AI in Clinical Documentation: Does It Really Save Time?

In this article, we examine how ambient AI systems work, how much time they can save in real-world clinical use, and what the transition from documentation to clinical action may mean, based on the available evidence.

A clinical encounter does not always end when the patient leaves the room. Completing notes, transferring information into the electronic health record (EHR), and carrying out other tasks arising from the encounter make up a significant but often less visible part of a physician’s day.

In a 2016 time-motion study involving 57 physicians across four specialties, physicians were observed to spend only 27% of their workday on direct clinical face time with patients, while 49.2% was spent on EHR and desk work. Physicians who completed after-hours work diaries also reported spending much of their evening time on EHR-related tasks.¹

How do ambient AI scribe systems work?

Ambient AI scribe systems follow the conversation taking place during a clinical encounter and aim to transform clinically meaningful information into a structured draft clinical note. Unlike conventional transcription, their role is therefore not limited to converting speech into text.

Transcription primarily converts spoken audio into written text. Ambient systems go further by organizing the resulting information within its clinical context, with the goal of taking over part of the documentation work that a physician would otherwise complete later.

From the physician’s perspective, the objective is straightforward: reduce the time spent taking separate notes during the encounter or re-entering the same information into another system afterward.

The generated clinical note is reviewed by the physician, edited when necessary, and ultimately approved by the physician.

Do ambient AI scribes really save time?

Available real-world studies suggest that ambient AI scribe use may be associated with measurable reductions in documentation time and time spent in the EHR. However, the magnitude of these gains can vary across users and clinical settings.

A 2026 multisite study evaluated 8,581 clinicians across five academic health systems. Of these, 1,809 were in the group that adopted an AI scribe.

AI scribe adoption was associated with 16 fewer minutes of documentation time and 13.4 fewer minutes of total EHR time per eight hours of scheduled patient time. The researchers used the eight-hour measure to compare clinicians with different work schedules on a common scale.²

In a smaller study conducted at Stanford Medicine, 45 physicians and 17,428 encounters were analyzed. During the period in which an ambient AI scribe was used:

• median documentation time per note decreased by 0.57 minutes,
• daily documentation time decreased by 6.89 minutes,
• total daily EHR time decreased by 19.95 minutes.

The study also found that the magnitude of time savings was not the same for every user.³

These findings suggest that ambient AI can reduce documentation burden. But clinical workload consists of more than writing notes.

From transcription to clinical action

When an encounter ends, the clinical note may be complete, but the work generated by that encounter can continue. Scheduling a follow-up appointment, planning subsequent steps, handling prescription-related processes, or completing actions across different systems can all be part of the same clinical workflow.

For that reason, preparing the note more quickly does not necessarily mean that the entire digital workload arising from the encounter has disappeared.

Dr.Medkit’s approach is not limited to transcription. In addition to turning information generated during the encounter into documentation, it aims to make the clinical and operational tasks arising from that information manageable within the same workflow.

For example, imagine that during an encounter the physician says:

“Let’s schedule the patient for a follow-up in three months.”

A transcription system converts this sentence into text.

An AI scribe may add the follow-up information to the clinical note.

An agent-based system, when the appropriate integrations and authorizations are in place, may understand the request and initiate the follow-up process or prepare the required action for the physician’s approval.

In other words, the goal is not only to write down what was said, but also to bring the work implied by what was said as close as possible to a ready-to-review state for the physician.

Agent-based systems in healthcare do not yet have a clinical evidence base as mature as that of ambient scribe systems. A 2026 scoping review identified only seven eligible studies across five databases, with most of the research remaining at an early and exploratory stage. Nevertheless, the systems reviewed showed capabilities such as initiating actions, pursuing defined goals, and interacting with different tools.⁴

In the same year, the MIRA study published in Nature provided a more concrete example of this direction. In a controlled EHR environment, the system was able to review patient history, order laboratory and imaging tests, evaluate results, and initiate certain clinical actions. However, the study was conducted in a sandbox environment rather than in real-world clinical practice.⁵

Does saved time simply mean seeing more patients?

No. Time saved on documentation does not necessarily translate into shorter visits or more patients seen during the same day. In some cases, the value may lie in allowing physicians to redirect their attention from the screen back to the patient.

In a 2026 direct-observation study conducted in Singapore, 169 clinical encounters were examined. During encounters in which an ambient AI scribe was used, documentation time decreased while the proportion of time physicians spent making eye contact with patients increased. At the same time, there was no significant change in total visit duration.⁶

This suggests that saved time does not always translate into a shorter encounter. In some situations, its value may instead appear in the amount of attention a physician can direct toward the patient rather than the screen.

Another dimension of documentation burden is work that extends beyond scheduled hours and its relationship with burnout. A 2024 systematic review and meta-analysis reported an association between EHR use and burnout among healthcare professionals.⁷

Early findings on ambient AI are also notable in this respect. In a 2025 study conducted across six health systems, self-reported burnout among participants decreased from 51.9% to 38.8% after 30 days of ambient AI use, alongside reported reductions in documentation-related cognitive burden and after-hours documentation.⁸

Because the study was observational, these outcomes should not be attributed to the technology alone. Still, the findings support the potential value of reducing documentation burden for physicians’ work experience.

For this reason, evaluating clinical AI solely by asking “How many minutes does it save?” may be insufficient. Where that time goes back also matters: to the clinical encounter, to the patient, to the physician’s attention, or to time outside working hours.

Why should physician oversight remain central?

As clinical AI systems become capable of carrying out a broader range of tasks, the importance of human oversight does not diminish. On the contrary, appropriate authorization, traceability, and physician approval become increasingly important.

Dr.Medkit therefore places the human-in-the-loop approach at the center of its design.

The system can structure clinical information, prepare documentation, and, with appropriate integrations, initiate certain actions. Clinical decisions and final approval remain with the physician.

This approach positions AI not as a system that makes decisions in place of the physician, but as one that supports the physician’s clinical and operational workflow.

Key takeaways

• Real-world studies suggest that ambient AI scribe systems can reduce documentation time and total EHR time.
• The magnitude of time savings can vary across clinicians and clinical settings.
• Time savings do not simply mean seeing more patients; physician attention and after-hours workload also matter.
• A potential next step for clinical AI is moving beyond transcription and note generation toward systems that can translate clinical intent into workflow actions under appropriate oversight.
• As systems take on more tasks, physician oversight, authorization, and traceability become increasingly important.

Technology can take on more of the work. Decisions and final approval remain with the physician.

References

1. 1Sinsky C, et al. Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Annals of Internal Medicine. 2016. DOI: 10.7326/M16-0961.
2. 2Rotenstein LS, et al. Changes in Clinician Time Expenditure and Visit Quantity With Adoption of Artificial Intelligence-Powered Scribes: A Multisite Study. JAMA. 2026. DOI: 10.1001/jama.2026.2253.
3. 3Ma SP, et al. Ambient artificial intelligence scribes: utilization and impact on documentation time. Journal of the American Medical Informatics Association. 2025. DOI: 10.1093/jamia/ocae304.
4. 4Collaco BG, et al. The role of agentic artificial intelligence in healthcare: a scoping review. npj Digital Medicine. 2026. DOI: 10.1038/s41746-026-02517-5.
5. 5Ferber D, et al. Towards autonomous medical artificial intelligence agents. Nature. 2026. DOI: 10.1038/s41586-026-10675-5.
6. 6Impact of an Ambient AI Scribe Among Clinicians and Patients: Real-World Prospective Observational Time-Motion Study. 2026.
7. 7Wu Y, et al. Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis. JMIR Medical Informatics. 2024. DOI: 10.2196/54811.
8. 8Olson KD, et al. Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout. JAMA Network Open. 2025. DOI: 10.1001/jamanetworkopen.2025.34976.