Before deciding whether an AI patient assistant is worth the investment, run the specific calculation for your site. Most sites that complete this calculation find that AI pays for itself within 4–8 weeks — not because it is magic, but because the coordinator time it frees and the lead conversion it recovers are worth 10–30x the tool’s monthly cost. This guide walks through the ROI calculation step by step so you can run the numbers for your site right now.
Component 1: Coordinator Time Saved Per Month
Track how much coordinator time currently goes to activities that AI can handle:
- Answering FAQ calls from patients who ultimately do not qualify: average 12 minutes per call × (calls per month × 50% FAQ-only rate)
- Initial eligibility phone screens: average 18 minutes × (total screens per month × 35% screen failure rate on criteria AI could catch)
- Scheduling calls for patients who could self-schedule: average 8 minutes × number of scheduling calls per month
Sum the monthly hours. Multiply by coordinator hourly cost (salary + benefits ÷ annual hours). This is your monthly coordinator time cost that AI can absorb.
Component 2: Recovered Lead Value
Estimate the number of leads currently going cold due to response delay:
- Monthly inquiries × percentage where first contact is more than 4 hours after submission = cold leads per month
- Cold leads × your site’s inquiry-to-enrolled rate × sponsor reimbursement per enrolled patient = monthly recovered enrollment value
Example: 60 monthly inquiries × 35% cold-off rate = 21 cold leads. 21 × 12% enrolled rate = 2.5 additional enrolled patients per month. 2.5 × $2,200 sponsor reimbursement = $5,500 monthly recovered value.
Component 3: Screen Failure Reduction Value
Estimate the cost reduction from AI pre-screening:
- Monthly screening visits × screen failure rate × percentage of failures on AI-catchable criteria = AI-preventable failures per month
- AI-preventable failures × average cost per failed screen visit (lab costs + coordinator time + PI time + reimbursement) = monthly screen failure cost reduction
Typical cost per failed screen visit: $180–450 depending on protocol complexity.
The Full ROI Calculation
Monthly AI value = Coordinator time saved + Recovered lead value + Screen failure reduction
Monthly AI cost = Platform subscription ($50–300/month for most platforms) + integration setup (one-time, amortized)
Monthly ROI = (Monthly value − Monthly cost) ÷ Monthly cost × 100
A representative calculation for a mid-sized research site: $2,400 coordinator time saved + $5,500 recovered lead value + $720 screen failure reduction = $8,620 monthly value. Platform cost: $150/month. Monthly ROI: 5,647%. Payback period on setup investment: 3–5 weeks.
The Break-Even Scenario
Even in the most conservative scenario — minimal coordinator time saved, low cold-lead recovery — an AI system that recovers one additional enrolled patient per month from lead response improvement pays for itself. One enrolled patient at typical sponsor reimbursement of $1,500–3,000 covers 6–20 months of AI subscription costs. The question for most sites is not whether AI pays for itself, but how quickly.
48-Hour Action List
- Hour 1: Calculate Component 1: ask your coordinator to track time spent on FAQ calls and eligibility screens for one week. Multiply by 4.3 for monthly estimate. Multiply by hourly cost.
- Hour 2: Calculate Component 2: pull your last 30 inquiry submissions, identify how many had first contact after 4 hours, and calculate the implied enrolled patient loss at your current conversion rate.
- Hour 3: Calculate Component 3: pull your last 20 screen failures, categorize by reason, and calculate the percentage on AI-catchable criteria. Multiply by your cost-per-screen-visit.
- Day 2: Run the full ROI calculation above. If monthly value exceeds monthly AI cost by a ratio of 5:1 or greater — which it does for the majority of sites — proceed with platform selection and 30-day trial implementation. If the ratio is below 3:1, your site may be too small for AI to move the needle meaningfully at this stage.