Resident physician perspectives on ambient AI scribing in academic family medicine

Resident physician perspectives on ambient AI scribing in academic family medicine | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Resident physician perspectives on ambient AI scribing in academic family medicine Himani Dhar , Angela Coderre-Ball , Akshay Rajaram doi: https://doi.org/10.1101/2025.05.29.25328559 Himani Dhar 1 Department of Family Medicine, Queen’s University , Kingston, Ontario, Canada MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Angela Coderre-Ball 1 Department of Family Medicine, Queen’s University , Kingston, Ontario, Canada MSc PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Akshay Rajaram 1 Department of Family Medicine, Queen’s University , Kingston, Ontario, Canada 2 Department of Emergency Medicine, Queen’s University , Kingston, Ontario, Canada MMI MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: akshay.rajaram{at}queensu.ca Abstract Full Text Info/History Metrics Data/Code Preview PDF 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. INTRODUCTION Up to 25% of a primary care physician’s workday is spent on documentation of clinical notes 1 , 2 and this increased administrative burden has been associated with decreased work satisfaction and increased burnout. 3 - 5 Increasingly, evidence suggests ambient AI scribes reduce time spent on documentation, 6 decrease workload and perceived burnout, 7 and improve patient-physician rapport. 8 , 9 While ambient AI scribes may be beneficial to attending physicians, less is known about their impact on resident physicians, who similarly spend significant time documenting in the EMR. 10 - 15 Our primary objectives were to assess resident physicians’ understanding of AI and ambient AI scribing in healthcare and explore their perceptions of the potential impacts of this technology. METHODS Our research adopts a qualitative grounded theory approach 16 , 17 and took place at the Department of Family Medicine at Queen’s University, an academic family medicine teaching site in Kingston, Ontario between August 30 and November 7, 2023. We invited first-year resident physicians (PGY-1) and second-year resident physicians (PGY-2) to participate in a 30-to 45-minute focus group conducted over video-conferencing. 18 One member of the authorship team (HD) facilitated all focus groups using a focus group developed by all authors (see Supplementary file 1). The facilitator was responsible for recording the discussion, asking questions, and clarifying participant questions related to the prompts. At the beginning of each focus group/interview a pre-recorded video demonstrating an ambient AI scribing program (see Supplementary file 2) was played for participants. Sessions were audio-recorded and transcribed verbatim by a hired transcriber. Following the approach to thematic analysis by Braun and Clarke, 19 two separate coders (HD, AR) independently performed manual line-by-line coding of the transcripts using NVIVO. 20 They compared and discussed discrepancies to enable the development of a common codebook. The research team (HD, AR, ACB) met to review codes and build themes. The project received research ethics board approval from the Queen’s University Health Sciences Research Ethics Board (FMED-6877-23). RESULTS Demographics Seven participants (three PGY-1, four PGY-2) participated in two focus groups (for PGY-1 and PGY-2 participants). Response Themes Theme 1: Understanding and exposure to AI and AI applications in healthcare There were differences in participants’ understanding of and exposure to AI in healthcare. Five of seven participants reported a basic understanding of AI and three of seven participants had a general understanding of the function of an ambient AI scribe ( Table 1 ). Most participants reported minimal exposure to AI in healthcare. Only one participant reported exposure to ambient AI scribing software. View this table: View inline View popup Table 1. Supporting quotations for themes 1 and 2 Theme 2: Perceived impacts on patient care Participants generally reported that looking at their screen to type disrupted the flow of the clinical encounter and felt this disruption negatively affected patient rapport, especially during conversations regarding mental health ( Table 1 ). Participants reported a perceived benefit of ambient AI scribe use would be improved focus on patients ( Table 1 ). However, some participants also described potentially detrimental effects, including speaking abnormal findings during physical examinations ( Table 1 ). Theme 3: Perceived impacts on the cognitive load of charting Participants highlighted the potential decreased time spent on documentation when using an ambient AI scribe ( Table 2 ) but emphasized the process of documenting as a valuable tool for learning and tracking clinically relevant details. Writing and reviewing notes was seen as a means for improving the structure of clinical encounters and served as a reflective process for learners as they considered conditions or ideas they may not have during the patient visit ( Table 2 ). View this table: View inline View popup Table 2. Supporting quotations for themes 3, 4 and 5 Multiple participants commented that the cognitive process of writing notes differed between reviewing and proof-reading. Actively documenting encounters was thought to provide benefits in learning, consolidation, and reflection, while reviewing notes required primarily reading and correcting notes ( Table 2 ). Participants also highlighted that ambient scribing could not include physicians’ thought processes. Two PGY-1 participants commented on the format and standardization of ambiently scribed notes potentially varying from their own personal note-taking style and interfering with an individual physician’s preferred format of documentation. Participants felt that the process of standardization in this manner could result in the overinclusion of detail ( Table 2 ). Theme 4: Performance and accuracy of ambient scribes Participants highlighted several concerns regarding the accuracy of ambient scribes in the context of speech differences across patients (accents, impairments or quieter voices), less structured encounters, and encounters with multiple people (e.g., healthcare workers, translators, and/or family members). Participants noted that in primary care, the discussion of multiple concerns in non-linear fashion may pose challenges for ambient AI scribes, and questioned how scribes would be able to assign the relevant details to each issue ( Table 2 ). Participants also raised concerns with the accuracy of transcriptions, including variations in terminology (e.g., “dizziness”), simple or colloquial terms, missed information or misinterpreted information, or simple terms used by paediatric patients. ( Table 2 ). Additionally, one PGY-2 highlighted that a given ambient scribe’s accuracy would depend on the set of training data used and that these data could be biased ( Table 2 ). Multiple participants reflected on the medicolegal implications, including concerns around having incomplete or inaccurate documentation due to scribe use as well as the implications of making clinical decisions based on incomplete or inaccurate information. They also wondered about whether it would be legally defensible to remove information from the scribed documentation where unnecessary or irrelevant details were included. Theme 5: Adoption of ambient scribes in family practice Participants highlighted several barriers to the adoption of ambient AI scribing in family medicine, including policies and procedures around patient consent, training, and compatibility with different EMRs ( Table 2 ). Participants had mixed feelings regarding adoption of ambient AI scribes. They highlighted their experience adapting to multiple EMRs during residency and that having to learn an additional piece of software could be overwhelming. They felt adoption would take time and repeated use would be needed to gain comfort. DISCUSSION This study explores the perspectives of resident physicians on the use of ambient AI scribes in an academic family medicine setting. Whereas residents acknowledged perceived benefits to ambient AI scribe use in terms of improved patient rapport, their limited exposure to this technology coupled with their concerns regarding its impact on documentation suggest the need for targeted education additional testing prior to its introduction in academic settings. The role of documentation in resident learning Given significant charting burden, 10 - 15 we were surprised to hear resident participants highlight the educational value of note writing and their preference for writing over editing in the context of ambient scribe deployment. Bowker et al. found that trainees used documentation as ways to support their clinical judgement, focus on patient issues, and identify gaps in knowledge and data collection. 21 In this way, writing encounter notes is important to developing mental models about patients and ambient AI scribe use prior to the consolidation of these skills could be detrimental overall to resident learning. Documentation quality Residents’ concerns regarding note quality resonate with a recent qualitative study that revealed attending physicians’ concerns with the accuracy, completeness and the formatting of notes following use of an ambient AI scribe. 7 Given that residents receive limited formal learning opportunities around charting, 22 developing a strong foundation through repeated practice of authoring notes is critical to identify potential errors or hallucinations in transcriptions. 23 Implications for adoption These findings suggest that use of ambient AI scribing by residents in academic family medicine practices may not yet be ready for widespread deployment. To ensure residents continue to develop strong clinical problem-solving skills, including the management and synthesis of information about patients, residency programs could consider continuing resident notetaking alongside ambient AI scribing tools or limiting their use to senior years (e.g., transition to practice) with dedicated instruction beforehand around the medicolegal considerations. Limitations Our sample size was small with participants reporting minimal exposure to AI, particularly in a practice setting. This observation is also likely due to the timing of this study, before widespread adoption of AI scribes in primary care in Canada. Furthermore, all participants were from one of four sites of an academic family medicine residency program. Accordingly, our findings may not be generalizable to other practice settings, including community and rural sites, longitudinal family medicine programs, or other specialties. CONCLUSIONS AND FUTURE WORK We conducted a qualitative study to gather the perspectives of family medicine resident physicians on ambient AI scribing. While participants perceived benefits of ambient scribing in reducing documentation burden and improving physician-patient rapport, they raised concerns related to its educational impact and quality of documentation. In addition to addressing methodologic limitations, future work on ambient AI scribing should explore testing with family medicine residents in diverse settings to inform adoption. Data Availability All data produced in the present study are not available. AUTHOR CONTRIBUTION STATEMENT AR and ACB conceived the study. HD facilitated the focus groups with AR as an observer. Both coded the transcripts. ACB supervised and reviewed the codebook prior to analysis. All authors contributed equally to the writing and review of the manuscript. All writing was completed without the assistance of any AI or automated writing technologies. All authors agree with the content of the article and to its submission for publication. SUPPLEMENTARY MATERIAL S1. Focus Group Guide View this table: View inline View popup Download powerpoint A.I. Definition and Summary Artificial intelligence (or A.I. for short) is a technology that allows machines to perform tasks and make decisions similar to how humans do. AI uses algorithms, which are like step-by-step instructions, to process huge amounts of data and find patterns or solve problems. It can recognize images and understand speech, which can allow it to help with a variety of complex tasks. AI can also adapt and improve over time by learning from its experiences. An example of an A.I. software you may have heard of is Chat-GPT. Ambient scribe Definition and Summary An ambient scribe is an A.I. technology that helps people capture and record information in a digital format. They can take notes and transcribe spoken words into written text. They can be helpful in summarizing conversations into a standardized format. This technology is especially helpful for individuals who need to keep accurate records of meetings, lectures, or interviews. Ambient scribes can be used on computers, smartphones, or other devices and save time and effort by eliminating the need to manually write everything down. S2. Video demonstrating an ambient scribe https://bit.ly/perspectives_supplementary2 ACKNOWLEDGEMENTS The authors wish to thank Dr. Noah Crampton of MutuoHealth for providing the ambient scribe demonstration used in this study. Footnotes Dr. Himani Dhar, MD CCFP is a practicing community family physician and recent graduate of the Queen’s University family medicine residency program. Dr. Angela Coderre-Ball, MSc PhD is an Assistant Professor (adjunct) in the Department of Family Medicine at Queen’s University. Dr. Akshay Rajaram is a practicing community emergency physician and Assistant Professor (Adjunct) in the Departments of Family and Emergency Medicine at Queen’s University. Competing Interests Dr. Rajaram co-founded and serves as the Chief Product Officer of 12676362 Canada Inc (doing business as Caddie Health), a start-up developing medical billing software for physicians. He owns equity in the company. The company is no longer active commercially and their product was not related to ambient scribing. Dr. Coderre-Ball is an employee of the Centre for Effective Practice, a not-for-profit organization dedicated to developing trusted, evidence-based tools, resources and programs for healthcare providers. She participated in this project in her capacity as adjunct assistant professor at the Department of Family Medicine. Funding, Disclosures, and Support This research received no funding. REFERENCES 1. ↵ Arndt BG , Beasley JW , Watkinson MD , Temte JL , Tuan WJ , Sinsky CA , et al. 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