Most labs feel the operational drag: portal logins, downloads, retyping prescriptions, fixing missing info, chasing remakes.
But when it’s time to invest, a feeling doesn’t get a project approved.
As the guide puts it:
“We need automation” isn’t a business case. Numbers are.
This post is a high-level summary of a framework you can run in ~15 minutes to estimate the ROI of intake automation.
The guide starts with intake because it typically has the fastest payback.
It has two core buckets:
Industry benchmark: labs with 8+ portals typically spend 1.5–2.5 hours/day just downloading scans.
Benchmark: 3–5 minutes per case. A 40-case lab spends 2–3.5 hours/day on data entry.
The worksheet converts daily intake hours into annual labor cost using:
Example from the guide:
A 40-case lab spending 4 hours/day on intake = 1,000 hours/year = $25K–$35K/year on downloads + entry alone.
Manual entry creates errors → errors create remakes → remakes destroy margin.
Benchmark: data entry errors can cost labs 2–5% of revenue (ex: $1M lab = $20K–$50K annually).
This is your “cost of doing nothing”—the money you’re spending (or losing) that automation can recover.
The guide recommends not assuming perfect automation overnight. Instead, apply realistic reductions:
Then it suggests a simple conservative method:
Apply a 75% efficiency gain to your total opportunity to estimate annual savings from intake automation alone.
This post is a high-level summary. The full PDF includes the worksheets, benchmarks, and a walkthrough for building an ROI case you can use with partners or investors.
