OBJECTIVE: The use of ambient AI documentation tools is rapidly growing in US hospitals and clinics. Such tools generate the first draft of clinical notes from scribed patient-provider conversations, which clinicians can then review and edit before signing into electronic health records (EHR). Understanding how and why clinicians make modifications to AI-generated drafts is critical to improving AI design and clinical efficiency, yet it has been under-studied. This study aims to address this gap. MATERIALS AND METHODS: We conducted semistructured interviews with 30 clinicians from the University of California, Irvine Health who used a commercial ambient AI tool in routine outpatient care. We invited them to describe how and why they edited AI drafts based on both their personal experience and review of some real-world examples identified from our previous studies. RESULTS: Modifications to AI drafts were primarily made to improve clinical accuracy and specialty-specific precision, reduce medico-legal and liability risk, and meet billing, coding, and documentation standards. Such editing was necessary due to reasons such as transcription errors, speaker attribution mistakes, overconfident statements without evidence, missing key clinical details, and AI's lack of information about the patient context. CONCLUSION AND DISCUSSION: Improving ambient AI documentation will require coordinated effort from vendors, institutions, and clinicians. Key targets include core model reliability (eg, transcription accuracy), specialty- and encounter-level customization, clinician-level personalization, more effective EHR integration, and institutional support (eg, training, governance, and standardized review guidance), complemented by clinicians' adaptive communication strategies that strengthen human-AI collaboration.

OBJECTIVE: Ambient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process. MATERIALS AND METHODS: Eight coders analyzed clinical documentation generated by ambient AI from 200 clinical encounters. We developed an inductive coding framework with 11 codes across 3 categories: clinical content, terminology, and language style. Interrater reliability was assessed using Cohen's kappa. We then applied thematic analysis to synthesize patterns across the coded edits. RESULTS: The most frequently edited content pertained to clinical facts including orders (eg, procedures, lab tests) (40.0%), symptoms (30.3%), medication prescriptions (27.3%), and diagnosis descriptions (25.9%). In comparison, edits related to terminology use (11.6%) and language style (7.2%) were less frequent. The results of our thematic analysis show that most edits can be categorized into one of the following 5 types: to revise factual discrepancies, to add medical specialty-specific details, to express diagnostic certainties, to convert patient expressions into objective assessments recorded in medical terms, and to reorganize or condense content. CONCLUSION AND DISCUSSION: Clinicians routinely revise ambient AI drafts to modify factual details and clinical specificity. Future work on AI development and clinical implementation should emphasize specialty customization and support personalized documentation practices, alongside clinician education that promotes robust and consistent review routines to ensure documentation quality.

BACKGROUND AND SIGNIFICANCE: Ambient listening tools powered by generative artificial intelligence (GenAI) offer real-time, scribe-like support that reduce documentation burden and may help alleviate burnout. This study assesses physician-perceived benefits and challenges of ambient AI implementation through surveys and evaluates its effectiveness in clinical workflows using automatically recorded electronic health record (EHR) time-efficiency metrics. METHOD AND MATERIALS: A quality improvement pilot has been underway at UCI Health since December 2023. Epic EHR Signal metrics were analyzed to assess changes in note length, documentation time, and same-day encounter closure rates. Matched pre- and post-implementation surveys evaluated physician-perceived changes in documentation burden, clinical efficiency, and care quality. We also examined open-ended survey responses using thematic analysis to supplement quantitative findings. RESULTS: Analysis on EHR usage data from 167 physicians showed significant reductions in note-writing time, despite an increase in note length. Survey responses (n = 65) also indicated statistically significant improvements across multiple domains. Physicians reported reduced cognitive demand (P = .031) and documentation effort (P = .014), alongside perceptions of enhanced clinical efficiency, patient-centered care, and EHR system usability. Thematic analysis confirmed these quantitative findings and identified opportunities for improvement, including specialty-specific customization and expanded AI functionality. DISCUSSION: Ambient AI tools demonstrated improved documentation efficiency, perceived care quality, and reduced cognitive workload. These benefits suggest potential to alleviate key burdens in clinical documentation. CONCLUSION: Future development should prioritize customization for specialty-specific and individual physician needs, ensure the reliability and accuracy of AI-generated content, and integrate ethical and legal considerations to facilitate safe and scalable implementation in patient-centered care contexts.

OBJECTIVE: We sought to ascertain perceived factors affecting women's career development efforts in the American Medical Informatics Association (AMIA) and to provide recommendations for improvements. MATERIALS AND METHODS: Data were collected using a 27-item survey administered via the AMIA newsletter and other social channels. Survey questions comprised 3 demographics, 15 Likert-scale, and 9 open-ended items. Likert-scale responses were summarized across respondent ages, career stages, and career domains, and open-ended responses were thematically analyzed. RESULTS: We received survey responses from 109 AMIA women members. Our findings demonstrate that AMIA had made strides in promoting career development, and the most effective AMIA efforts included social events (83%), panel discussions (80%), and scientific sessions (79%). However, despite these efforts, women members perceived that gender-specific challenges persisted within AMIA, and recognized the need for increased networking opportunities (96%), raising awareness of gender-specific challenges (95%), and encouraging gender proportional representation in leadership (92%). DISCUSSION: International and national biomedical informatics professional communities have put forth efforts to address gender-specific issues in career development. Yet, our study identified that some of these, including the deep-rooted gender power hierarchy and bias, are still perceived as profound in AMIA. CONCLUSION: Even though existing career development efforts for women are highly effective, important perceived gender-specific career development issues require further attention and investigation to improve existing AMIA activities.