt, we prefer to remove something from the output by default,\u201d says LeBrun<\/p>\n
\u201cTrust is everything in medicine. Earning that trust means giving clinicians confidence through accuracy, transparency, and respect for their expertise.\u201d<\/p>\n
Alexandre LeBrun, Co-founder and Chief Executive Officer, Nabla<\/p>\n
New tools must also interweave with existing workflows and platforms to avoid adding more complexity for clinicians. \u201cAny product can look great, but if it doesn\u2019t fit well into your existing workflows, it\u2019s almost useless,\u201d says LeBrun.<\/p>\n
In sectors like customer service, it is straightforward to build a new interface or platform, but that approach isn\u2019t feasible\u2014or desirable\u2014in health care. \u201cIt’s a complex network of dependencies with so many workflows and processes,\u201d says LeBrun. \u201cEverybody would like to get rid of these things, but it’s not possible because you would need to change everything at once.\u201d Agentic AI approaches offer great promise to sectors like health care because they can \u201cimprove the process without getting rid of the legacy infrastructure,\u201c LeBrun explains.<\/p>\n
By simplifying complex systems, automating routine tasks, and continuing to take on more of the time-consuming burden of administrative work, agentic AI holds great promise in further augmenting ambient AI assistants. Ultimately, the technology\u2019s potential is not in making medical decisions or replacing clinicians, but in supporting health care workers to dedicate more of their time and attention to their main priority: their patients. \u201cAI should focus on supporting decisions and automating everything downstream,\u201d says LeBrun. \u201cThe first role of AI is to get physicians back to the state where they make medical decisions.\u201d<\/p>\n