Introduction to AI Assistants in Healthcare
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. According to Dr. Ed Lee, Chief Medical Officer at Nabla, "For 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."
Benefits of Uninterrupted Patient Engagement
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. Alexandre LeBrun, co-founder and chief executive officer of Nabla, notes that "patients are very excited" about the use of AI devices during visits, as they feel they receive better care when their physician can focus on them.
Streamlining Clinical Workflows
Nabla’s system can further support clinicians by automating pre-charting, reviewing and organizing a patient’s information in their EHR before an appointment, and coding medical data for use in areas like billing. The platform has also expanded to include 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.
The Promise of Agentic AI
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 envisions 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.
Enhancing Clinical Decision Support
Dr. Lee believes 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 types of clinical decision support, where AI can safely operate with clinicians always "in the loop." To achieve this, education is essential, and Lee emphasizes the need for clinicians to understand the basics of AI, its limitations, and its potential applications in patient care.
Safely Integrating AI into Workflows
Applying AI to high-stakes sectors like healthcare requires a careful balance between productivity and accuracy. LeBrun stresses that "trust is everything in medicine," and that earning that trust means giving clinicians confidence through accuracy, transparency, and respect for their expertise. Nabla uses techniques like adversarial training models to check outputs and defaults to conservative responses to ensure precision.
Conclusion
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’s potential is not in making medical decisions or replacing clinicians, but in supporting healthcare workers to dedicate more of their time and attention to their main priority: their patients.
FAQs
- What is the primary benefit of using AI assistants in healthcare?
The primary benefit is that it liberates clinicians from time-consuming tasks, allowing them to fully engage with their patients. - What is agentic AI, and how does it differ from current AI assistants?
Agentic AI is a more advanced form of AI that can link to all the tools clinicians use, simplifying multi-step interactions and enhancing clinical decision support. - How can AI be safely integrated into clinical workflows?
AI can be safely integrated by ensuring accuracy, transparency, and respect for clinicians’ expertise, and by using techniques like adversarial training models to check outputs. - What is the ultimate goal of using AI in healthcare?
The ultimate goal is to support healthcare workers in dedicating more time and attention to their patients, rather than making medical decisions or replacing clinicians.
