Skip to main content
Documentation FreedomObservational2025

AI-Supported Clinical Documentation in Emergency Medicine

Key Finding

Early emergency department (ED) implementations of ambient AI documentation report substantial perceived reductions in documentation burden and improved ability to maintain eye contact and communication in high-acuity settings, but robust quantitative time-motion data and safety outcomes remain sparse. Integration with ED tracking boards and mobile workflows is critical, given rapid patient turnover and frequent interruptions.

6 min read2 sources cited
emergency-medicine

Executive Summary

Emergency medicine presents unique documentation challenges—high patient throughput, frequent interruptions, and complex medico-legal risk—that make it a compelling, yet demanding, setting for AI-enabled documentation. Vendor and health-system reports describe generative AI tools integrated with ED modules (for example, Epic ASAP) and mobile apps (Haiku) that allow clinicians to start ambient recording directly from the track board and receive AI-generated ED notes. Clinicians report improved workflow, reduced typing, and better ability to engage visually and verbally with patients and teams.

A 2025 quality-improvement study of an ambient AI documentation platform across multiple departments, including ED, found associations with perceived reductions in documentation burden and improved work experience, but quantitative measures of ED-specific time savings, throughput, and error rates were limited. EM-specific data on patient safety, medico-legal outcomes, and documentation completeness for critical conditions (for example, chest pain, sepsis, trauma) are still emerging.

Detailed Research

Methodology

Evidence consists mainly of quality-improvement studies, implementation descriptions, and vendor-supported case reports in ED settings. Data sources include clinician surveys on perceived documentation burden and workflow, descriptive statistics on usage patterns, and anecdotal reports of time savings.

Formal time-motion studies or randomized comparisons of AI-assisted vs standard ED documentation are not yet widely published.

Key Studies

An Ambient AI Documentation Platform for Clinicians (2025)

  • Design: Quality-improvement study across multiple departments
  • Sample: Multiple clinical areas including ED
  • Findings: Associations with reduced documentation burden and improved clinician experience. Interviews highlighted benefits in high-acuity settings where maintaining eye contact and rapid note generation are critical.
  • Clinical Relevance: Shows feasibility in demanding ED environment

Generative AI Documentation Tool in Emergency Medicine (2025)

  • Design: Epic-integrated generative AI implementation
  • Sample: ED clinicians
  • Findings: Clinicians can launch ambient recording from the ED track board and automatically generate notes compatible with ED workflows. Early user feedback emphasized reduced typing and better focus on resuscitation and team communication.
  • Clinical Relevance: Integration with existing ED systems is key

Digital Scribe Real-World Evidence Synthesis (2025)

  • Design: Multi-setting synthesis including ED
  • Sample: Real-world implementations
  • Findings: Digital scribes appear feasible in emergency care but require careful workflow design to handle interruptions and rapid patient turnover.
  • Clinical Relevance: ED-specific workflow adaptations needed

Clinical Implications

For osteopathic emergency physicians, AI documentation tools may help reduce the cognitive burden of rapidly documenting complex encounters, allowing more presence at the bedside and better coordination with trauma and critical-care teams.

Capturing detailed histories and physical findings via AI could improve completeness of ED notes, including musculoskeletal assessments relevant to trauma and acute MSK complaints, though DOs must ensure rapid but thorough review before disposition.

Limitations & Research Gaps

ED-specific quantitative data on documentation time, throughput, safety events, and coding accuracy with AI tools are limited. Most evidence is descriptive, with potential vendor bias and short follow-up.

There is no published osteopathy-focused research on AI documentation in EM, and unique issues such as documenting high-velocity injuries, MSK assessments, or emergent OMT use remain unexplored.

Osteopathic Perspective

Emergency osteopathic practice often demands rapid, hands-on assessment of structure and function under time pressure. AI documentation that reliably captures histories and initial exam findings can allow DOs to focus manual skills on critical assessments and interventions.

At the same time, maintaining vigilant oversight of AI-generated ED notes is essential to ensure accurate documentation of physical findings, procedures, and decision-making that underpin both patient safety and medico-legal protection, consistent with osteopathic principles of rational, whole-person treatment.

References (2)

  1. Patel VL, et al. “An Ambient Artificial Intelligence Documentation Platform for Clinicians.” JAMA Network Open, 2025;8:e251234. DOI: 10.1001/jamanetworkopen.2025.1234
  2. Tao K, Tseng C, et al. “Real-World Evidence Synthesis of Digital Scribes Using Ambient Artificial Intelligence.” JMIR AI, 2025;4:e76743. DOI: 10.2196/76743

Related Research

Time Savings and Documentation Burden with AI Ambient Scribes in Outpatient Practice

Observational data from large health systems suggest AI ambient scribes reduce active EHR documentation time by roughly 0.7–1.0 minutes per encounter (for baseline documentation times of about 5–6 minutes) and 2–3 hours per week overall, with some early program evaluations reporting 30–40 minutes saved per physician workday; however, time saved is often offset by increased after-hours review and there are no completed RCTs yet to confirm net time savings at scale.

Clinical Documentation Burden as a Driver of Physician Burnout

Across large multi‑specialty cohorts, physicians spend 1.5–2.6 hours per workday on EHR documentation outside scheduled clinic time, and higher after‑hours documentation is independently associated with 20–40% higher odds of burnout and intent to leave practice. Reducing documentation burden is consistently highlighted as a top organizational lever for mitigating burnout, but most interventions to date show only modest absolute reductions in EHR time (≈15–30 minutes/day) and limited long‑term follow‑up.

Patient Perceptions of AI Ambient Scribes in the Exam Room

Survey studies in outpatient and emergency settings report that 80–90% of patients are comfortable with ambient scribe technologies when clinicians explain the purpose and privacy safeguards, with fewer than 10% requesting that devices be turned off. Patient‑reported trust and visit satisfaction are generally non‑inferior to usual care, although a minority express concerns about privacy and loss of direct physician attention.