Healthcare providers in the U.S. face significant pressure. Recent data shows that physicians can spend up to six hours each day on electronic health records (EHR) and clinical documentation. This heavy administrative workload contributes to clinician burnout. Although burnout rates dropped slightly from 53% in 2023 to 48% in 2024, it remains a major issue. With continuing workforce shortages and an aging population, reducing non-clinical tasks is becoming urgent.
AI-assisted ambient scribes use natural language processing (NLP) and voice recognition technology to transcribe conversations between patients and providers automatically. These tools capture patient encounters and create detailed clinical notes in real time. Unlike traditional scribes who accompany providers physically, ambient AI scribes work quietly in the background. They record complete consultation details and structure them into EHR-compatible formats immediately.
Ambient AI scribes are software systems that passively listen to and transcribe medical conversations. They rely on speech recognition and NLP to turn spoken language into organized, clinically relevant documentation. This includes SOAP (Subjective, Objective, Assessment, Plan) notes, prescriptions, referral letters, and follow-up instructions.
These AI tools overcome some limitations of human scribes:
One example is Sunoh.ai, which is used by more than 50,000 providers in the U.S. It offers real-time transcription embedded in EHRs such as eClinicalWorks. The system also captures diverse dialects and accents accurately, supporting equitable healthcare documentation.
Besides ambient scribes, automation tools that handle administrative tasks like phone calls and credentialing verification improve clinic operations. Combining AI-assisted documentation with workflow automation can make medical practices more efficient overall.
Simbo AI provides an example of AI applied to front-office phone automation and provider communication. Their AI Phone Copilot is created for medical practices and hospitals to handle high call volumes. It automates scheduling, answering patient questions, and insurance verification. This reduces front desk workload and increases patient access, leaving staff free for more complex tasks.
Credentialing, often slow and manual, also benefits from AI-powered automation. These tools can:
Using ambient AI scribes together with workflow automation solutions like those from Simbo AI, practices can improve both patient engagement and clinical documentation efficiency.
Industry surveys indicate that about 75% of healthcare providers expect AI ambient scribes to become mainstream within the next three years. The future workforce will likely see deeper integration of voice AI assistants, ambient listening, and automated workflows reducing documentation time and improving care access.
Organizations such as WellSpan Health and The Ottawa Hospital reported positive outcomes with early AI adoption. AI’s role will grow beyond documentation to tasks like referral coordination, order entry, and retrieving clinical evidence, as platforms like Microsoft Dragon Copilot show.
Medical practice leaders focusing on operations and technology should consider ambient AI scribes as part of their strategies to improve workflows and meet patient care needs without adding provider workload.
Using ambient AI scribes along with workflow automation such as front-office phone management can improve provider efficiency and reduce documentation burdens in U.S. medical practices and hospitals. As these tools develop and integrate better, healthcare administrators have an opportunity to implement AI solutions that have shown positive results in clinical settings.
The study aims to systematically review existing evaluation frameworks and metrics used to assess AI-assisted medical note generation from doctor-patient conversations, and to provide recommendations for future evaluations.
Ambient scribes are AI tools that transcribe discussions between doctors and patients, organizing the information into formatted notes, aimed at reducing the documentation burden for healthcare providers.
Two major approaches were identified: traditional NLP metrics like ROUGE and clinical note scoring frameworks such as PDQI-9.
Gaps include diversity in evaluation metrics, limited integration of clinical relevance, lack of standardized metrics for errors, and minimal diversity in clinical specialties evaluated.
Seven studies published between 2023-2024 met the inclusion criteria, focusing on clinical ambient scribe evaluation.
Most studies used simulated rather than real patient encounters, limiting the contextual relevance and applicability of the findings to real-world scenarios.
The study suggests developing a standardized suite of metrics that combines quantifiable metrics with clinical effectiveness to enhance evaluation consistency.
Developers contribute by creating novel metrics and frameworks for scribe evaluation, but there is still minimal consensus on which metrics should be measured.
Challenges include variability in experimental settings, difficulty comparing metrics and approaches, and the need for human oversight in grading and evaluations.
Real-world evaluations provide in-depth insights into the performance and usability of the technology, helping ensure its reliability and clinical relevance.
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