Resident physician perspectives on ambient AI scribing in academic family medicine
Dhar, H.; Coderre-Ball, A.; Rajaram, A.
Show abstract
While ambient artificial intelligence (AI) scribes have been received positively by primary care physicians, the perceptions of resident physicians are not yet unclear. We conducted a qualitative study involving focus groups with first and second-year family resident physicians from a single urban academic family health team to gauge their understanding of ambient AI scribing and their perceptions of its potential impact on patient care. Seven resident physicians participated in two focus groups. Sessions were audio recorded and transcribed verbatim, then analyzed inductively to identify themes. We categorized the findings into five themes: 1) understanding of and exposure to AI, 2) perceived impact of ambient AI scribing on the practice of family medicine, 3) perceived impact on the cognitive load of charting, 4) performance and accuracy of ambient AI scribes, and 5) implications for adoption. Residents in this study reported minimal exposure to AI and concerns regarding the impacts of ambient AI scribing on the documentation process and quality of notes. Future research should explore the potential effects of ambient scribes on resident documentation prior to testing in practice.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Exploring Patient and Staff Experiences of Video Consultations During COVID-19 in an English Outpatient Care Setting: Secondary Data Analysis of Routinely Collected Feedback Data 96%
- Improving emergency department patient-doctor conversation through an artificial intelligence symptom taking tool: an action-oriented design pilot study 94%
- Design and Formative Evaluation of a Voice-based Virtual Coach for Problem-Solving Treatment 93%
Similar papers in this journal
- Improving capacity for advanced training in obstetric surgery: Evaluation of a blended learning approach 93%
- Large language models for generating medical examinations: systematic review 92%
- Effect of introducing interprofessional education concepts on students of various healthcare disciplines in the United Arab Emirates 91%
Similar papers in this journal
- Evaluating user experience with immersive technology in simulation-based education: a modified Delphi study with qualitative analysis 94%
- Nurses’ experience of using video consultation in a digital care setting and its impact on their workflow and communication 94%
- Identifying clinical skill gaps of healthcare workers using a digital clinical decision support algorithm during outpatient pediatric consultations in primary health centers in Rwanda 93%
Similar papers in this journal
- Benefits and Challenges of Using Virtual Primary Care During the COVID-19 Pandemic: From Key Lessons to a Framework for Implementation 94%
- Theory of radiologist interaction with instant messaging decision support tools: a sequential-explanatory study 94%
- Developing contents for a digital drug adherence tool with reminder cues and personalized feedback: a formative mixed-methods study among children and adolescents living with HIV in Tanzania 92%
Similar papers in this journal
- Physician experiences of electronic health records interoperability and its practical impact on care delivery in the English NHS: A cross-sectional survey study 94%
- What is the suitability of clinical vignettes in benchmarking the performance of online symptom checkers? An audit study 93%
- Understanding good communication in ambulance pre-alerts to Emergency Department. Findings from a qualitative study of UK emergency services 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.