2f:I[703058,["/_next/static/immutable/chunks/3bhq_5eh1d760.js","/_next/static/immutable/chunks/1855-ogyi8eh4.js","/_next/static/immutable/chunks/2pfsjpn6k6zlg.js","/_next/static/immutable/chunks/26c7v3_3uwnwu.js","/_next/static/immutable/chunks/25a3ozcrvi1ff.js","/_next/static/immutable/chunks/2r4wx50nvjh_4.js","/_next/static/immutable/chunks/23fsw06hsi5b1.js","/_next/static/immutable/chunks/06_yt270h41rg.js","/_next/static/immutable/chunks/3ezsiz-7shkna.js","/_next/static/immutable/chunks/20x2y96ldu5wx.js","/_next/static/immutable/chunks/2lxntcm3pd1_h.js","/_next/static/immutable/chunks/00dgco8irq3jw.js","/_next/static/immutable/chunks/00nwe-kq0rboc.js","/_next/static/immutable/chunks/2051ffx3xzio9.js"],"PaperListPageClient"] 30:T674,Generative artificial intelligence (AI) is reshaping the way clinicians record their clinical notes. AI-scribe systems leverage generative AI capabilities to transcribe clinical encounters into draft clinical notes. In this study, we assessed clinician uptake and estimated modelled documentation time savings for an AI-scribe system in an emergency department (ED). ED physicians and trainees were provided access to an AI-scribe for 5 weeks. Data from the first week were excluded. The transcript of each presentation, the initial AI-generated clinical note and the final EMR clinical notes were used to calculate time to finalise AI-assisted notes. Forty ED consultants and 23 trainees accessed the AI-scribe. Over the study period, nine consultants (22.5%) and 11 registrars (48%) used the system. The AI-scribe was used for 248 ED presentations, including 185 (74.6%) by trainees and 63 (25.4%) by consultants. The system generated 44,489 words. Following clinician review, 18,140 words were added and 2274 were deleted prior to submitting the final clinical notes. For a clinician with an average typing speed, use of the AI-scribe resulted in a time saving of 7.1 h of documentation. This was reduced to 4.9 h for rapid typer clinicians. Overall, the initial AI-generated notes were modified on 1143 occasions. The most frequently revised section was the history of presenting illness (23.3%) followed by the management plan (22.9%). Uptake of AI-scribe was higher among trainees than consultants, and the platform achieved substantial time savings. Future studies are required to quantify real-time productivity gains over longer periods.31:T9bf,ABSTRACT Background Generative artificial intelligence (AI) is reshaping the way clinicians record their clinical notes. AI‐scribe systems leverage generative AI capabilities to transcribe clinical encounters into draft clinical notes. In this study, we assessed clinician uptake and estimated modelled documentation time savings for an AI‐scribe system in an emergency department (ED). Method ED physicians and trainees were provided access to an AI‐scribe for 5 weeks. Data from the first week were excluded. The transcript of each presentation, the initial AI‐generated clinical note and the final EMR clinical notes were used to calculate time to finalise AI‐assisted notes. Results Forty ED consultants and 23 trainees accessed the AI‐scribe. Over the study period, nine consultants (22.5%) and 11 registrars (48%) used the system. The AI‐scribe was used for 248 ED presentations, including 185 (74.6%) by trainees and 63 (25.4%) by consultants. The system generated 44,489 words. Following clinician review, 18,140 words were added and 2274 were deleted prior to submitting the final clinical notes. For a clinician with an average typing speed, use of the AI‐scribe resulted in a time saving of 7.1 h of documentation. This was reduced to 4.9 h for rapid typer clinicians. Overall, the initial AI‐generated notes were modified on 1143 occasions. The most frequently revised section was the history of presenting illness (23.3%) followed by the management plan (22.9%). Conclusion Uptake of AI‐scribe was higher among trainees than consultants, and the platform achieved substantial time savings. Future studies are required to quantify real‐time productivity gains over longer periods. 25:[[["$","script","0",{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"CollectionPage\",\"name\":\"Emergency Department Documentation Burden: condition Research & Clinical Takeaways\",\"description\":\"Explore peer-reviewed papers on Emergency Department Documentation Burden (condition). Clinician-written summaries and actionable practice insights. Updated daily.\",\"url\":\"https://www.reachrx.ai/hub/tag/condition/Emergency%20Department%20Documentation%20Burden\",\"breadcrumb\":{\"@type\":\"BreadcrumbList\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https://www.reachrx.ai\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Research Hub\",\"item\":\"https://www.reachrx.ai/hub\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Conditions\",\"item\":\"https://www.reachrx.ai/hub/tag/condition\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Emergency Department Documentation Burden\",\"item\":\"https://www.reachrx.ai/hub/tag/condition/Emergency%20Department%20Documentation%20Burden\"}]}}"}}]],["$","$L2f",null,{"title":"Emergency Department Documentation Burden: Research & Clinical Takeaways","description":"Discover peer-reviewed research on Emergency Department Documentation Burden with concise clinician summaries and next-step guidance.","papers":[{"doi":"10.1111/1742-6723.70272","pmid":"42087797","title":"Evaluation of an\n AI\n Scribe Tool in the Emergency Department: A Single‐Arm Observational Study","journal_title":"Emergency Medicine Australasia","journal_issn":"1742-6731","published_at":"2026-05-06T00:00:00.000Z","url":"https://doi.org/10.1111/1742-6723.70272","source_score":2,"is_retracted":false,"is_open_access":true,"raw_metadata":{"ooir":{"day":"2026-05-10","doi":"10.1111/1742-6723.70272","days":"7","issn":"1742-6731","rank":"6","field":"Clinical Medicine","score":"2","category":"Emergency Medicine","categories":["Emergency Medicine"],"_categories":["Emergency Medicine"]},"pubmed":{"doi":"10.1111/1742-6723.70272","title":"Evaluation of an AI Scribe Tool in the Emergency Department: A Single-Arm Observational Study.","journal":"Emergency medicine Australasia : EMA","methods":"ED physicians and trainees were provided access to an AI-scribe for 5 weeks. Data from the first week were excluded. The transcript of each presentation, the initial AI-generated clinical note and the final EMR clinical notes were used to calculate time to finalise AI-assisted notes.","results":"Forty ED consultants and 23 trainees accessed the AI-scribe. Over the study period, nine consultants (22.5%) and 11 registrars (48%) used the system. The AI-scribe was used for 248 ED presentations, including 185 (74.6%) by trainees and 63 (25.4%) by consultants. The system generated 44,489 words. Following clinician review, 18,140 words were added and 2274 were deleted prior to submitting the final clinical notes. For a clinician with an average typing speed, use of the AI-scribe resulted in a time saving of 7.1 h of documentation. This was reduced to 4.9 h for rapid typer clinicians. Overall, the initial AI-generated notes were modified on 1143 occasions. The most frequently revised section was the history of presenting illness (23.3%) followed by the management plan (22.9%).","abstract":"$30","keywords":[],"pubmed_id":"42087797","conclusions":"Uptake of AI-scribe was higher among trainees than consultants, and the platform achieved substantial time savings. 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Interpret efficiency figures with caution.","value_numeric":null}],"summaries":{"what_studied":"This single-arm observational study evaluated clinician uptake and modelled documentation time savings of an AI-scribe tool (NexusMD.ai) in an Australian public hospital ED over 4 weeks (June–July 2025), covering 248 patient presentations by consultants and trainees.","key_result":"The AI-scribe saved 7.1 h of documentation time for average typists (40.8% efficiency gain) and 4.9 h for fast typists (35.3% gain) across 248 encounters. Trainee uptake (48%) was nearly double that of consultants (22.5%). Clinicians made 1,143 edits to AI-generated notes; the most revised sections were the history of presenting illness (23.3%) and management plan (22.9%).","limitations":"- Time savings were **modelled**, not directly observed; typing and reading speeds came from the general population, not ED clinicians.\n- Single-site study at one Australian ED limits generalisability.\n- Voluntary participation and exclusion of hospital medical officers/interns may inflate trainee uptake figures and undercount overall use.","practice_takeaway":"AI scribes can cut documentation time by ~35–41% in a busy ED setting, with trainees adopting them more readily than consultants. 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