Original Investigation
Health Informatics
Use of an AI Scribe and Electronic Health Record Efficiency
Kevin Pearlman, Wen Wan, Sachin Shah, et al
JAMA Netw Open 2025;8;(10):e2537000. doi:10.1001/jamanetworkopen.2025.37000
Question Is an ambulatory clinician’s use of an artificial intelligence (AI) scribe associated with improved efficiency in the electronic health record (EHR) when compared with covariate-balanced controls?
Findings In this cohort study including 125 AI scribe users and 478 covariate-balanced nonusers, clinicians who used an AI scribe had reductions in time spent in the EHR system and time in notes (per appointment) compared with the control group. No changes were identified in after-hours time spent documenting per appointment, mean time to close encounter, mean appointment length, or monthly number of completed office visits.
Meaning These findings suggest that AI scribes are associated with reductions in the amount of time clinicians spend documenting and writing notes in the EHR.
Abstract
Importance Time spent interacting with electronic health records (EHRs) is strongly associated with clinician burnout. Artificial intelligence (AI) scribes may offer a promising solution to EHR-related burnout. However, previous studies on their effectiveness are limited by selection bias.
Objective To evaluate the association of an AI scribe with EHR efficiency using a pre-post analysis among AI scribe users and a comparison of AI scribe users with a covariate-balanced control group of nonusers.
Design, Setting, and Participants This retrospective cohort study included ambulatory clinicians at an academic health system during a 3-month pilot period (July 1 to September 30, 2024).
Exposure Use of an AI scribe.
Main Outcomes and Measures Primary outcomes were time spent in the EHR, time spent in notes, and after-hours time spent documenting (“pajama time”) (all per appointment). Secondary outcomes were time to close encounters, appointment length, and monthly appointment volume. Two analyses were conducted: a within-individual pre-post comparison of AI scribe users (n = 125) and nonusers (n = 478), and a between-group comparison of AI scribe users and nonusers using propensity score overlap weighting to balance covariates.
Results A total of 125 AI scribe users (83 women [66.4%]; 69 [55.2%] with >10 years in practice; 46 [36.8%] in a medical subspecialty, 45 [36.0%] in surgery, and 34 [27.2%] in primary care) and 478 covariate-balanced AI scribe nonusers (267 women [55.9%]; 248 [51.9%] with >10 years in practice; 233 [48.7%] in a medical subspecialty, 155 [32.4%] in surgery, and 90 [18.8%] in primary care) were included. In the pre-post analysis, AI scribe users experienced significant reductions in median time in the EHR per appointment (baseline: median, 22.2 minutes [IQR, 12.1-37.0 minutes]; intervention period: median, 20.2 minutes [IQR, 11.5-31.4 minutes]; difference, −2.0 minutes; P < .001), time in notes per appointment (baseline: median, 7.5 minutes [IQR, 4.3-13.4 minutes]; intervention period: median, 7.0 minutes [IQR, 3.6-10.8 minutes]; difference, −0.5 minutes; P < .001), and time to close encounters (baseline: median, 24.4 hours [IQR, 7.7-94.0 hours]; intervention period: median, 17.3 hours [IQR, 5.4-57.0 hours]; difference, −7.1 hours; P < .001), with no significant differences in after-hours time spent documenting, appointment length, or appointment volume. In the weighted generalized linear regression, AI scribe use was associated with an 8.5% (95% CI, −12.8% to −3.9%; P < .001) lower mean EHR time (ie, 2.4 minutes) and a 15.9% (95% CI −21.2% to −10.4%; P < .001) lower mean time in notes (ie, 1.8 minutes) with no significant differences in other outcomes.
Conclusions and Relevance In this retrospective cohort study, clinicians using an AI scribe spent significantly less time in the EHR and in notes in both pre-post and propensity score analyses. These findings suggest that AI scribes may improve documentation efficiency and reduce clinician workload.
