Enhancing Structured Team Communication in Acute Care Settings with Ambient AI Scribes

Area of Science:

• Healthcare technology
• Clinical communication
• Artificial intelligence in medicine

Background:

• Team-based communication is vital for safe acute care, relying on standardized frameworks like IPASS.
• Current documentation of these discussions is often incomplete in electronic health records.
• Incomplete documentation hinders quality improvement (QI) and organizational learning.

Purpose of the Study:

• To explore the potential of ambient artificial intelligence (AI) scribes in acute care settings.
• To enhance documentation of structured, team-based provider-to-provider communication.
• To support quality improvement (QI) initiatives through better data capture.

Main Methods:

• Outlining the application of ambient AI scribes for documentation of daily rounding and handoff discussions.
• Examining sociotechnical challenges: workflow integration, consent, surveillance, and vendor collaboration.
• Describing proof-of-concept demonstrations for feasibility assessment.

Main Results:

• Ambient AI scribes show promise for capturing structured team communication.
• Potential benefits include improved documentation, clinician well-being, and data-driven QI.
• Successful implementation necessitates workflow adaptation, verification, governance, and trust-building.

Conclusions:

• Ambient AI scribes offer a novel approach to documenting structured team discussions in acute care.
• This technology can strengthen communication reliability and systems learning.
• Future research should assess impacts on patient safety, workforce well-being, and patient outcomes.