Jae-yeon Lee

All Publications

• Ambient AI Scribes and Emergency Department Documentation Burden: Retrospective Cohort Study. JMIR AI Preiksaitis, C., Alvarez, A., Winkel, M., Karamatsu, M., Brown, I., Sama, N., Morris, L., Lee, J. Y., Gubbels, A., Wahl, E., Frye, A., Schoeffler, A., Gharahbaghian, L., Rose, C. 2026; 5: e92193

  Abstract

  Clinician burnout has reached crisis levels in emergency medicine, with clinical documentation burden identified as a central contributing factor. Ambient artificial intelligence (AI) scribes offer a promising approach to reduce this burden, but objective evidence in the emergency department (ED) setting remains limited, and prior reports have been constrained by short observation windows and low adoption.This study aimed to evaluate the association between ambient AI scribe use and on-shift documentation time during a 13-month staged rollout in a busy ED, accounting for physician- and patient-level factors.We conducted a retrospective cohort study at a tertiary academic ED from February 2025 to March 2026. The analytic cohort comprised 10,344 encounters managed by 100 attending physicians across 4 ED care settings. We restricted analysis to encounters managed by a single attending physician and excluded those with human scribes. The comparison group comprised encounters in which the ambient AI scribe was not used; use was determined entirely at attending physician discretion on an encounter-by-encounter basis. The primary outcome was on-shift documentation time derived from electronic health record audit logs. We used mixed-effects linear models with physician random intercepts to adjust for patient and encounter characteristics.Ambient AI scribe use was associated with a 72.6-second reduction in on-shift documentation time per encounter (95% CI 63.8-81.4; P<.001). The effect was similar in magnitude for high-use physicians (use rates of ≥18.2%, which was the cohort mean; -71.6 seconds) and low or moderate users (-64.2 seconds), with no statistically significant difference (P=.51). Note character count decreased by 690 characters (95% CI 273-1107; P=.001); after-shift documentation time increased modestly by 9.1 seconds (95% CI 2.9-15.3; P=.004). Negative control outcomes were largely null, and a within-clinician placebo permutation test yielded a distribution centered at 0 (mean -0.8 seconds), inconsistent with the observed effect arising from confounding alone.In this single-center analysis, ambient AI scribe use was associated with a statistically significant reduction in on-shift documentation time (P<.001), equivalent to approximately 24 minutes per 8-hour shift if used across 20 encounters. These findings extend prior descriptive work with adjusted inferential evidence and support the clinical relevance of ambient AI scribes for ED documentation burden, although the magnitude of benefit varies by physician, patient, and workflow factors.

  View details for DOI 10.2196/92193

  View details for PubMedID 42391625

• Ambient Artificial Intelligence Scribe Adoption and Documentation Time in the Emergency Department. Annals of emergency medicine Preiksaitis, C., Alvarez, A., Winkel, M., Karamatsu, M., Brown, I., Sama, N., Morris, L., Lee, J., Gubbels, A., Wahl, E., Frye, A., Rose, C. 2026

  Abstract

  STUDY OBJECTIVES: To describe real-world adoption of an ambient artificial intelligence (AI) scribe in the emergency department (ED) and compare documentation time and note characteristics between ambient and standard encounters using electronic health record audit logs.METHODS: We performed a retrospective observational study of adult ED encounters at a tertiary academic medical center. Attending physicians could optionally use an ambient AI scribe to generate notes from patient-clinician conversations. We included single-attending encounters in core ED zones and excluded visits with human scribes. Electronic health record audit logs provided documentation of time during and after the shift, total electronic health record time, and note length. We summarized adoption by physician, zone, and acuity and compared medians between ambient and standard encounters.RESULTS: Among 8,740 eligible encounters, 976 (11.2%) used ambient AI. Thirty-five of 92 attendings (38%) used the tool, and a small group of high-frequency users accounted for most ambient encounters. Ambient use clustered in telemedicine and vertical-care zones (chair-based ambulatory care) and in lower-acuity patients, as well as those not requiring interpreters. Median on-shift documentation time was 2:45 min for ambient encounters versus 3:50 min for standard encounters (difference -1:05; -28%). Median total electronic health record time was 8:39 min versus 10:21 min (-16%), and ambient notes were shorter overall.CONCLUSION: Early ED implementation of ambient AI scribes showed low but highly skewed adoption, with physicians favoring lower acuity, noninterpreted encounters. When used, ambient AI was associated with shorter on-shift documentation time, total electronic health record time, and note length.

  View details for DOI 10.1016/j.annemergmed.2025.12.017

  View details for PubMedID 41665590