Interruptions have always been intrinsic to medicine, yet their true burden in outpatient settings remains surprisingly poorly measured. An observational study of outpatient internal medicine physicians in China documented an interruption rate of 6.46 per hour, a figure that captures only the disruptions researchers could directly observe. Comparable studies in United States outpatient practices have not been conducted in the past decade, a gap the Colorado authors attribute to the diffuse nature of ambulatory workflows. Unlike the emergency department or the operating room, where interruptions are discrete and countable events, primary care now runs on asynchronous communication and overlapping responsibilities that make disruptions difficult to quantify and, more insidiously, easy to normalize. Each individual ping seems trivial; collectively, they fragment the encounter into slivers of attention.

The technological transformation of the past two decades explains much of this fragmentation. Before widespread electronic health record adoption, clinicians were hardly free from distraction. Telephone calls, pagers, overhead pages, and in-person questions all demanded immediate attention. But communication flowed through fewer channels with more clearly defined workflows, making interruptions easier to integrate into the rhythm of the day. Today, clinicians navigate a proliferating array of platforms: patient portals, secure staff messaging systems, fax, mail, and the electronic in-basket, each with its own interface, its own monitoring burden, and its own implicit expectation of rapid response. The tools were intended to improve access and efficiency. Instead, their proliferation has paradoxically increased redundancy and disruption, forcing constant channel switching that alters attention, workflow, and the fundamental experience of clinical care.

The consequences are not merely cosmetic. A substantial body of research demonstrates that interruptions impair task performance, and studies of physicians specifically show that interruptions increase cognitive load, reduce efficiency, and contribute to clinical errors. Research on emergency physicians has linked interruptions and multitasking to task errors, a finding with obvious implications for any setting where diagnostic reasoning unfolds under divided attention. But the damage extends beyond the clinician’s desk. Patients experience fragmented attention directly, and their perception of care is shaped by the quality of the interaction itself. Studies of oncologists have shown that greater physician eye contact is associated with higher patient perceptions of trust, underscoring that sustained attention during the clinical encounter is not a soft nicety but a measurable component of the therapeutic relationship.

When attention is fragmented, the implicit message to the patient is that the encounter itself is negotiable. A patient who has invested significant effort to be seen, only to watch her physician’s eyes dart to a screen, may reasonably conclude that asynchronous channels, portal messages and phone calls, are a more reliable means of accessing medical attention. This creates a self-reinforcing loop: fragmented in-person care drives patients toward the very asynchronous messaging that further fragments clinicians’ attention, increasing the volume of in-basket work that then intrudes on the next visit. The viewpoint authors describe this as a shared state of dissatisfaction, in which clinicians feel distracted, staff feel unsupported, and patients feel undervalued during the most critical moments of care.

Frontline clinical staff experience a parallel and often overlooked pressure. The medical assistant who recognizes that a delayed vaccine order is escalating tension in a crowded waiting room must decide, on the spot, whether to interrupt a clinician already managing competing demands. In this role, staff function as communication gatekeepers within a complex system, exercising constant judgment often without standardized expectations. They carry responsibility for patient flow and communication decisions while receiving limited guidance on how to use the available modalities. The authors argue this mismatch increases stress, heightens error risk, and contributes to job dissatisfaction and burnout among frontline staff. These challenges have intensified since the COVID-19 pandemic, as remote work has reduced face-to-face access to team members, despite evidence that in-person communication improves job satisfaction among clinic staff.

Crucially, the authors frame the problem as a system-level design failure rather than a deficit of individual effort, and their proposed solutions are correspondingly structural. The most technologically prominent among them is ambient artificial intelligence: tools that generate clinical notes in real time by listening to the encounter, offloading documentation from the clinician. Recent research published in JAMA Network Open found that ambient AI scribes can reduce administrative burden and professional burnout. By untethering clinicians from the keyboard, such tools allow them to disengage from the screen and reorient attention toward the patient, improving perceived quality of care while enhancing presence and cognitive focus. Freed from documentation duties, the computer can even be repositioned as a shared educational resource, used to review imaging, laboratory data, or treatment plans together with the patient. When thoughtfully implemented, the authors suggest, ambient AI may help restore face-to-face care as the central act of medicine while reducing the burnout driven by documentation overload and relentless task switching.

Technology alone, however, is insufficient, and the authors are explicit on this point. Health systems must also design workflows that physically and temporally protect in-person visits. One promising strategy is the creation of dedicated, protected time during the clinic day for asynchronous work, allowing clinicians to address messages and administrative tasks in defined blocks rather than squeezing them into patient encounters. Research in ambulatory general internal medicine practices has found that this approach, while requiring a small decrease in bookable time for synchronous care, maintains quality measures and patient satisfaction while reducing clinician-reported intentions to cut back clinical time, an outcome the authors emphasize is especially important given the current shortage in the primary care workforce. In other words, protecting attention may be one of the most effective retention tools available to a strained system.

Clear system-wide communication guidelines are equally essential. Standardizing expectations around urgency, response times, and appropriate channels improves efficiency and gives clinicians more time to focus on synchronous patient care. Practical strategies include directing new symptoms to nurse triage by phone, discouraging nonessential portal messages, and avoiding communication sent simultaneously to multiple recipients, a practice that multiplies in-basket volume without adding information. Studies of academic internal medicine clinics have shown that simple tools can meaningfully reduce portal message volume, taming the in-basket that has become a leading driver of after-hours work. Health systems, the authors argue, should also audit whether all existing communication pathways are necessary at all; eliminating redundant channels may simplify navigation, reduce cognitive load, and restore focus to the therapeutic encounter.

The deeper message of the viewpoint is that face-to-face care remains the foundation of medicine, and that preserving its integrity requires deliberate efforts to rebalance communication systems, not to reduce access, but to ensure that when patients and clinicians are finally in the same room, that moment is protected. The question “Am I keeping you from something important?” is uncomfortable precisely because the honest answer is so often yes: the prescription correction, the falling patient, the vaccine order all matter. The goal of a well-designed system is to make sure those competing demands are handled in their proper place, so that the person in front of the clinician is never made to feel like the interruption.

Subject of Research: Workflow interruptions and communication burden in primary care and their impact on clinicians, staff, and patients

Article Title: Am I Keeping You from Something Important?

Article References: Arenz, D., Stenehjem, K., & Fuest, S. (2026). Am I Keeping You from Something Important?. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10865-x

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10865-x

Keywords: primary care, physician interruptions, electronic health records, ambient AI scribes, in-basket burden, patient portal, clinician burnout, patient trust, eye contact, medical assistants, health communication, workflow design

That moment of misdirection, described by Dr. Danilo Rojas-Velasquez of Harvard Medical School and Beth Israel Deaconess Medical Center in a new viewpoint article published in Academic Psychiatry, sits at the heart of an argument that is gaining urgency across academic medicine: the language clinicians write into charts is not a neutral record but a durable force that shapes how every subsequent clinician perceives a patient. Psychiatry, a chart-heavy specialty in which practitioners learn to scan prior notes for buzzwords that key early hypotheses, is particularly exposed. Words travel with patients across years of records, and the language chosen by one team determines how the next team imagines the person in the bed.

Clinical medicine has already made a deliberate effort to soften the edges of its vocabulary. Undomiciled has largely replaced homeless, non-adherent has supplanted non-compliant, and declined is preferred to refused. These substitutions reflect a genuine ethical awareness that stigmatizing descriptors can follow patients for decades. But Rojas-Velasquez argues that such lexical swaps can function as a kind of hygiene that leaves the underlying clinical gaze unchanged. A patient called non-adherent by a frustrated team is not necessarily better understood than one called non-compliant; the person is simply described in the currently sanctioned vocabulary. The deeper issue is not which words are approved but what those words do once they are placed in a chart and begin to circulate.

The empirical evidence behind this concern is substantial. A growing body of research documents that stigmatizing language in the medical record measurably alters downstream clinical decisions, including pain management and the intensity of diagnostic workups. Studies have found that descriptors such as pleasant and cooperative are applied unevenly across patient race, meaning that the chart itself can become a vector for the transmission of bias. Physician use of stigmatizing language in patient records has been documented systematically in large-scale analyses, and researchers have shown that negative framing in one note propagates into the impressions formed by later readers. Yet documentation is still often treated as a matter of mechanics: billing compliance, problem-list hygiene, and the architecture of the assessment and plan. The ethics of clinical language, what a clinician is actually doing to a patient when writing about them, has remained a hidden curriculum, absorbed by osmosis from whichever notes a trainee happens to read.

Into this landscape, ambient scribes and large language model-assisted documentation are arriving faster than the educational infrastructure can adapt. Randomized trials of ambient AI scribes in clinical practice are already underway, and health systems are deploying the tools at scale. The technical mechanism of concern is subtle but consequential: AI tools inherit and polish the linguistic conventions of their training data, which means that stigmatizing patterns already present in charts are being laundered into outputs that feel more authoritative precisely because they are fluent. A trainee who might once have written that a patient seems frustrated may now sign a note describing him as hostile and uncooperative simply because that is what the model produced. Fluency, in this context, is not accuracy; it is a cosmetic upgrade applied to inherited bias.

There is also an educational cost. Historically, the note has been the place where clinical formulation happens. Writing was thinking, and the attending’s question of why a trainee described a patient in a particular way was a teaching moment built directly into the workflow. When the note arrives pre-formed by an algorithm, that moment is structurally removed unless educators deliberately rebuild it. The formulation step, in which a clinician weighs observations, considers alternative explanations, and commits to a reasoned characterization of the patient, is being quietly absorbed by the model. What remains for the trainee is verification, which is a fundamentally different and far less formative cognitive task.

In response, the article proposes three practical steps for teaching note writing in the age of AI. The first is to teach note-reading before note-writing. On a rotation, a resident might review prior documentation, name the impression those notes created, and then compare that impression with what actually emerges at the bedside. The point is not to grade the prior note or its author but to make the chart’s authorial power visible before the resident adds to it. This exercise converts an invisible influence into an object of explicit study, allowing trainees to feel firsthand how a one-line summary can pre-load an entire clinical encounter with assumptions.

The second step is to build a next-reader pause into supervision. Before signing a note, the trainee considers how it would read to a covering clinician at three in the morning, to the patient with portal access, and to a family member reviewing the chart. Open Notes initiatives and patient-facing documentation have already shifted the audience for clinical writing; patients can now read the words written about them, and imagining these readers helps residents notice attributions and tones of voice that would otherwise pass unexamined. Qualitative work on patient-centered documentation in the era of open notes suggests that this kind of deliberate audience-awareness changes how clinicians draft, encouraging precision and fairness over shorthand judgment.

The third step treats the editing of AI-drafted notes as genuine clinical work rather than clerical review. If formulation is being absorbed by the model, the trainee’s task is not merely to check grammar or fill gaps but to interrogate what the draft has already decided: where observations have hardened into character judgments, where hedge words have drifted toward unwarranted certainty, and where descriptors would not survive the next-reader test. The article suggests a weekly note rounds, in which a team reviews and revises an anonymized AI-drafted note together, turning a hidden judgment back into a shared teaching moment. In this arrangement, the AI draft becomes a specimen to be dissected rather than a product to be signed, restoring the pedagogical function that automation threatened to erase.

The chart remains one of the few places in medicine where a patient is constructed in their absence, by people with power over them, in language they have historically been unable to see. Open Notes is changing the last part of that sentence, and ambient AI is changing the second, but the construction itself endures, and arguably matters more now than it did when notes lived in locked basements. Consultation-liaison psychiatrists, who live in other clinicians’ notes before they ever meet their patients, are unusually positioned to notice this dynamic. They watch language do its quiet work, and they write notes that will in turn shape how the next team thinks. Whether the current generation of trainees inherits a thoughtful relationship to that responsibility, or simply a more polished vocabulary applied at greater speed, will depend in large part on how medical educators decide to teach it. The stakes are not stylistic. They concern who patients become on paper before anyone meets them in person, and whether the accelerating machinery of AI-assisted documentation amplifies clinical understanding or merely automates its failures.

Subject of Research: Teaching ethical clinical documentation and note-writing skills to psychiatry trainees in the era of AI-assisted medical records

Article Title: Teaching Note Writing in the Age of AI: Three Practical Steps

Article References: Rojas-Velasquez, D. (2026). Teaching Note Writing in the Age of AI: Three Practical Steps. Academic Psychiatry. https://doi.org/10.1007/s40596-026-02434-5

Image Credits: AI Generated

DOI: 10.1007/s40596-026-02434-5

Keywords: clinical documentation, artificial intelligence, psychiatry education, stigmatizing language, medical records, ambient AI scribes, large language models, Open Notes, clinical formulation, medical bias, consultation-liaison psychiatry, graduate medical education