Longitudinal effects of ambient AI scribe use on documentation burden and financial productivity: A quasi-experimental study

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Abstract
Importance: Clinician adoption and adaptation of new tools evolve over time. Prior studies of ambient Artificial intelligence (AI) scribes have primarily relied on single time-point measurements (e.g., pre-post), potentially obfuscating their true impact on outcomes. Objective: To investigate longitudinal effects of an AI scribe tool on patient encounter-level outcomes. Design: Case series across 48 weeks (24 pre, 24 post) per clinician. Setting: Primary care clinical encounters occurring between 01/05/24 and 10/31/25. Participants: Primary care clinicians (attending physicians and advanced practice providers). Exposure: Ambient AI scribe introduction to clinical workflow, indexed to study day zero, per clinician. Main outcomes and measures: Encounter-level measurements of documentation time (note writing time, time outside of scheduled hours (TOSH), pajama time), note writing patterns (note length, note closure <24h) and clinician’s billed work Relative Value Units (wRVU) with a focus on changes from pre-period outcomes at Day 0 and 150. Results: 220 primary care clinicians (Mean age=43.7, 70.9% females; 56.4% physicians) from 36 clinics, conducting 314,845 patient encounters were included. All outcomes evolved from day zero to day 150 and are compared back to pre-period levels. There was evidence of an immediate 7% decrease on average in note writing time at day zero (Incidence Rate Ratio, IRR 0.93, 95%CI [0.89, 0.96]), intensifying to a 15% decrease by day 150 (IRR 0.85, 95%CI [0.83, 0.87]). There was no evidence of a change in pajama time or TOSH at day zero; however, at day 150, there was evidence of a 18% decrease in pajama time (0.82, 95%CI [0.73, 0.91]) and a 13% decrease in TOSH (0.87, 95%CI [0.77, 0.99]). At day zero, there was evidence of a 5% increase (1.05, 95%CI [1.00, 1.10]) in note length and 31% increase in note closures (1.31, 95%CI [1.13, 1.53]), with both slowly attenuating to pre-period levels by day 150. Although there was no evidence of changes in wRVU at day zero, there was a 2% increase total wRVU at day 150 (1.02, 95%CI [1.01, 1.03]). Conclusions and relevance: Longitudinal changes were gradual, but persistent, underscoring the gradual adaptation of AI scribes, as clinicians situated these tools within their workflows. Key Points: Question: How do the patterns of use of an ambient Artificial Intelligence (AI) scribe evolve over time? Findings: In this longitudinal, quasi-experimental study on clinician use of an ambient AI scribe, documentation time, note writing patterns and financial productivity evolved over a 150-day period. Compared to the pre-period, note writing time savings increased from 7% (day zero) to 15% (day 150); changes in all other considered outcomes including time outside of scheduled hours, pajama time, note length, note closure <24h, billed work Relative Value Units evolved over the 150-day period. Meaning: Clinician use of ambient AI scribes showed persistent changes in patterns of use over a 150-day period, highlighting a gradual adaptation process and the need for longitudinal assessment.

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