HCA 120 Module 8 Ambient AI Scribes and Health Care Work Example

A Listener in the Exam Room: Ambient AI Scribes and the Future of Documentation Work

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Health Care Administration Program, Aspen University

HCA 120: Healthcare Information Systems

Instructor Name

Month Day, Year

A Listener in the Exam Room: Ambient AI Scribes and the Future of Documentation Work

Each wave of health care technology has promised to save time and often added work instead. The newest candidate is the ambient artificial intelligence scribe: software that listens to a visit through a phone or microphone, with permission, and drafts a clinical note for the provider to review. This paper explains how ambient scribes work, reviews early evidence, considers their likely effects on different roles at a composite family practice and proposes a cautious pilot.

The Problem They Target

Ambient scribes aim at the documentation burden that electronic records created. Family physicians tracked through record event logs averaged close to six hours a weekday in the system, part of it at home in the evening, and clerical tasks made up almost half of those hours (Arndt et al., 2017). Observers in ambulatory clinics likewise recorded that nearly half (49.2%) of physicians' clinic hours went to screens and paperwork (Sinsky et al., 2016). A tool that drafts notes could return some of that time.

How They Work

The provider starts the scribe on a phone or computer after the patient agrees. The software records the conversation, converts speech to text, identifies what is clinically relevant and drafts a note organized into the practice's usual sections, often within a minute of the visit's end. The provider reviews, edits and signs the note. Depending on the product and settings, recordings may be deleted after the note is drafted.

What the Evidence Shows

Evidence is early. In a randomized trial of two commercial ambient scribes in outpatient practice, one scribe reduced time spent writing notes by 9.5% compared with a control group, while the other showed no significant change; both were associated with possible improvements in burnout and task load, and clinicians reported that clinically significant inaccuracies occurred occasionally (Lukac et al., 2025). The authors called for larger trials and ongoing vigilance. Time savings, in other words, are real but modest, and accuracy still depends on the provider's review.

Likely Effects by Role

The technology would change work beyond providers. The table summarizes likely effects at the composite practice.

Risks: Accuracy

An ambient scribe can mishear, omit or invent details, such as a medication dose or a symptom the patient denied. Because drafts look polished, providers may review them less carefully than their own notes. A note with an error becomes part of the legal record and can mislead the next clinician. The practice would need clear rules that providers review every draft and a way to report errors.

Risks: Consent and Privacy

Recording a visit raises privacy questions. Patients must be told what is recorded, who processes it and how long it is kept, and be free to decline without any effect on their care. Some visits, such as those involving sensitive topics, may be unsuitable. The vendor must sign an agreement to protect health information, and the practice must know where recordings and transcripts are stored and when they are deleted.

Effects on Coding

Longer, more detailed notes may support higher visit levels. That can be accurate, capturing work that was done but never documented, or it can inflate codes if the note includes more than the provider actually addressed. Coders should audit a sample of scribe-drafted notes against the visits' actual content during any pilot.

A Cautious Pilot

The composite practice would pilot one scribe with three volunteer providers for three months. Measures would include time in notes and after-hours record time from event logs, provider burnout scores, the rate of errors found in drafts, patient consent and decline rates, patient experience comments and a coding audit. A decision to expand would require time savings without an increase in errors or complaints.

Cost and Contracts

Ambient scribe products are usually sold as monthly subscriptions per provider. A practice must weigh that cost against measured time savings and any change in visit volume or provider retention. Contracts should state how the vendor stores and uses recordings, whether data are used to train the vendor's models, how errors are reported and how the practice can end the contract and retrieve or delete its data.

Keeping Up With Change

The course asks how health care professionals can keep learning as technology advances. Ambient scribes will change quickly, and today's evidence may not apply to next year's products. Administrators should read independent trials rather than vendor summaries, ask peers about their experience and build evaluation into every pilot. Staff whose tasks change should be retrained rather than left behind.

Other Emerging Tools

Ambient scribes are one of several tools beginning to spread. Others include software that drafts replies to patient portal messages, systems that suggest codes from notes and tools that predict which patients are likely to miss appointments. Each raises the same questions: does it save time in practice, how often is it wrong, who checks its output and what happens to patient data. The evaluation approach proposed here can be applied to any of them.

Patient Perspective

Patients may welcome a provider who looks at them rather than a screen, or may feel uneasy knowing the visit is recorded. The pilot would ask patients about both. Clear signs, a short explanation at check-in and an easy way to decline would help patients decide without pressure.

Conclusion

Ambient AI scribes target a real problem, the hours providers spend documenting, and early trial evidence suggests modest time savings with occasional inaccuracies. Their effects would reach medical assistants, coders, front desk staff and patients. A careful pilot with clear measures, consent and error reporting would let a practice learn whether the technology helps its own people before adopting it widely.

References

Arndt, B. G., Beasley, J. W., Watkinson, M. D., Temte, J. L., Tuan, W.-J., Sinsky, C. A., & Gilchrist, V. J. (2017). Tethered to the EHR: Primary care physician workload assessment using EHR event log data and time-motion observations. Annals of Family Medicine, 15(5), 419-426. https://doi.org/10.1370/afm.2121

Lukac, P. J., Turner, W., Vangala, S., Chin, A. T., Khalili, J., Shih, Y.-C. T., Sarkisian, C., Cheng, E. M., & Mafi, J. N. (2025). Ambient AI scribes in clinical practice: A randomized trial. NEJM AI, 2(12), Article AIoa2501000. https://doi.org/10.1056/AIoa2501000

Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., Westbrook, J., Tutty, M., & Blike, G. (2016). Allocation of physician time in ambulatory practice: A time and motion study in 4 specialties. Annals of Internal Medicine, 165(11), 753-760. https://doi.org/10.7326/M16-0961