AI scribes promise to ease documentation burden, but new real-world data reveal a more complex reality: modest efficiency gains, unchanged after-hours work, and important questions about how clinicians actually use the time they save.
Study: Changes in Clinician Time Expenditure and Visit Quantity With Adoption of Artificial Intelligence–Powered Scribes. Image credit: Andrey_Popov/Shutterstock.com
Artificial intelligence (AI)–powered scribes can moderately benefit clinicians by reducing the time to record and document electronic health records and increasing weekly visit volume, as reported in a new study published in JAMA.
AI scribes emerge to tackle growing documentation burden
Electronic health record (EHR) documentation is a time-consuming task for clinicians, taking on average 2.3 hours per 8 hours of patient care. This documentation time is associated with clinician burnout, often limiting clinical capacity, patient access, and quality of care.
Artificial intelligence (AI)–powered documentation tools, also known as AI scribes, have been developed to reduce EHR documentation burden and improve clinician satisfaction. However, studies investigating the effectiveness of AI scribes have produced heterogeneous findings, and evidence on their impact on productivity remains limited.
Given this gap in the literature, the current study explored whether the adoption of AI scribes in health systems can bring any changes in EHR time expenditure and weekly visit volume, and whether these changes vary by clinicians’ characteristics.
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Multisite US study examines real-world AI scribe adoption
The study included five academic health care institutions across multiple regions of the United States that introduced AI scribes to their clinicians. The study population included ambulatory clinicians, advanced practice clinicians, and resident physicians who had the option to use an AI scribe.
In the entire study population, clinicians who were given access to AI scribes were considered AI scribe adopters, regardless of whether they actively used the tool (intention-to-treat definition). Conversely, clinicians who were not given access to AI scribes were considered nonadopters.
The main parameters analyzed in the study were time spent on documentation, time spent on the EHR outside scheduled working hours, and weekly visit volume.
AI scribes modestly reduce EHR and documentation time
The study enrolled 8581 clinicians, including 1809 AI scribe adopters. The participants were from primary care, medical, and surgical specialties, and the majority were attending physicians, followed by advanced practice clinicians and resident physicians.
The analysis of pre- and post-adoption trends indicated that adoption of AI scribes is associated with 13 fewer minutes of EHR time and 16 fewer minutes of documentation time per 8 hours of scheduled patient care, representing 3.0 % and 10.0 % relative decreases in time spent, respectively.
Furthermore, AI scribe adoption was associated with 0.49 additional weekly visits delivered, representing a 1.7 % increase in weekly visit volume. However, no significant changes in EHR time outside working hours were observed following AI scribe adoption.
Among clinician groups, greater improvements following AI scribe adoption were observed in primary care specialists, advanced practice clinicians, female clinicians, residents, and clinicians who used AI scribes in 50 % or more of visits.
The revenue analysis estimated an additional 167.37 USD in monthly evaluation and management (E/M) revenue per clinician associated with AI scribe adoption.
Time savings may shift toward other clinical responsibilities
The study reveals that AI scribe adoption by clinicians in academic health care institutions is associated with a modest reduction in total EHR time and documentation time, and a modest increase in weekly visit volume.
Notably, the study finds that AI scribe adoption does not significantly change work outside of working hours, despite moderate reductions in total EHR time and documentation time. These findings suggest that although AI scribe adoption has saved clinicians’ time on documentation, some of these time savings may be reallocated to other patient care activities, such as reviewing documentation for accuracy, monitoring electronic inbox messages from patients, addressing test results, or conducting medical record review.
As observed in the study, the time saved by AI scribe adoption was highest among primary care specialists, advanced practice clinicians, female clinicians, residents, and clinicians who used AI scribes in 50 % or more of their visits. Among these clinician groups, residents are a crucial population for assessing the usefulness of AI scribe adoption, given how critical documentation is for learning and the unknown implications for resident learning.
Since the number of residents who used AI scribes was limited in the study, the researchers highlight the need for future studies to evaluate the impact of AI scribes on time expenditure and influence on resident learning.
Despite the highest benefits observed for clinicians who used AI scribes in 50 % or more of visits, only 32 % of adopters used their AI scribes that frequently. This finding highlights the need for robust training and support for adopters.
Because of the nonrandomized study design, the observed changes may not be exclusively associated with the adoption of AI scribe; some unmeasured differences between adopters and nonadopters may also influence the findings. Future studies should investigate the reproducibility of these findings and identify the factors that can enhance the benefits of this technology.
Furthermore, the study included only academic health care institutions, with an average weekly encounter volume of about 20. The observed benefits may differ in non-academic settings, where clinicians have much higher visit volumes.
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