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Free AI programs can create a logically derived discharge summary from a SOAP note.

About the Role Mercor is hiring certified medical coders on behalf of a healthcare AI partner building advanced clinical documentation tools. In this role you…

Heidi Health (heidihealth.com) is an AI-powered medical scribe that automates clinical documentation by transcribing patient consultations in over 110

Background: Health care organizations have started to implement artificial intelligence (#AI)–powered ambient scribe technology in clinical documentation workflows. Early outpatient studies have shown mixed results. Few studies have evaluated ambient scribes in the emergency department (ED). Due to differences in setting and patient acuity between the ED and ambulatory clinics, there remains a pressing need to research this technology in the ED. Objective: This study aimed to evaluate emergency physicians’ (EPs) satisfaction, perceived documentation efficiency, after-shift documentation time, and trust in ambient scribe–generated notes compared with in-person scribes or independent documentation, and to identify ED-specific challenges. Methods: A cross-sectional survey was conducted among 16 board-certified adult and pediatric EPs who were granted access to the ambient scribe technology across 4 EDs. EPs used the ambient scribe for several months before completing a survey with multiple-choice and free-text responses. We performed a mixed methods analysis by summarizing quantitative data through descriptive statistics and performing a practical thematic analysis on free-text responses. Results: Of the 16 EPs, 14 (87.5%) completed the survey. Among respondents, 9 (64.3%; 95% CI 38.7%-83.7%) reported being satisfied or very satisfied with the ambient scribe, while 3 (21.4%; 95% CI 7.6%-47.6%) expressed dissatisfaction. When given the option, 7 (50%; 95% CI 26.8%-73.2%) respondents preferred the ambient scribe, 4 (28.5%; 95% CI 11.7%-54.6%) preferred in-person scribes, and none (95% CI 0%-20.6%) preferred independent documentation. Among previous users of in-person scribes, 50% (4/8; 95% CI 21.5%-78.5%) favored the ambient scribe. The ambient scribe was reported to improve documentation efficiency (10/14, 71.4%; 95% CI 45.4%-88.3%) and reduce after-shift documentation time (9/14, 64.3%; 95% CI 38.7%-83.7%). However, only 42.9% (6/14; 95% CI 22.3%-69.2%) of respondents trusted the accuracy of ambient scribe–generated notes, compared with 75% (6/8; 95% CI 40.9%-92.9%) who trusted in-person scribes. Few respondents found the ambient scribe helpful for physical examinations (3/13, 23.1%; 95% CI 8.2%-50.3%) or medical decision-making documentation (5/14, 35.7%; 95% CI 16.3%-61.2%). A thematic analysis identified 5 themes: challenges due to the workplace environment, challenges due to the patient population, workflow improvement, workflow harm, and narrow usefulness. Conclusions: This mixed methods pilot study is among the first to evaluate ambient scribe technology in the ED. Our results add ED-specific insights to literature focused on the outpatient setting. Our findings reveal the potential for enhancing documentation efficiency and reducing administrative burden while highlighting setting-specific challenges. While most EPs preferred artificial intelligence (#AI)–assisted documentation over independent charting, confidence in documentation accuracy and functionality remains limited compared with human scribes and varies by note component. As the demand for efficiency in emergency medicine continues to grow, scalable solutions such as ambient scribes could play a pivotal role if functionality, reliability, and physician trust can be further optimized.

A study led by researchers at the University of Toronto found that artificial intelligence (AI) scribes substantially reduced the amount of time physicians spent documenting patient visits during simulated primary care appointments. The study, published in JAMIA Open, involved nine physicians who completed simulated encounters both with and without AI scribe technology. Researchers analyzed video recordings to compare how physicians allocated their time. When AI scribes were used, physicians spent 69.1% less time on clinical documentation. Without the technology, doctors devoted more than one-third of each simulated encounter to note-taking. AI scribes instead recorded the encounter and generated structured clinical notes that physicians could subsequently review and edit. Researchers observed that this workflow reduced interruptions during patient interactions and shifted documentation activity toward reviewing and correcting AI-generated notes. The researchers also identified an unexpected change in physicians' workflow. Some doctors used scratch pads to record thoughts and reminders during appointments, suggesting that conventional note-taking may support clinical reasoning and the formulation of follow-up questions. This finding highlights that reducing documentation time could also change how physicians organize information and make clinical decisions during consultations. The research is part of a broader University of Toronto and Women's College Hospital project examining AI technology in Canadian health care. The researchers emphasized the importance of studying the Ontario context because privacy regulations and health-care practices can differ from those in the United States. The findings do not establish how AI scribes will perform in routine clinical practice. The study used simulated encounters and involved only nine physicians. Further research is needed to examine real-world documentation quality, patient acceptance, privacy concerns, consent, and effects across different patient populations. Researchers also note that AI scribes address only one component of physicians' administrative workload, which can also include referrals, prescription orders, billing, and regulatory requirements.

In this post, we explore how to fine-tune a leaderboard-topping, NVIDIA Nemotron Speech Automatic Speech Recognition (ASR) model; Parakeet TDT 0.6B V2. Using synthetic speech data to achieve superior transcription results for specialised applications, we'll walk through an end-to-end workflow that combines AWS infrastructure with the following popular open-source frameworks.

AI clinical documentation platform T-Pro has acquired BigHand Healthcare, now working with 120 NHS Trusts.