EvidenceSimulator: Ambient AI Scribes
What does the evidence predict for your context?
This simulator lets you explore how the current research on ambient AI scribes might apply to your specific situation — your product choice, your baseline burnout rate, your implementation approach, and your practice setting.
Every projection is traced to a specific published study. Where the evidence is strong, you'll see tight confidence bands and green badges. Where it's uncertain, you'll see wider bands and orange warnings. And where we simply don't have evidence yet, you'll see grayed-out placeholders — because knowing what we don't know is just as important as knowing what we do.
This is an educational tool, not a deployment calculator. It is versioned (currently v0.1), living (updated as new evidence emerges), and honest about uncertainty. Built using the EvidenceCycle methodology from the same evidence base as our Living EvidenceAtlas: Ambient AI Scribes.
This is an early release. We welcome feedback — reach out via LinkedIn or email.
Your Context
Product selection determines which evidence set drives all projections.
Approximate percentage of your clinicians currently experiencing burnout.
The utilization gap between 71% and 30% is implementation-driven, not technology-driven.
All evidence is from US academic centers. Other settings receive generalizability warnings.
Select all that apply. Output panels will expand for your priorities.
Approximate number of clinicians in initial deployment. Used for NNT-based projections.
Projections
Projected Burnout Prevalence Shift
Absolute Reduction
−18.4pp
NNT
1.68
Clinicians Helped
~30of 50
Sustained across 4 timepoints (6/12/18/24 weeks) in single RCT [1]
Professional fulfillment: no significant improvement expected (+0.14 pts, P=0.04 vs pre-specified threshold of P<0.025) [1]
Durability beyond 24 weeks: no data
Evidence cards: EC-014, EC-009, EC-021, EC-015
Expected daily time savings: 22 minutes
95% CI: 10–33 min/day
Objective EHR audit log data [1]
Corroborated by self-report: −30 min/day (P<0.001, n=237) [2]. Self-report exceeds objective measure, suggesting subjective relief may be greater than measured time savings.
After-hours time: outlier-sensitive, not reliable [1]
Evidence cards: EC-016, EC-020, EC-023, EC-026
71%
Expected utilization rate (range: 55%–80%)
Patient Consent
99.92%
22 declined out of all encounters [1]
Clinician Satisfaction
8.1/10
Post-only, no baseline [2]
Technology alone does not guarantee adoption. Afshar invested in PDSA cycles and human-factors experts [1].
Long-term utilization (>6 months): no data
Evidence cards: EC-028, EC-022, EC-025
Want the full deployment playbook — thresholds, monitoring signals, audit workflows?
The EvidenceSimulator shows you what the evidence predicts. Our programs give you the tools to deploy, monitor, and govern AI in your specific context.
Learn about our programsEvidence Sources
- [1]Afshar M et al. A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being. NEJM AI. 2025;2(12).
- [2]Olson KD et al. Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout. JAMA Network Open. 2025;8(10).
- [3]Lukac PJ et al. Ambient AI Scribes in Clinical Practice: A Randomized Trial. NEJM AI. 2025;2(12).
- [4]Williams CYK et al. Physician- and Large Language Model-Generated Hospital Discharge Summaries. JAMA Intern Med. 2025;185(7).
- [5]Chung P et al. Verifying Facts in Patient Care Documents Generated by Large Language Models Using Electronic Health Records. NEJM AI. 2025;3(1).
- [6]Brodeur P et al. State of Clinical AI Report 2026. ARISE Network. January 2026.
Suggested citation: Chief Health AI. EvidenceSimulator: Ambient AI Scribes, v0.1. 2026. chiefhealthai.com/evidenceatlas/ambient-scribes/simulator.
This simulator is an educational tool for exploring published evidence. It is not a clinical decision aid, not FDA-regulated, and not a substitute for reading the primary evidence. All projections are based on the cited published studies and are subject to the limitations described in the EvidenceAtlas. No user data is collected or stored.