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Emergency physicians experience substantial documentation burden, contributing to physician burnout. Human scribes reduce documentation workload but are expensive and pose staffing challenges. Ambient artificial intelligence (AI) scribes offer a potential alternative by automating note generation from clinician-patient conversations using AI. We compared ambient AI and human scribes against encounters with no scribe on emergency physicians' documentation time and clinical productivity. This retrospective cross-sectional observational study evaluated emergency department encounters from January 2025 to September 2025 at 4 hospitals within a large integrated health care system. Encounters were categorized as ambient AI scribe, human scribe, or no scribe. Patient demographics, documentation time, work relative value units (wRVUs), and shift data were extracted. Attending documentation time was modeled using median quantile regression with standard errors clustered at the physician level and controlling for encounter-level variables. Clinical productivity measured as total wRVUs per shift hour was modeled using generalized estimating equations adjusting for physician and shift-level variables. Among 198,178 emergency department encounters, 8,489 (4.3%) used ambient AI scribes, 15,947 (8.0%) used human scribes, and 173,742 (87.7%) had no scribe. Median patient age was 49 years (interquartile range 30 to 68 years), and 53.1% were female. Compared with encounters with no scribe, ambient AI scribes were associated with a 1.6-minute reduction in adjusted median attending documentation time per note (95% confidence interval 0.3 to 2.9), whereas human scribes were associated with a 3.3-minute reduction (95% confidence interval 2.3 to 4.3). Total wRVUs per shift hour did not differ among groups. Ambient AI and human scribes were associated with reduced physician documentation time. Clinical productivity did not differ between study groups.

To investigate the performance of artificial intelligence (AI) scribes and their impact on clinical documentation time. Artificial intelligence scribes were assessed using clinical simulations with 9 primary care physicians, each conducting 4 simulated encounters with standardized patients with and without the use of an AI scribe. Clinical simulations were audio and video recorded, and videos were coded for documentation behaviors using a designed coding scheme. Clinical documentation accounts for 36.3% of the simulated clinical encounter without the use of the AI scribe, whereas primary care physicians spend 11.2% of the simulated encounter on clinical documentation when using an AI scribe. The use of AI scribes accounts for a 69.1% reduction in documentation time. Clinical simulations offered a controlled and realistic environment to assess the impact of AI scribes on primary care physicians' workflows, demonstrating their potential to reduce documentation time. Artificial intelligence scribes demonstrate potential to alleviate the burden of clinical documentation, addressing a critical factor contributing to physician burnout. Further research is needed to understand the impact of AI scribes in real-life clinical settings across complex patient scenarios.

Ambient artificial intelligence (AI) scribes have the potential to reduce documentation burden and improve efficiency, clinician experience, and care quality. However, evidence from large-scale implementations in emergency departments (EDs), particularly regarding patient experience and documentation quality, remains limited. We aimed here to evaluate the implementation of an ambient AI-powered scribe in EDs, assessing adoption, efficiency, transcription accuracy, clinician experience, patient experience, and medical report quality. We conducted a 12-month multicenter retrospective observational study (February 2025-January 2026) across five emergency specialties in 48 Spanish hospitals. All level 4 and 5 emergency consultations were included, with approximately 2.27 million emergency visits during the study period. We analyzed monthly adoption rates, consultation duration, transcription accuracy, patient experience measured by Net Promoter Score, clinician experience assessed through cross-sectional surveys, and documentation quality evaluated via structured audits. Scribe was used in 1,032,558 consultations (45.3&#xa0;%), with monthly adoption increasing from 7.7&#xa0;% to 57.8&#xa0;%. A total of 2,097 physicians used Scribe at least once, including 1,198 high-intensity users. Scribe-assisted consultations were significantly shorter (p&#xa0;<&#xa0;0.001), with a mean relative time savings of 21.8&#xa0;%. Transcription accuracy remained high and stable throughout the study (mean: 93.9&#xa0;%). Audits of clinical reports showed higher documentation quality scores for Scribe-generated reports, with a large effect size and consistent improvements across physicians. Patient experience was significantly higher in Scribe-assisted consultations, and clinician experience was positive overall, particularly regarding workload, stress, and physician-patient interaction. Implementation of an ambient AI scribe in emergency care was feasible, scalable, and associated with improved efficiency, higher patient experience, enhanced documentation quality, and positive clinician experience. These findings support the role of ambient AI scribes as a value-based health care intervention that simultaneously improves care quality, patient experience, and provider well-being.

In intensive care unit (ICU) settings, structured team-based communication, such as multidisciplinary rounds, handoffs, and goals-of-care discussions, is foundational to high-quality care. However, accurately documenting these complex discussions in the medical record remains a challenge due to time pressures, documentation burdens, and competing clinical demands. Ambient artificial intelligence (AI) scribes, which passively transcribe and summarize spoken interactions, offer a potential solution to assist ICU clinicians with documentation. Yet, little is known about how ICU clinicians perceive the integration of these tools into their high-stakes, collaborative workflows. This study explores clinicians' perceptions of integrating ambient AI scribes into structured team-based ICU discussions, including multidisciplinary rounds, handoffs and transitions of care, and goals-of-care discussions, with the broader goal of informing the implementation of these scribes into real-world ICU clinical workflows. Interviews and focus groups were conducted with ICU clinicians, including nurses, attendings, trainees (residents/fellows), respiratory therapists, and advanced practice practitioners, who routinely participate in structured ICU discussions. Transcripts were analyzed using grounded theory to identify documentation needs, barriers to documentation, and considerations for the implementation of ambient AI scribes in the ICU setting. A total of 52 individuals, including 18 ICU attendings, 5 advanced practice practitioners, 10 ICU trainees, 9 ICU nurses, and 10 ICU respiratory therapists, participated. Clinicians emphasized the importance of accurate documentation, but noted persistent barriers such as time constraints, documentation burden, and competing teaching and patient care responsibilities. Clinicians expressed enthusiasm about ambient AI scribes' potential to reduce documentation burden and improve quality, but requested personalization of outputs, robust consent protocols, and transparency around data use. Participants viewed ambient AI scribes as a promising tool to enhance both documentation fidelity and communication quality in ICU settings. Successful implementation may be contingent upon transparent data governance, specialty-specific customization, and sustained efforts to build clinician trust. ICU clinicians were optimistic about the potential of ambient AI scribes to ease documentation burden and improve the capture of critical clinical discussions, but expressed concerns over transparency regarding data use. Successful implementation may depend on clinician training, customization of output, and transparent institutional policies on data use and consent.

Clinician burnout has reached crisis levels in emergency medicine, with clinical documentation burden identified as a central contributing factor. Ambient artificial intelligence (AI) scribes offer a promising approach to reduce this burden, but objective evidence in the emergency department (ED) setting remains limited, and prior reports have been constrained by short observation windows and low adoption. This study aimed to evaluate the association between ambient AI scribe use and on-shift documentation time during a 13-month staged rollout in a busy ED, accounting for physician- and patient-level factors. We conducted a retrospective cohort study at a tertiary academic ED from February 2025 to March 2026. The analytic cohort comprised 10,344 encounters managed by 100 attending physicians across 4 ED care settings. We restricted analysis to encounters managed by a single attending physician and excluded those with human scribes. The comparison group comprised encounters in which the ambient AI scribe was not used; use was determined entirely at attending physician discretion on an encounter-by-encounter basis. The primary outcome was on-shift documentation time derived from electronic health record audit logs. We used mixed-effects linear models with physician random intercepts to adjust for patient and encounter characteristics. Ambient AI scribe use was associated with a 72.6-second reduction in on-shift documentation time per encounter (95% CI 63.8-81.4; P<.001). The effect was similar in magnitude for high-use physicians (use rates of &#x2265;18.2%, which was the cohort mean; -71.6 seconds) and low or moderate users (-64.2 seconds), with no statistically significant difference (P=.51). Note character count decreased by 690 characters (95% CI 273-1107; P=.001); after-shift documentation time increased modestly by 9.1 seconds (95% CI 2.9-15.3; P=.004). Negative control outcomes were largely null, and a within-clinician placebo permutation test yielded a distribution centered at 0 (mean -0.8 seconds), inconsistent with the observed effect arising from confounding alone. In this single-center analysis, ambient AI scribe use was associated with a statistically significant reduction in on-shift documentation time (P<.001), equivalent to approximately 24 minutes per 8-hour shift if used across 20 encounters. These findings extend prior descriptive work with adjusted inferential evidence and support the clinical relevance of ambient AI scribes for ED documentation burden, although the magnitude of benefit varies by physician, patient, and workflow factors.

Ambient Artificial Intelligence (AI) medical scribes are emerging technologies designed to streamline clinical documentation. Although promising results have been reported in medical disciplines, limited research has examined their suitability for use in allied health services. To evaluate clinician acceptance and patient acceptability of an AI scribe within allied health settings at an Australian public hospital and health service. A mixed-methods design incorporating surveys and semi-structured interviews with allied health clinicians and patients. In total, 97 eligible clinician survey responses were analysed, and 27 clinicians participated in interviews. Most clinicians reported high perceived ease of use, citing the AI scribe's intuitive design and adaptability. However, customisation challenges impacted their overall user experience. Perceived usefulness was also high, with reported improvements in documentation quality, workflow efficiency, patient care, and job satisfaction. Clinicians emphasised the importance of reviewing AI-generated documentation to ensure accuracy and patient safety. They also highlighted that, in certain contexts, the use of AI scribes requires careful consideration to ensure patient care and engagement are maintained and that use with some patients (i.e., those experiencing persecutory delusions involving surveillance) may be contraindicated. While patient sample size was limited (19 surveys, four interviews), data indicated overall comfort with the AI scribe, although concerns were raised regarding data privacy and accuracy. AI scribes show promise for allied health practice, offering multiple benefits recognised by both clinicians and patients. Their effective use may depend on clinician review of generated notes, careful consideration of patient wellbeing, and addressing usability challenges related to customisation and integration. Responding to the concerns raised by both groups may be key to ensuring confident and sustainable adoption.

Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-facing time, and increased burnout. Artificial intelligence (AI) medical scribes have emerged as a promising solution to reduce this burden, yet real-world evidence remains limited and heterogeneous, and data from European health systems are especially scarce. This evaluation combines 2 complementary data sources: objective editing metadata from 236,153 notes generated by 1295 clinicians, describing operational editing behavior within the AI medical scribe, and paired self-reported survey responses from 177 fully onboarded clinicians, capturing perceived change in documentation time and clinician experience. This study aimed to evaluate the association of an AI medical scribe on documentation time and clinician experience. This observational real-world evaluation was conducted between April 26, 2024, and October 27, 2025, using retrospective paired ratings. The study was carried out across multiple specialties in primary, secondary, and hospital care within Capio Ramsay Sant&#xe9;, a large integrated health care provider operating in Sweden. Eligibility was limited to fully onboarded users, defined as clinicians who had used the scribe for at least 3 months, created more than 100 notes, generated at least 1 document or certificate, and used the conversational edit ("Add or adjust") feature at least once. Following the introduction of the AI medical scribe, the estimated time spent on documentation per note was lower than before (4.72 vs 6.69 minutes; -29%, P<.001). On a 5-point Likert scale, ratings for the ability to work without stress related to administrative tasks were higher after introduction than before (mean 3.14 vs 2.41; P<.001; median change 0 points, 95% CI 0-1), as were ratings for perceived presence with patients (mean 4.33 vs 3.73; P<.001; median change 0 points, 95% CI 0-1). The median editing time was 93 seconds, and it did not decrease significantly over continued use. Among sustained, fully onboarded adopters in a European health care system, use of an AI medical scribe was associated with reductions in self-reported documentation time, administrative stress, and increase of presence with patients, consistent with findings from prior US-based studies. Because the survey cohort represents a highly selected subgroup of users who adopted and continued using the tool mainly in general practice, these associations may not generalize to clinicians who discontinued use or never fully adopted the scribe, and the generalizability across specialties remains unverified. The single-arm observational design and reliance on retrospective self-report are important considerations when interpreting these associations. A limitation of this analysis is that 138,196 notes were excluded because their recorded editing time was 0; these notes may have been used as generated, used as a starting point and later modified in the medical record system, or discarded, which limits the operational interpretation of the editing-time findings.

This study assessed over 2000 patient perspectives on the use of ambient AI scribes in outpatient visits. This prospective quality improvement study was conducted at Stanford Health Care between May and July 2025. Outcome measures included patient perceived helpfulness of ambient AI scribes and patient interest in future use. Among 2202 survey respondents, 70.1% patients found the ambient AI scribe helpful and 73.6% preferred future use of ambient AI scribes. Small but statistically significant differences were observed across gender, age, and race. In an evaluation of patient perceptions of an ambient AI scribe integrated into clinical practice, the majority of patients who had experienced the technology found it acceptable, and most found the tool helpful and desired use with future visits. These results suggest that ambient AI scribes may be acceptable to many patients in clinical practice. Further research is needed to inform patient-centered design and workflow improvements.

Medical scribes are increasingly used in outpatient clinics in the United States to reduce physician documentation burden, but their economic and operational impact remains inconsistently characterized. This scoping review synthesized evidence on the impact of medical scribes in outpatient care in the United States, focusing on physician productivity, documentation efficiency, and financial outcomes. Nine databases were searched from January 2010 to September 2024 for English-language studies reporting economic or productivity outcomes of medical scribe use in outpatient settings. Two reviewers independently screened 1,945 unique records; data were charted using a piloted template and synthesized thematically. Twenty United States-based studies met the inclusion criteria: nine pre-post evaluations, five controlled cohort studies, two randomized controlled trials (RCTs), two microsimulation models, and two systematic reviews. Scribes consistently improved physician productivity: 19 of 20 studies (95%) reported increased work relative value units (wRVUs), higher patient throughput, and reduced documentation time. Clinician satisfaction improved across 9 studies; patient experience remained stable. Economic outcomes varied: 13 of 20 studies attempted return on investment (ROI) analysis, but only 6 included comprehensive costs (wages, training, licensing, turnover). Cost neutrality was achievable in high-volume clinics with predictable scheduling and unutilized capacity, but financial returns depended heavily on specialty, baseline productivity, and cost accounting rigor. Medical scribes enhance operational efficiency and physician workflow in United States outpatient clinics, though economic value is context-dependent and incompletely documented. Standardised cost frameworks and controlled evaluations in non-United States health systems are needed to guide implementation decisions.

ConspectusThe widespread adoption of graphene in commercial technology has long been hindered by the difficulty of producing high-quality material at scale and patterning it into functional circuits without complex, multistep lithography. This Account explores our laboratory's solution to this challenge: the photothermal transformation of solution-processable graphene oxide (GO) into laser-scribed graphene (LSG). Unlike pristine graphene, which is difficult to disperse and pattern, GO is easily synthesized from bulk graphite and can be cast into uniform films on virtually any substrate via scalable solution-processing techniques.The transition from GO to LSG is significantly simplified through the use of a standard CO2 laser source. We observed that laser-scribed GO areas undergo highly effective reduction, characterized by a rapid expansion and exfoliation of the layers. This results in a film with exceptional conductivity and high porosity, yielding a remarkable surface area of 1520 m2 g-1. This reduction process fundamentally alters the chemical composition of the film, shifting it to a carbon content of 96.5% with a residual oxygen content of only 3.5%.Beyond pure carbon architectures, the core of our recent research focuses on the chemical diversification of the LSG library. After the first finding of laser-scribed graphene for supercapacitors, we found that by utilizing GO as a versatile host matrix, we have demonstrated that functional additives can be seamlessly integrated into the 3D graphene network during the scribing process. This Account will highlight our work from pure laser-scribed graphene to various materials with LSG as a host matrix, including laser-assisted lattice recovery of graphene by carbon nanodot incorporation, Si/LSG composites for high-capacity lithium-ion anodes, and the incorporation of VCl3 to create multivalent vanadium oxide/graphene hybrids for advanced sodium-ion batteries.These hybrid materials allow for precise tuning of the electrochemical and electronic properties of the resulting devices. We will detail how this laser-scribe approach enables the integration of these materials into multifunctional electronic systems. By bridging the gap between molecular-level carbon chemistry, heteroatom doping, and roll-to-roll manufacturing, LSG provides a blueprint for the future of wearable electronics and sustainable, integrated energy systems.

Any tool that can reduce the administrative burden on healthcare providers while preserving safe, accountable and high-quality medical documentation is of immense value both to healthcare institutions and consumers. The key question we need to answer is whether a prospective tool can reduce these burdens while maintaining (and, ideally elevating) quality documentation standards. The goal of this study is to describe the local performance of a large language model-based documentation assistive tool to draft safe, high-quality documentation in the Child Development Unit at the Women's and Children's Hospital. By generating local evidence of performance, we can assess the suitability of the artificial intelligence (AI) Scribe and inform a larger interventional study protocol and establish evidence-based governance. Using an algorithmic audit framework developed specific to our context, we will compare clinician-written clinical notes to AI-generated notes produced in parallel to the standard of care (ie, a 'silent' or translational trial paradigm). We will compare the time required to review clinical documentation per the standard of care compared with the AI-supported workflow with consideration to the accuracy of the final documentation. Finally, we will qualitatively describe AI-generated notes and compare them to the current standard to identify specific areas where clinical guidelines (eg, performance information, risk mitigation) would support appropriate clinical use. Ethics approval has been obtained by the Women's and Children's Health Network Human Research Ethics Committee (HREC) (HRE00067) and the South Australian Aboriginal HREC (#04-25-1185). This protocol offers an accessible example for health institutions looking to apply an evidence-based approach to AI Scribe assessment that prioritises clinical documentation standards. We will publish our study results in an academic journal and include a publicly accessible summary for the general public on the Women's and Children's website. 10.17605/OSF.IO/P6TM5.

Artificial intelligence (AI) medical scribes are an emerging technology intended to reduce the documentation burden. In pediatrics, evidence on implementation in the Spanish context is very limited. To evaluate the implementation of an AI medical scribe in pediatric outpatient clinics, examining its effects on clinician-patient interaction, documentation quality, and barriers to adoption. Prospective observational proof-of-concept study (February-October 2025) conducted at a tertiary pediatric hospital. Forty physicians from three subspecialties participated, and 197 visits were documented. The evaluation combined direct observations (n = 30), surveys of clinicians (n = 18) and patients (n = 20), and a quality assessment using a rubric based on the mPDQI-9. With the AI scribe, eye contact increased (median 95.7% vs. 78.6%; p < 0.001), with no significant change in visit duration (25.7 vs. 21.9 min; p = 0.262). Documentation quality improved in accuracy/completeness (p = 0.008), organization/structure (p < 0.001), and overall score (median 23 vs. 17; p = 0.008). Documentation of information provided to the patient was more frequent (80% vs. 20%; p = 0.003). Post-visit review time accounted for 33%-38% of total time. Fifty-five percent of users reported being satisfied or very satisfied, although 67% reported technical issues and 33% reported requiring extensive corrections. Adoption was concentrated in fewer than one-third of trained clinicians. The AI medical scribe was associated with improvements in interaction and selected domains of documentation quality. However, technical barriers and limited adoption persisted and should be addressed before wider deployment.

Documentation burden and cognitive load contribute to clinician burnout, and ambient AI scribes offer a promising solution, though real-world evaluations remain limited. This study evaluated the impact of an ambient AI scribe on documentation burden, cognitive workload, and perceived burnout in clinical practice. Forty clinicians participated in a 60-day pilot during which the AI scribe generated draft notes from encounter audio. Pre- and post-implementation surveys included validated single-item burnout measure, workflow assessments, NASA Task Load Index (NASA-TLX) scores, and EHR-autologged data on after-hours documentation. Clinicians reported significant reductions in perceived documentation time and after-hours work (all p < 0.001), with large effect sizes (r = 0.73-0.80). NASA-TLX domains demonstrated significant decreases in mental demand, physical demand, effort, time pressure, and stress (all p &#x2264;0.001; g = 0.76-1.54). In contrast, objective EHR documentation minutes did not change significantly (t (15) = 1.52, p = 0.15, g = 0.36). Perceived documentation time aligned more closely with objective EHR-derived categories following implementation. These findings suggest that ambient AI scribes meaningfully reduce perceived workload and improve calibration of documentation burden, even when measurable documentation time remains stable.

Documentation burden contributes significantly to psychiatric clinician burnout, with psychiatrists spending an average of three hours per workday on administrative tasks rather than direct patient care. While quantitative studies demonstrate that ambient artificial intelligence (AI) scribes reduce workload and improve documentation quality, the experiential perspectives of clinicians and standardized patients (SPs) remain unexplored, particularly in psychiatry where the therapeutic relationship is central to practice. Our aims were to explore clinician and standardized simulated patient perspectives on ambient AI scribes in psychiatric consultations, examining experiences, concerns, and conditions for successful implementation. This study was part of a cross-sectional quantitative simulation study, where qualitative part was planned a-priori. We conducted focus groups with psychiatrists and SPs following a simulation-based crossover study comparing traditional versus AI-assisted documentation. Data were analyzed using Braun and Clarke's reflexive thematic analysis framework. In this focus group session with eight psychiatrists and four SPs, total six themes were identified: (1) Clinical Presence, whereby reduced documentation barriers were perceived to allow more authentic human connection; (2) Liberation from documentation burden, with clinicians describing cognitive and emotional relief beneficial for their mental health; (3) Clinical Competence Amplified, in which clinicians perceived the AI scribe as enhancing practice through intelligent translation to psychiatric terminology and prompting for missed assessments; (4) Calibrating Trust, where participants navigated initial uncertainty while recognizing the importance of human oversight; (5) Privacy, stigma, and psychiatric exceptionalism, highlighting unique consent and confidentiality considerations for psychiatric populations; and (6) Perceptions of an enhanced standard of care, with both groups expressing strong preference for AI-assisted consultations with conditions for adoption. In this exploratory simulation-based qualitative study, AI scribes were perceived as having potential to support more present, patient-centered psychiatric consultations by reducing documentation burden. Implementation should be cautious and requires transparent consent processes, clinician training in behavioral adaptations, robust human oversight, specialty-specific templates, and real-world evaluation with patients receiving psychiatric care.

Ambient artificial intelligence (AI) documentation systems have emerged as a promising strategy to reduce electronic health record burden and support patient-centered, value-based healthcare (VBHC). Early studies report gains in efficiency and clinician well-being, but large-scale evaluations outside North America are limited. We aimed here to assess the 16-month real-world implementation of an ambient AI documentation system (Scribe) in outpatient care in Spain, focusing on its adoption, transcription semantic agreement, efficiency, and clinician experience. A retrospective, multicenter observational analysis was performed using aggregated operational data from September 2024 to December 2025. Monthly Scribe adoption, consultation duration by modality, and cosine-similarity-based semantic agreement were evaluated. Professional experience was assessed through two cross-sectional surveys, including usage-intensity stratification. Statistical analyses included descriptive metrics, Welch tests, Hedges' g, FDR correction, and reliability and correlation analyses. Scribe adoption increased from 2.7% to &#x223c;31% of all outpatient visits, totalling more than 2.33 million assisted encounters. Consultation duration showed modest differences (15.01 vs. 14.65&#x2005;min), with several months favouring Scribe and convergence toward parity as workflows matured. Semantic agreement remained high and stable (87.4-89.2%). Professional experience showed improvements in five of seven domains (g&#x2009;=&#x2009;0.25-0.41) with excellent reliability (&#x3b1;&#x2009;>&#x2009;0.93) and consistent gains across usage groups. Large-scale deployment of ambient AI documentation was feasible, showed high semantic agreement, was well-accepted and was associated with improvements in clinician experience. Maturation patterns suggest that efficiency gains may be reinvested into patient interaction and outcome-relevant care, consistent with a VBHC-aligned hypothesis.

Ambient artificial intelligence scribes (AI scribes) are emerging as tools to optimize clinical documentation, although the need persists to evaluate their comprehensive impact on quality of care beyond operational efficiency. The aim of this study is to explore the perception of medical professionals in a tertiary pediatric hospital regarding the expected impact of AI scribes on effectiveness, efficiency, patient safety, and patient experience, and to identify facilitators and barriers to their adoption. Cross-sectional observational study using an anonymous survey of 120 physicians in October 2025. Rogers' framework (technology adoption), CFIR (implementation), and WHO/AHRQ quality dimensions were applied. Descriptive analysis, comparison by specialty (Fisher's exact test), and multivariate analysis (PCA and K-Means) were performed. 82% had prior knowledge of this technology and 89% expressed willingness to use it. Perceived improvement was higher for efficiency (80.8%; 95%CI: 72.9%-86.9%) and effectiveness (73.3%; 95%CI: 64.8%-80.4%), while patient safety (35.0%; 95%CI: 27.1%-43.9%) and patient experience (43.3%; 95%CI: 34.8%-52.3%) generated greater uncertainty. Professionals were concentrated in intermediate adoption phases (decision: 32%; implementation: 30%). Multivariate analysis identified three profiles: pragmatic (35.8%), humanistic (43.3%), and resistant (20.8%). Resources considered essential were technical support (87%), training (86%), and time (74%). Despite high willingness to adopt AI scribes, perceived impact focuses on efficiency and effectiveness, with uncertainty regarding safety and patient experience. Implementation strategies should include specific training that highlights these connections and contextualized pilot evaluations.

Ambient AI scribe tools that capture clinician-patient conversations and generate draft notes are increasingly deployed to reduce documentation burden, but patient-facing acceptance may influence implementation success, especially in rural areas. To characterize rural respondents' attitudes toward AI scribes across (1) trust in documentation accuracy, (2) perceived impact on patient-provider interaction, and (3) preference for future use, and to examine how attitudes vary by demographic, socioeconomic, health, and digital-access characteristics. We conducted a cross-sectional analysis of 1,050 rural respondents in the 2024 Canadian Digital Health Survey. Each outcome was dichotomized. XGBoost classifiers were trained for each outcome using prespecified predictors (sex, age group, race/ethnicity, education, employment status, household income, chronic disease, self-reported health, and high-speed internet access). Models demonstrated strong overall performance on a held-out test set. Subgroup differences were summarized using marginally standardized predicted probabilities with bootstrap 95&#xa0;% confidence intervals. Predicted endorsement decreased across three attitude domains, from trust in documentation accuracy to interaction benefit and future-use preference. Predicted endorsement was higher among males than females across outcomes (e.g., future-use preference: 0.388 vs 0.313). Higher education and chronic disease were consistently associated with more favorable responses (e.g., future-use preference: graduate degree 0.466 vs less than high school 0.308; chronic disease 0.408 vs no condition 0.298). Compared with White respondents, visible minority, non-Indigenous respondents had lower future-use preference (0.293 vs 0.349), while Indigenous respondents showed higher predicted future-use preference (0.423 vs 0.349). Predicted probabilities were similar by internet access status across all three outcomes. Among rural respondents, trust in AI scribe accuracy does not fully translate into perceived interaction benefit or willingness to use AI scribes in future encounters, supporting rollout strategies that prioritize clear communication, privacy transparency, and meaningful choice.

Physician burnout, often driven by documentation burden in electronic health records, remains a major challenge in clinical care. Ambient artificial intelligence (AI) scribes generate draft notes from clinical encounters, offering a scalable approach to reduce administrative workload, yet population-level evaluations remain limited. The objective of this study is to assess changes over time in clinician perceptions of workload, well-being, and care delivery following implementation of ambient AI scribes. We conducted a prospective repeated cross-sectional survey study of physicians and advanced practice providers at four time points: baseline and approximately 1, 3, and 6 months' postimplementation. Outcomes included cognitive workload (NASA Task Load Index), professional fulfillment and burnout (Professional Fulfillment Index and Mini-Z), and perceptions of documentation burden, communication, and clinical capacity. Responses were grouped into clinically meaningful categories, and chi-square tests compared distributions across time. Secondary analyses compared baseline characteristics of respondents with linkable data across time points to the full cohort and evaluated subgroup differences for specific items. A total of 1,600 clinicians responded at baseline, with 172, 117, and 101 respondents at 1-, 3-, and 6-month follow-up, respectively. Over 95% of follow-up respondents reported generating &#x2265;5 ambient AI-supported notes, with use increasing significantly over time (p&#x2009;<&#x2009;0.05). Clinicians reported statistically significant reductions in mental demand, time pressure, and documentation effort at postimplementation intervals (p&#x2009;<&#x2009;0.05). Time spent on documentation outside clinic declined, with more clinicians reporting <2&#x2009;hours/day (p&#x2009;<&#x2009;0.05). Burnout increased across follow-up time points (p&#x2009;<&#x2009;0.05). Confidence in patient comprehension and willingness to increase patient volume improved. Subgroup analyses mirrored overall trends. Ambient AI scribes were associated with sustained reductions in cognitive workload and perceived documentation burden. Despite these improvements, burnout increased over time, likely reflecting multifactorial influences and limitations of unpaired survey data. These findings support continued implementation and further longitudinal evaluation of clinician well-being.

Ambient artificial intelligence (AI) scribe technology is entering clinical settings with the promise of reducing documentation burden. For health system leaders, these tools pose governance challenges rather than simple information technology upgrades. Early pilots show reductions of up to 30% in after-hours charting, but results vary with implementation quality. This article argues that Canada should classify AI scribes as Class II Software as a Medical Device and outlines governance questions for executives and boards. By treating ambient AI scribes as strategic leadership priorities - rather than technology deployments - healthcare leaders can balance efficiency with ethical stewardship and strengthen patient trust.