Aaron T. Chin

Background: Ambient AI documentation reduces documentation burden and electronic health record (EHR) time for ambulatory clinicians. Newer features also queue orders and offer some level of coding support for clinician review. This study compares two commercial products deployed at UCLA Health. Objective: To compare Nabla and Epic Systems, Inc. ambient AI solution among ambulatory clinicians at UCLA Health on usability, documentation burden, perceived note quality, adoption, EHR time and productivity, clinician-reported experience with order queueing and coding support, coding and billing patterns and integrity, work productivity, and patient-reported communication experience. Methods: Two-arm parallel-group randomized trial, 1:1, among 374 ambulatory clinicians at a single academic health system who hold an active license for a standalone ambient application (Nabla). Randomization is covariate-constrained where cohort size permits, balancing age, sex, half-day clinic sessions, specialty group, and baseline System Usability Scale (SUS) score. Intervention runs 11/9/2026 to 1/17/2027, with the first four weeks as lead-in. The primary outcome is SUS score at follow-up, compared between arms. Analysis is by intention-to-treat using mixed-effects models. Ethics and dissemination: Determined exempt by the UCLA Institutional Review Board (IRB-26-2147). Survey completion indicates consent. No patient-level data are collected. Findings will directly inform operational decision making and will be published regardless of direction.

Recurrent sinopulmonary infections are a hallmark of common variable immunodeficiency (CVID), yet electronic health records (EHR) struggle to distinguish multiple discrete infections from a single infection comprising multiple clinic visits. Using diagnosis codes and antibiotic prescriptions, we developed a temporal windowing methodology that groups related sinusitis encounters into episodes based on timing and antibiotic escalation patterns. We applied this approach to 79 CVID patients, 224 rituximab-treated patients with hypogammaglobulinemia, and 2,994 matched controls. Immunodeficient patients had more frequent sinusitis episodes and greater episode burden than controls. Windowed episode features, when combined with infection PheCodes, yielded the best-performing Ridge regression classifiers in both the CVID-only (AUC-ROC 0.787) and pooled CVID/RTX (AUC-ROC 0.724) models, outperforming PheCodes combined with simple encounter counts and PheCodes alone. In a held-out cohort of five patients with pre-diagnosis sinusitis data, the classifier correctly flagged 4 of 5 as high-risk. Quantifying recurrence and refractoriness of infections through temporal windowing improves EHR-based phenotyping of immunodeficiency.

BACKGROUND: Generative AI (genAI) chart summarization tools embedded in electronic health records (EHRs) are being rapidly deployed across U.S. health systems. Although these tools represent a promising solution to alleviate cognitive burdens, their effects have not been examined in randomized-clinical trials (RCTs). METHODS: In this pragmatic RCT at a single academic health system, 284 outpatient clinicians across forty-two specialties were assigned 1:1 to Epic’s outpatient chart summarization tool or a usual-care control arm over 90 days, from February 23 to May 23, 2026. The primary outcome was physician task load (PTL) adapted for pre-charting. Prespecified exploratory outcomes included additional validated psychometrics as well as usability, safety, and time-based measures. Descriptive statistics included interaction and usage of the tool. RESULTS: Of 74,474 AI chart summaries generated, 14.2% were interacted with by a clinician; the proportion of generated summaries interacted with declined from 21.5% in month 1 to 10.5% in month 3, and the proportion of clinicians using the tool at least once per month declined from 88.7% to 66.2%. The adjusted between-arm difference in PTL at follow-up favored the intervention arm (scale 0-400; −27.4; 95% CI, −49.4 to −5.3; P=0.02). Among the Professional Fulfillment Index (PFI; scale 0-4, lower=better) psychometrics, overall burnout (−0.20; 95% CI, - 0.38 to −0.01) and work exhaustion (−0.24; 95% CI, −0.47 to −0.02) were lower in the intervention arm, with little difference in overall professional fulfillment (+0.04; 95% CI, −0.16 to 0.25). Charting time per encounter showed no significant between-arm difference during steady state (−1.2 seconds; 95% CI, −19.0 to 16.6). The net promoter score was −22, indicating that on average, clinicians did not recommend the tool. Among free-text respondents, 57.1% reported at least one concern, most commonly tool limitations or inaccurate information. No adverse patient safety events or near-misses were reported. CONCLUSION: An EHR-integrated AI chart summarization tool modestly reduced physician task load and was associated with lower burnout, without time savings and against declining engagement. Sustained usage and oversight of reported inaccuracies remain open challenges. Trial Registration: ClinicalTrials.gov Identifier: NCT07438743

Background: EHR documentation and chart review contribute to clinician workload and burnout. To alleviate pre-charting burden, Epic has released a new generative AI chart summarizer tool, which has become widely adopted; however, its impact has not been examined in randomized trials. Objective: To evaluate whether access to an Epic generative AI chart summarization tool reduces cognitive task load among ambulatory providers compared with usual care. Methods: Two-arm, parallel-group randomized controlled trial among ambulatory clinicians across multiple specialties. Clinicians will be randomized 1:1 to tool access versus usual care for 90 days. The primary outcome is change in a 4-item physician task load (PTL) adapted for the pre-charting task. Exploratory outcomes include EHR-derived time metrics (Caboodle and Signal), professional fulfillment/burnout (PFI), usability (SUS), clinician satisfaction, aggregated patient experience item from CG-CAHPS, and reported safety related metrics. Ethics and Dissemination: Analyses will use clinician-level survey responses and aggregated EHR metrics; no patient-level protected health information will be included in the analytic dataset. Results will be disseminated via preprint and peer-reviewed publication. Article summary – Strengths and limitations of this study: This study is a 3-month pragmatic randomized controlled trial evaluating a native EHR-embedded generative AI tool that summarizes prior clinical notes for ambulatory encounters. The primary outcome uses a validated cognitive task load instrument adapted specifically for pre-charting activities. Exploratory outcomes include objective EHR-derived time metrics, validated psychometric measures of burnout and professional fulfillment, and clinician-reported survey measures assessing perceived usefulness of the tool. The trial is single-centered, which may limit generalizabilty, and the intervention is optional-use and unblinded, which may attenuate observed effects and introduce performance bias.

Ambient artificial intelligence (AI) scribes record patient encounters and rapidly generate visit notes, representing a promising solution to documentation burden and physician burnout. However, the scribes’ impacts have not been examined in randomized clinical trials. In this parallel three-group pragmatic randomized clinical trial, 238 outpatient physicians, representing 14 specialties, were assigned 1:1:1 via covariate-constrained randomization (balancing on time-in-note, baseline burnout score, and clinic days per week) to either one of two AI scribe applications — Microsoft Dragon Ambient eXperience (DAX) Copilot or Nabla — or a usual-care control group from November 4, 2024, to January 3, 2025. The primary outcome was the change from baseline log writing time-in-note. Secondary end points measured by surveys included the Mini-Z 2.0, a four-item physician task load (PTL), and Professional Fulfillment Index — Work Exhaustion (PFI-WE) scores to evaluate aspects of burnout; work environment; stress; and targeted questions addressing safety, accuracy, and usability. DAX was used in 33.5% of 24,696 visits; Nabla was used in 29.5% of 23,653 visits. Nabla users experienced a 9.5% (95% confidence interval [CI], −17.2% to −1.8%; P=0.02) decrease in time-in-note versus the control group, whereas DAX users exhibited no significant change versus the control group (−1.7%; 95% CI, −9.4% to +5.9%; P=0.66). Increases in total Mini-Z (scale 10–50; DAX 2.83 [95% CI, +1.28 to +4.37]; Nabla +2.69 [95% CI, +1.14 to +4.23]) and reductions in PTL (scale 0–400; DAX −39.9 [95% CI, −71.9 to −7.9]; Nabla −31.7 [95% CI, −63.8 to +0.4]), and PFI-WE (scale 0–4; DAX 0.32 [95% CI,−0.55 to −0.08]; Nabla −0.23 [95% CI, −0.46 to +0.01]) scores suggest improvement for users of either scribe versus the control. One grade 1 (mild) adverse event was reported, while clinically significant inaccuracies were noted “occasionally” on five-point Likert questions (DAX 2.7 [95% CI, 2.4 to 3.0]; Nabla 2.8 [95% CI, 2.6 to 3.0]). Nabla reduced time-in-note versus the control. Both DAX and Nabla resulted in potential improvements in burnout, task load, and work exhaustion, but these secondary end point findings need confirmation in larger, multicenter trials. Clinicians reported that performance was similar across the two distinct platforms, and occasional inaccuracies observed in either scribe require ongoing vigilance. (Funded by the University of California, Los Angeles, Department of Medicine and others; ClinicalTrials.gov number, NCT06792890.)

Importance Ambient artificial intelligence (AI) scribes record patient encounters and generate visit notes almost instantaneously, representing a promising solution to documentation burden and associated physician burnout. Despite swift and widespread adoption of AI scribes, their impacts have not been examined in randomized-clinical trials. Objective To test the effectiveness of two AI scribes in reducing time spent writing notes and associated burnout in a randomized-clinical trial. Design Parallel three-arm pragmatic randomized-clinical trial where physicians were assigned 1:1:1 via covariate-constrained randomization (balancing on time-in-note, baseline burnout score, and clinic days /week) to either one of two AI scribe applications—Microsoft DAX or Nabla—or a usual-care control group from 11/4/2024-1/3/2025. Setting A large academic health system in California. Participants 313 outpatient physicians were recruited based on leadership referrals and department-wide emails. 238 participants representing 14 specialties qualified. Intervention Intervention-arm physicians gained access to an AI scribe for two months. Main Outcomes and Measures The primary outcome was change from baseline log writing time-in-note. Secondary outcomes measured by surveys included Mini-Z 2.0, 4-item physician task load (TL), and Professional Fulfillment Index-Work Exhaustion (PFI-WE) scores to evaluate aspects of burnout, work environment, and stress, as well as targeted questions addressing safety and accuracy. Results DAX was used in 33.5% of 24,696 visits; Nabla was used in 29.5% of 23,653 visits. Nabla users experienced a 9.5% [95% CI:-17.2%,-1.8%] (p=.02) decrease in time-in-note versus the control group and a 7.8% [-15.5%,-0.1%] (p=.05) decrease versus DAX users, while DAX users exhibited no significant change versus control (-1.7% [-9.4%,+5.9%]; p=.66). Total Mini-Z, scaled 10-50 with higher scores indicating improvement, increased with users of any scribe (+2.76 [+1.41,+4.10]; p<.001). Reductions in TL (scale 0-400, TL=-35.8 [-63.7,-7.9]; p=.01) and work exhaustion (scale 0-4, PFI-WE=-0.27 [-0.48,-0.07]; p=.01) were seen with users of any scribe. One Grade 1 (mild) adverse event was reported, while clinically-significant inaccuracies were noted “occasionally” on 5-point Likert questions (DAX 2.7 [2.4-3.0] vs. Nabla 2.8 [2.6-3.0]; p=.68). Conclusion and Relevance Use of Nabla reduced time-in-note, while use of any scribe led to modest improvements in physician burnout, work exhaustion, and task load. Performance was remarkably similar across two distinct vendor platforms, and occasional inaccuracies observed in either scribe require ongoing physician vigilance. Trial Registration ClinicalTrials.gov Identifier: NCT06792890