A useful draft still needs an accountable author
Consider a clinician who finishes a visit with a polished note already waiting in the electronic record. The patient received more eye contact, the clinician typed less, and the note appears complete. Yet the assessment attributes a family member’s symptom to the patient, converts a possibility into a diagnosis, or omits a medication change. This illustrative situation explains why organizations should evaluate the complete documentation process, including the work required to recognize and correct a plausible error.
Ambient scribes typically capture a clinical conversation and use automated processing to produce documentation. Their authorized role, available context, integration, and outputs differ. A health system should describe the specific product and workflow it intends to deploy before promising a benefit. Drafting a note, creating a patient summary, suggesting codes, and initiating an order are different activities with different consequences. Approval for one should not silently authorize another.
A randomized trial involving 238 physicians across 14 specialties compared two ambient products with usual practice. One product produced a statistically significant reduction in time in notes relative to control; the other did not. Use also varied across encounters, and participants reported inaccuracies. These findings support a product-specific and workflow-specific assessment, rather than treating ambient documentation as a uniform intervention. They do not establish a current ranking of vendors or prove safety across all clinical settings. [1]
The executive decision is therefore conditional: whether a defined configuration improves the work of a defined clinical group while maintaining acceptable documentation quality and patient experience. A license purchase cannot answer that question. A governed evaluation can produce evidence that makes the decision more defensible.
Review approach and interpretation
This narrative review uses 25 peer-reviewed sources addressing ambient documentation, clinician workload, note quality, usability, patient perspectives, and implementation. Primary bibliographic records and available full texts were examined through September 8, 2026, with recent research prioritized and earlier studies retained when they addressed a distinct question. Some assessments were limited to indexed primary abstracts; detailed implementation claims were restricted accordingly. The review is a structured executive synthesis, not a systematic review or a pooled estimate of effectiveness.
Randomized comparisons provide stronger evidence about the evaluated interventions than uncontrolled before-and-after reports. Observational studies help characterize adoption at scale but remain vulnerable to selection, concurrent workflow changes, and incomplete adjustment. Simulation can reveal errors under controlled conditions without reproducing the complexity of routine care. Interviews and surveys explain experiences and implementation concerns; they do not independently establish clinical safety or causal effects on burnout.
Interpretation also depends on what investigators measure. Time inside a note editor differs from total electronic-record time. Perceived cognitive burden differs from a validated burnout measure. A completed draft differs from a signed record, and a preferred note can still contain an important inaccuracy. The practical framework below is an inference from this varied evidence, intended for local evaluation rather than adoption as a validated intervention.
Define the workload benefit precisely
A quality-improvement evaluation involving physicians at an academic health system associated ambient documentation with reduced electronic-record time and lower perceived cognitive demand, while note length increased. The observational design cannot isolate the tool from selection and implementation effects. Its relevance is that workload and output characteristics can change together; more documentation is not automatically better documentation. [2]
A retrospective health-system analysis of 1,547 active users used an interrupted time-series approach. Ambient use was associated with modest changes in documentation measures, but the study did not find an association with appointments per day. Voluntary adoption and the absence of random assignment limit causal conclusions. An organization should not convert a reduction in one time measure into a guaranteed increase in clinical capacity. [3]
A small time-motion evaluation in Singapore found less documentation time and greater eye contact, without a significant change in total consultation cycle time. This separates a potentially valuable improvement in how work feels and how attention is allocated from a claim that visits become shorter. Patient interaction may improve even when the schedule remains unchanged. [4]
Choose the intended benefit before deployment. A clinic might prioritize reducing unfinished documentation, protecting time after a shift, reducing the mental effort of reconstructing a conversation, or allowing more attention during the encounter. Each requires a different measurement plan. Combining them into one favorable score can conceal that the intervention helped one outcome while leaving another unchanged.
Measure the work that moves elsewhere
A pilot in Hawaii found variable effects across clinicians and use patterns. Higher use was associated with some stronger time benefits, while not every patient-experience or burnout outcome improved significantly. Such findings may reflect both exposure and selection: clinicians who find a tool useful may continue using it more often. High-use and low-use groups are not automatically comparable. [5]
An emergency-department evaluation associated ambient use with less on-shift documentation time but slightly more after-shift documentation time per encounter. Use was discretionary, so the comparison cannot fully remove differences in clinicians or cases. The operational lesson is to measure when work occurs, as well as how much is recorded. A projected saving across a hypothetical shift should not be reported as an observed reduction in shift length. [6]
Map the work before and after implementation. Include patient explanation, activation, troubleshooting, reading the draft, checking facts, correcting omissions, handling failed recordings, reconciling imported content, and responding to later questions. Some activities may become easier while others become more demanding. Ask whether the change transfers work to nursing staff, trainees, medical assistants, information technology teams, or patients reviewing their records.
Use a stable baseline and a comparison group when feasible. Record clinical session length, case mix, clinician experience, use frequency, and major concurrent changes. Report both the population offered the tool and the population analyzed. If only persistent users remain in the final assessment, make that limitation visible. A favorable result among completers does not describe everyone who started.
Treat editing as clinical work
An analysis of clinician edits to ambient-generated notes found changes involving clinical facts, symptoms, medications, diagnoses, orders, and presentation. Editing patterns help identify where review effort is concentrated. However, an edit is not necessarily evidence of an error: clinicians may add information unavailable in the conversation, adjust structure, or express a preference. Conversely, a note with few edits is not necessarily accurate. [7]
Interviews with clinicians identified concerns involving omitted context, speaker attribution, factual inaccuracies, and overly confident wording. These findings explain why a fluent draft can demand careful attention. The organization should train reviewers to compare the note with their clinical understanding, rather than merely proofreading grammar or approving a familiar template. [8]
Define the signing clinician’s responsibilities clearly. The clinician must have a practical opportunity to verify the record and correct it through the established workflow. Verification should focus on consequential content such as the assessment, medication decisions, follow-up instructions, patient preferences, and the identity of speakers. The tool should not make unsigned machine output appear to be an approved clinical judgment.
Review effort belongs in the business case. If a clinician saves drafting time but spends that time searching for omissions, the gross saving overstates the benefit. Equally, verification may be clinically worthwhile even when it limits the apparent efficiency gain. The goal is a usable, accurate record produced with manageable work, not the smallest number of human edits.
APPLIED CASE · FICTIONAL WORKFLOW
A fluent note. The wrong person.
A family member mentions their own dizziness during a visit. The patient says they have not experienced that symptom. The ambient draft nevertheless records that the patient reports dizziness. The signing clinician identifies the attribution error, corrects the note using verified encounter context, and completes the rest of the clinical review before signing.
This original teaching scenario applies the editing and note-quality discussion above and below. It is not a reported patient event, a vendor test, or an estimate of how frequently an error occurs. Studies of clinician edits and their rationale support attention to clinical meaning and review effort; the example and response sequence are proposed applications of that evidence. [7,8]
Watch for the decision: fixing one sentence does not verify the whole note. Unresolved concerns remain unsigned while the clinician follows the approved support process. A corrected error also needs a route back to the clinical safety owner so the organization can investigate, change the workflow when needed, and retest.
Executive application: evaluate draft defects and residual errors in signed notes separately. Include multi-speaker encounters, total review effort, and patient choice. An isolated correction demonstrates a control in this scenario; it does not establish that a deployment is safe. [7–9]
Read the application-video transcript
A fluent note. The wrong person. Would your review catch it?
This fictional example applies the Ambient AI Scribes article to a documentation workflow.
Before capture, explain the approved process and offer the established alternative.
In this case, the patient agrees and the clinician activates the scribe.
During the conversation, a family member mentions their own dizziness.
The patient says that they have not experienced that symptom.
The generated draft states: the patient reports dizziness.
It sounds plausible, but it assigns the symptom to the wrong person.
Before signing, the clinician checks the draft against the encounter and clarifies anything uncertain.
This review identifies the speaker attribution error.
The clinician removes the unsupported patient symptom and documents only the relevant, verified context.
Correcting one sentence does not verify the rest of the note.
Review the assessment, medication decisions, follow-up, and other consequential content before signing.
An unresolved concern stays unsigned and moves through the approved support process.
After the complete review, the clinician signs the corrected note.
The correction is reported through the approved internal safety workflow, with the minimum necessary information.
The clinical safety owner investigates how the error arose and whether the workflow needs to change.
Include multi-speaker encounters in evaluation. Check drafts and signed notes separately.
Executives review documentation quality, total workload, and patient trust as separate outcomes.
This example demonstrates a control. It does not establish an error rate or prove the product safe.
Read Ambient AI Scribes at The Healthcare Executive.
Use the article to plan verification, evaluate a bounded pilot, and make an accountable deployment decision.
Establish a note-quality evaluation that can detect harm
In a prospective pilot, physicians reviewed a subset of ambient-generated notes for accidental inclusions, omissions, hallucinations, and bias. A small proportion of evaluated notes contained errors judged capable of serious harm if uncorrected. The sample was selected from a larger set of notes, so the reported rate should not be treated as the product’s universal error rate. The study nevertheless shows why local safety review is necessary. [9]
Create an evaluation sample that includes ordinary encounters and situations likely to challenge the system. Consider complex medication discussions, changing plans, multiple speakers, uncertain diagnoses, interpreters, interruptions, and sensitive conversations. These are proposed testing priorities, not a claim that every system fails in each situation. Include cases from the specialties and populations actually expected to use the tool.
Assess both the draft and the final signed record when the evaluation design permits. The draft reveals what the system produces; the signed record reveals what remains after the human process. Distinguish missing information, incorrect information, unsupported additions, wrong-person attribution, and failures of structure that could obscure an important instruction. Document severity, detectability, correction effort, and the denominator used to calculate each rate.
Use reviewers who understand the clinical context, and resolve material disagreements through a defined process. A single overall quality score can complement, but should not replace, examination of consequential defects. Record uncertainty when the available audio or reference record cannot establish what happened. Do not force an ambiguous case into an artificial correct-or-incorrect category simply to complete a dashboard.
Make patient trust a separate outcome
A prospective survey of more than 2,000 patients who had experienced ambient documentation found that most respondents regarded it as helpful and preferred future use. That is useful acceptability evidence for the surveyed setting. It does not establish that all patients welcome recording or that those who did not respond shared the same views. [10]
A preimplementation survey at another academic center found a mixture of favorable, neutral, and unfavorable expectations. Respondents raised accuracy and privacy concerns and valued education and permission. The response rate was low and the sample did not represent the full clinic population. Preimplementation expectations and postexperience reactions answer different questions and should not be combined as if they were one measure. [11]
Explain the workflow in language patients can use to make a decision. Clarify what is captured, what output is created, who reviews it, and how questions or corrections are handled. The explanation should match the organization’s actual configuration and applicable requirements. Do not claim that data are never retained, never leave a location, or are never used for another purpose unless those statements have been verified for the deployed service and contract.
Provide a usable alternative when a patient declines or when a clinician judges ambient capture unsuitable. The patient should not have to negotiate with the technology during a vulnerable conversation. Monitor whether explanations are understandable, whether declining is practical, and whether patients feel able to discuss sensitive information. A high average satisfaction score can coexist with important concerns in a smaller group.
Validate language and communication conditions locally
A prospective Arabic-English evaluation showed the feasibility of a bilingual ambient system in a limited clinical setting, with independently assessed note quality. Its single-arm design and modest sample support further evaluation, rather than assurance that any bilingual workflow will perform equally well. Language performance is a property of the particular system, task, and context being tested. [12]
A simulation study examined communication style, accents, and speech impairments. It found that performance was broadly stable across several tested conditions but vulnerable to particular speech characteristics. Nonsignificant comparisons in a limited simulation do not prove equivalence across accents or populations. The findings support testing representative communication conditions and retaining a reliable alternative when capture is inadequate. [13]
Include patients who use interpreters, assistive communication, or more than one language when those workflows are in scope and the evaluation can be conducted appropriately. Do not assume that strong English-language results transfer automatically. Clarify whether the system is transcribing, translating, summarizing, or performing several transformations, because each can introduce a different kind of error.
Equity monitoring requires a purpose and a response. Collect only information the organization can handle responsibly, use appropriate safeguards, and avoid reporting small identifiable groups. If a performance difference appears, investigate the workflow and system conditions before attributing it to patients. A finding should lead to a concrete adjustment, additional evaluation, or restriction of use, with accountable follow-through.
Respect specialty boundaries
Qualitative work with psychiatrists and simulated patients described possible gains in clinical presence alongside concerns about privacy, stigma, trust, and specialty-specific documentation. Because the encounters were simulated, the results do not establish the experience of patients receiving psychiatric care. They identify questions that a real-world evaluation must address. [14]
Interviews with intensive-care clinicians identified interest in capturing complex discussions, but also concerns about consent, data use, personalization, and team communication. This was a study of perceptions rather than a demonstration that ambient systems safely document rounds or goals-of-care decisions. An outpatient result should not automatically authorize deployment in these settings. [15]
Define scope by workflow, rather than by a broad department label. A scheduled follow-up, a bedside handoff, a family meeting, and multidisciplinary rounds involve different speakers, context, and documentation purposes. Some conversations may require a pause, an alternative method, or a more constrained output. Assign a clinical owner who can change the workflow when its assumptions no longer hold.
Specialty templates also need scrutiny. A template can improve organization while encouraging unsupported completeness if the system fills expected sections with assumptions. Make uncertainty visible and permit sections to remain appropriately incomplete. A concise record of what was actually assessed is preferable to a polished account that implies an examination or decision that did not occur.
Interpret the expanding evidence without overselling it
A scoping review of digital-scribe validation found heterogeneous methods and limited real-world testing across the literature it examined. A rapid review of earlier clinical deployments likewise found sparse evidence and uneven measurement. These reviews are useful maps of the evidence at their search dates; they should not erase later controlled studies or be presented as a complete description of every current product. [16, 17]
An earlier usability study using mock consultations found that editing an automatic summary took somewhat less time than writing manually, while unedited automatic summaries scored less well on documentation quality. Participants were medical students with relevant experience, not a representative sample of practicing clinicians. This supports studying the combined human-and-system process, without translating the observed timing directly into a health-system productivity estimate. [18]
A clinician survey associated ambient adoption with improved perceptions of documentation and work satisfaction. The pre- and postimplementation surveys differed in anonymity and respondent groups, and the authors called for stronger measurement. Such reports can identify promising experiences while remaining insufficient to prove a durable reduction in burnout. [19]
The literature should be read as several related questions. Can a system generate a useful draft? Does a clinician complete documentation with less effort? Does the final record remain accurate? Does the patient experience improve? Does the organization realize a sustainable benefit? Evidence for one question may justify testing the next, but it should not be used as a substitute for answering it.
Build a business case that separates observation from projection
A quality-improvement evaluation combining electronic-record data and surveys found less time in notes and reduced perceived task load, while the measured reduction in burnout was not statistically significant. A multicenter survey-based evaluation reported improved burnout and other experiences after a short period of use. These findings differ in design, population, and measurement; the stronger result in one study should not be generalized to every implementation. [20, 21]
A cohort study comparing users with covariate-balanced nonusers associated scribe use with lower electronic-record and note time, without significant changes in several other outcomes, including appointment volume. A small mixed-methods primary-care evaluation found a significant reduction in characters typed but no significant change in its other measures. Together, these studies argue for a business case that can accommodate limited or uneven effects. [22, 23]
Separate observed benefit from proposed financial translation. Reduced time in notes may improve recovery time, attention, schedule resilience, or capacity. Which outcome occurs depends on staffing, appointment design, demand, and organizational choices. A model that converts every minute into an additional billable encounter contains assumptions that should be explicit and tested.
Include subscription, integration, training, evaluation, privacy and security review, support, change management, downtime, and clinician verification costs. Consider the cost of maintaining an alternative documentation workflow. Report uncertainty with scenarios that are clearly labeled as projections, rather than presenting a single optimistic return as an empirical finding. A sound investment can be justified by better work even when additional revenue is not demonstrated.
Connect procurement to clinical operations
A competitive analysis of six scribes used standardized encounters to examine usability, technical performance, and accuracy. Products differed, and none was consistently free of errors. The limited scenarios cannot identify the best product for every organization, but they demonstrate the value of evaluating concrete tasks instead of relying on a generic demonstration. [24]
A separate note-quality study compared ambient drafts with physician-authored reference notes using recordings and transcripts. Overall quality, thoroughness, organization, succinctness, and unsupported content did not move uniformly in the same direction. The restricted information available to both sets of authors also matters. Preference for a note is not equivalent to proof that it contains fewer consequential errors. [25]
Require a deployment specification that identifies the product version, integrations, permitted users, supported languages, authorized outputs, retention settings, and escalation contacts. Contract and security specialists should verify the applicable data-handling arrangements. Clinical governance should determine what evidence is required before expanding use. Neither review replaces the other.
Plan for change after purchase. Model updates, new templates, EHR changes, altered data flows, and expanded clinical settings can change performance. Establish how changes are disclosed, assessed, and, when necessary, limited or reversed. Maintain a usable fallback. The organization needs operational authority to respond to a safety concern without waiting for the next annual procurement review.
A managerial decision table
The following table is a proposed governance aid. It distinguishes decision areas so that success in one cannot conceal a material failure in another.
| Decision area | Evidence to examine | Accountable response |
|---|---|---|
| Workload | Time in notes, total EHR work, after-hours work, verification effort, and use patterns | Clinical operations checks whether work improved or moved elsewhere |
| Documentation quality | Representative draft and signed-note review, consequential omissions and inaccuracies, correction burden | Clinical safety owner investigates patterns and changes the authorized workflow |
| Patient trust | Understandable explanation, practical choice, experience, concerns, and correction access | Patient-experience and clinical teams repair the process and examine unequal effects |
| Technical operation | Integration reliability, failed captures, version changes, availability, and support demand | Technology owner maintains controls, fallback, and change records |
| Organizational value | Observed outcomes, full operating cost, explicit financial assumptions | Executive sponsor decides whether to continue, adapt, expand, or stop |
No universal numeric threshold is proposed here. The organization should define acceptable performance and escalation criteria with the responsible clinical, technical, and professional reviewers before evaluation. Thresholds should reflect the seriousness of possible harm and the reliability of the measurement, not only the desire to complete a rollout.
Run a bounded pilot with an explicit decision
Begin with a defined group and authorized workflow, a documented baseline, and a plan for independent review of consequential concerns. Train clinicians on activation, patient explanation, limitations, verification, correction, and fallback. Make it clear how to report an error or near miss without requiring the clinician to diagnose the underlying software problem.
Review the pilot as an operating process. Examine who was offered access, who used it, which encounters were excluded, how often it failed, what work changed, and what patients reported. Investigate both unusually favorable and unfavorable results. A small number of enthusiastic users can reveal a useful fit, but it cannot establish that the same result will follow from universal deployment.
At the decision meeting, name the options explicitly: continue within scope, change the workflow and retest, expand with conditions, or discontinue. Record the evidence supporting the choice, its limitations, the accountable owner, and the date or event that will trigger reconsideration. Do not describe a pilot as successful merely because its licenses were used or its average satisfaction score was high.
Expansion should carry its measurement plan with it. New specialties, languages, patient groups, and outputs can introduce conditions the pilot did not test. Preserve a route for clinicians and patients to raise concerns after the initial evaluation ends. A responsible rollout produces an ongoing capability to observe and respond, rather than a one-time approval document.
Make the data flow understandable to its owners
Before a pilot starts, trace the actual path from the encounter to the signed record. Identify the devices involved, where processing occurs, which services receive information, what is stored, and how approved content enters the EHR. This is a proposed operational control, not an assertion that all ambient systems use the same architecture. The map should describe the deployed configuration, including any organization-specific integration.
Assign an owner at every consequential transition. Someone must be able to answer what happens when a recording is interrupted, a draft is delivered to the wrong workflow, a clinician leaves the organization, or a patient raises a correction request. Define which records the organization needs for investigation and which information should not be retained unnecessarily. Decisions about access, retention, and secondary use require the applicable professional and organizational review.
Test ordinary failure conditions as well as ideal use. A network interruption, device change, delayed draft, or unavailable integration should lead to a clear next step. Clinicians need to know whether to continue with the established documentation method, recover a draft, or seek support. They should not have to infer the state of a recording from an ambiguous icon while managing a clinical encounter.
The purpose is to make accountability practical. A long contract can describe obligations without giving the clinical team a usable response when something goes wrong. Translate the verified arrangements into concise operating instructions, role-specific training, and a support pathway that can reach the people authorized to act.
Learn from an error without waiting for a trend
An important inaccuracy may require immediate action even when the average quality score remains favorable. The local response should begin with the affected record and any clinical consequences: who needs to review the information, whether a correction is required, and whether an established patient-safety process should be activated. The appropriate clinical and professional teams should determine those actions under existing policies.
Then examine how the error passed through the workflow. Distinguish what was said, what was captured, what was generated, what the clinician could see, and what was signed. A missed correction might reflect a system defect, misleading presentation, inadequate context, excessive review workload, or several conditions together. Labeling every event “user error” prevents useful learning; attributing every edit to a model defect is equally uninformative.
Feed the findings into the authorized-use decision. Responses may include a template change, additional training, narrower scope, vendor investigation, enhanced sampling, or temporary suspension of a particular function. These are proposed options whose suitability depends on the event. Record the decision and verify that the chosen action reaches the people using the system.
A reporting channel should also return information to those who raised concerns. Explain what was found, what remains uncertain, and what changed when that information can appropriately be shared. This closes the organizational learning process and makes it easier for clinicians to identify problems before they become repeated defects in signed records.
Limitations and implications for leaders
This literature is growing quickly, while products, interfaces, and implementation practices change. Many evaluations are short, voluntary, single-system, or dependent on self-report. Some studies examine simulated encounters or limited language conditions. Observational adjustment reduces certain biases but cannot remove all differences between users and nonusers. Negative or nonsignificant results also require interpretation in light of sample size and measurement quality.
The review does not provide a vendor ranking, a legal assessment of recording or privacy requirements, or a validated universal deployment standard. Its proposed controls are management recommendations inferred from recurring findings and limitations. Local clinical, privacy, security, accessibility, and professional review remains necessary for the actual service and population involved.
Ambient documentation deserves serious evaluation because the work it addresses matters. Leaders can make that evaluation more credible by asking for evidence of a usable final record, a measurable improvement in work, and an acceptable patient experience. Those are related responsibilities with separate evidence. Keeping them visible allows organizations to recognize real benefits while responding promptly when the technology creates work or risk that a polished draft can conceal.
References
- Lukac PJ, Turner W, Vangala S, Chin AT, Khalili J, Shih YT, et al. Ambient AI Scribes in Clinical Practice: A Randomized Trial. NEJM AI. 2025;2(12). doi: 10.1056/aioa2501000.
- Guo Y, Wang J, Hu D, Tam S, Gilman C, Chow E, et al. Evaluating ambient artificial intelligence documentation: effects on work efficiency, documentation burden, and patient-centered care. Journal of the American Medical Informatics Association. 2026;33:273–282. doi: 10.1093/jamia/ocaf180.
- Husa RA, Haggerty J, Nute AW, Levine J, Love K, Martinez X, et al. Ambient Artificial Intelligence Use and Clinician Documentation Burden, Productivity, and Efficiency. JAMA network open. 2026;9(5):e2615762. doi: 10.1001/jamanetworkopen.2026.15762.
- Tan JYE, Rafi IBM, Sng GGR, Tung JYM, Lim DYZ, Ong JCL, et al. Impact of an Ambient AI Scribe Among Clinicians and Patients: Real-World Prospective Observational Time-Motion Study. JMIR medical informatics. 2026;14:e85580. doi: 10.2196/85580.
- Harvey CJ, Morita J, Huynh W, Woo RK, Lee JP. Ambient AI Scribe Implementation in an Ambulatory Setting in a Single Medical Group: Prospective Study. JMIR medical informatics. 2026;14:e84104. doi: 10.2196/84104.
- Preiksaitis C, Alvarez A, Winkel M, Karamatsu M, Brown I, Sama N, et al. Ambient AI Scribes and Emergency Department Documentation Burden: Retrospective Cohort Study. JMIR AI. 2026;5:e92193. doi: 10.2196/92193.
- Guo Y, Hu D, Yang Z, Kim S, Tran B, Lee J, et al. What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis. Journal of the American Medical Informatics Association : JAMIA. 2026;33(8):1457-1465. doi: 10.1093/jamia/ocag073.
- Guo Y, Hu D, Yang Z, Chow E, Tam S, Perret D, et al. Clinicians’ rationale for editing ambient AI–drafted clinical notes: persistent challenges and implications for improvement. Journal of the American Medical Informatics Association : JAMIA. 2026;33(7):1345-1353. doi: 10.1093/jamia/ocag059.
- Taylor SL, Jost M, MacDonald S, Ren Y, Hilton S, Davenport S, et al. Quality of Clinical Notes Created by Ambient Listening Generative AI: Pragmatic Prospective Pilot Study. JMIR medical informatics. 2026;14:e86474. doi: 10.2196/86474.
- Shah SJ, Murtagh KN, Char D, Jeong Y, Crowell T, Smith M, et al. Patient perspectives on clinicians’ use of ambient AI scribes. JAMIA open. 2026;9(3):ooag104. doi: 10.1093/jamiaopen/ooag104.
- Leiserowitz G, Mansfield J, MacDonald S, Jost M. Patient Attitudes Toward Ambient Voice Technology: Preimplementation Patient Survey in an Academic Medical Center. JMIR medical informatics. 2025;13:e77901. doi: 10.2196/77901.
- Khan UT, Khan AT, Aljaadi W, Alhadlaq R, Baqashmer Z, Alsafi Y, et al. A Bilingual Arabic-English Ambient AI Scribe for Clinical Documentation: Prospective Evaluation Study. JMIR medical informatics. 2026;14:e83335. doi: 10.2196/83335.
- Draper TC, Leake J, Cox T, Lamb-Riddell K, Johns BE, McCormick J, et al. AI-generated clinical summaries: errors and susceptibility to speech and speaker variability. BMJ health & care informatics. 2026;33(1):e101918. doi: 10.1136/bmjhci-2025-101918.
- Bokhari SA, Nawaz FA, Usman FM, Arshad Z, Sudhir M, Krage R, et al. Clinician and simulated patient perspectives on ambient AI scribes in psychiatric consultations: a qualitative study. Frontiers in psychiatry. 2026;17:1821065. doi: 10.3389/fpsyt.2026.1821065.
- Jalilian L, Manafi N, Vandiver MS, Lukac P, Kadambi A. Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study. JMIR medical informatics. 2026;14:e81445. doi: 10.2196/81445.
- Kerimoğlu E, Notermans FV, Silkens MEWM, de Mul M, Ahaus KCTB, van der Boon RMA. Validating Digital Scribes: A Scoping Review of Evaluation Practices and Clinical Use. Journal of medical systems. 2026;50(1):62. doi: 10.1007/s10916-026-02392-3.
- Kanaparthy NS, Villuendas-Rey Y, Bakare T, Diao Z, Iscoe M, Loza A, et al. Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review. JMIR AI. 2025;4:e76743. doi: 10.2196/76743.
- van Buchem MM, Kant IMJ, King L, Kazmaier J, Steyerberg EW, Bauer MP. Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study. JMIR AI. 2024;3:e60020. doi: 10.2196/60020.
- Albrecht M, Shanks D, Shah T, Hudson T, Thompson J, Filardi T, et al. Enhancing clinical documentation with ambient artificial intelligence: a quality improvement survey assessing clinician perspectives on work burden, burnout, and job satisfaction. JAMIA open. 2025;8(1):ooaf013. doi: 10.1093/jamiaopen/ooaf013.
- Stults CD, Deng S, Martinez MC, Wilcox J, Szwerinski N, Chen KH, et al. Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians. JAMA network open. 2025;8(5):e258614. doi: 10.1001/jamanetworkopen.2025.8614.
- Olson KD, Meeker D, Troup M, Barker TD, Nguyen VH, Manders JB, et al. Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout. JAMA network open. 2025;8(10):e2534976. doi: 10.1001/jamanetworkopen.2025.34976.
- Pearlman K, Wan W, Shah S, Laiteerapong N. Use of an AI Scribe and Electronic Health Record Efficiency. JAMA network open. 2025;8(10):e2537000. doi: 10.1001/jamanetworkopen.2025.37000.
- Alpert JM, Saper R, Boose E, Ruff J, Hopkins K, Gaskins D, et al. Evaluating an artificial intelligence scribe for clinical documentation. Digital health. 2025;11:20552076251395588. doi: 10.1177/20552076251395588.
- Ha E, Choon-Kon-Yune I, Murray L, Luan S, Montague E, Bhattacharyya O, et al. Evaluating the Usability, Technical Performance, and Accuracy of Artificial Intelligence Scribes for Primary Care: Competitive Analysis. JMIR human factors. 2025;12:e71434. doi: 10.2196/71434.
- Palm E, Manikantan A, Mahal H, Belwadi SS, Pepin ME. Assessing the quality of AI-generated clinical notes: validated evaluation of a large language model ambient scribe. Frontiers in artificial intelligence. 2025;8:1691499. doi: 10.3389/frai.2025.1691499.
Disclaimer
This Management Atlas article provides evidence-informed executive education. It does not provide medical, legal, or regulatory advice and does not replace organization-specific professional review.
