Empower · Healthcare AI value
Set the baseline, track adoption and workflow change, and connect capacity to results leaders can defend.
Sourced healthcare evidence
These results come from different settings, study designs, and stages of adoption. Each number links to its source and names the evidence type so you can read it in context.
3.3%
Among 6,877 patients with sepsis identified before antibiotics, confirmation of a machine-learning alert by a clinician within three hours was associated with lower in-hospital mortality across a five-hospital deployment.
Nature Medicine, 20226.2 → 5.3 min
A quality improvement study at one large healthcare organization found a decrease in mean time spent in notes per appointment after ambient AI implementation.
JAMA Network Open, 202551.9% → 38.8%
The proportion fell after 30 days of ambient AI scribe use among 263 ambulatory clinicians across six US health systems.
JAMA Network Open, 2025+1.81 RVUs/week
Ambient AI scribe adoption was associated with an annualized $3,044 increase per physician under the 2025 Medicare Physician Fee Schedule, with no increase in claim denials.
JAMA Network Open1,794 workdays
The Permanente Medical Group reported 15,791 hours of physician time returned through ambient scribe use, equivalent to 1,794 working days.
Permanente Medicine4,000+ in 15 weeks
Cleveland Clinic reported two minutes less documentation time per appointment and 14 minutes less per clinician each day after its rollout.
Cleveland Clinic3 min → <40 sec
Penn Medicine reported that coordn8 processed about 200,000 faxes by December 2024, representing more than 4,500 staff hours.
Penn Medicine CHTI82% · 74% · 79%
In 2024, hospitals reported evaluating predictive AI for accuracy at 82%, bias at 74%, and post-implementation evaluation or monitoring at 79%.
ASTP/ONC data brief$7.3M/year
A model trained on claims from 48 million people projected this result for a care management program enrolling 500 people with the highest predicted risk. The result depends on the intervention and model assumptions.
arXiv, 2019The measurement rule
Time saved becomes financial value when it produces throughput, access, revenue, cost reduction, risk reduction, or retention. Before that conversion, report capacity created.
A measurement model
A credible value case starts with the current workflow and follows the change through adoption, use, quality, safety, and organizational results.
Capture current volume, time, cost, quality, safety, and workforce experience before the tool changes the workflow.
Name the users, encounters, tasks, or cases included in the deployment.
Measure actual use across the eligible population and learn why people opt out.
Watch the process, handoffs, review, and escalation around the tool.
Tie the change to capacity, access, quality, safety, cost, revenue, or retention.
Active use and workflow fit set the ceiling for the original business case.
Expected value × active adoption × workflow integration × quality factor
Report capacity first. Count financial value when the time produces a visible organizational result.
Hours saved × loaded hourly cost × redeployment factor
Include integration, security, governance, training, monitoring, and support in the cost.
Total implementation cost ÷ monthly net benefit
State the counterfactual assumptions and the evidence behind them.
Expected loss without AI − actual loss with AI
Pair average gains with the quality and safety signals that can change their meaning.
Gross benefit × quality factor − expected safety loss − monitoring cost
Scaled economics include costs and operating conditions that a pilot often leaves outside its boundary.
Report each one separately
Common questions
Capture the current workflow baseline, including eligible volume, time, cost, quality, safety, and workforce experience. Define the users and cases in scope so adoption and outcomes have a clear denominator.
Report it first as capacity created. Count financial value when that capacity becomes throughput, access, revenue, cost reduction, risk reduction, or retention.
Scaled deployment introduces integration, security, governance, training, monitoring, and support costs. Separate reporting gives leaders a clearer view of the economics at each stage.
Use external benchmarks to shape questions and comparison ranges. Your own baseline, eligible population, adoption rate, workflow design, and outcome measures provide the evidence for your organization.
Choose measures that cover active adoption, workflow performance, quality and safety, capacity or outcomes, and financial effect. Three to five measures usually keep the scorecard focused enough to guide decisions.
Bring one live deployment
We can map the baseline, adoption measures, workflow signals, and outcomes that fit your healthcare AI project.