Case study
Leading Dialysis Chain
Healthcare
How do you validate dialysis sites where access, regulation, and patient demand all matter?
Predictive analytics helped balance patient demand, referral proximity, competition, and accessibility.
Problem
What was at stake?
A national dialysis network needed a faster, data-backed way to validate new facilities before major capital investment.
MapZot.AI work
How the decision was modeled.
Outcome
What became clearer?
Cost of being wrong
$8M–$9M per facility
A wrong dialysis facility can mean years of lost ROI, weak utilization, and poor access for patients who need proximity to care.
The goal was not more data. The goal was a cleaner decision before capital, lease commitments, buildout time, and leadership attention were locked in.
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