The Economics of Precision Health for Hospitals
Adding a genomic service line used to mean a lab, a bioinformatics team, and an 18-month build. Pricing is set on the pilot call.
The barrier to genomic revenue for a hospital was never demand. Patients already ask for it, and clinicians already see where it would help. The barrier was the build — the capital, the accreditation, and the year or more it takes to stand up a lab you may never fully utilise. That equation has changed.
The old math
Doing genomics in-house means paying for the whole stack before you see a single report: sequencing hardware, a bioinformatics team, a curated interpretation layer, quality accreditation, and the 12-to-24-month timeline to assemble it. For most hospitals and clinics that is a capital project competing against every other capital project — and one whose payback depends on volume you have to build demand for at the same time. The result is that a service patients want stays on the roadmap indefinitely. (We break the choice down in full in build in-house vs white-label.)
The white-label math
A white-label model inverts the economics. Instead of buying capacity, you deliver each report under your own brand. The capital cost of a lab and a bioinformatics team comes off the table entirely. Pricing is set on the pilot call.
The first offer is a decision-support reading of the bloodwork you already run, with no change to your collection workflow. DNA and microbiome stay separate reports for the patients who want more than one test. The hospital's doctors keep the final say. Pricing is set on the pilot call.
Model it for your practice
We model the numbers with you on the pilot call.
Payback, and where the risk sits
The two variables that move this model most are volume and conversion — not capital, because there isn't any. That is the point. A white-label service line reaches payback in the window it takes to integrate, measured in weeks, and it does so without asking the balance sheet to carry a lab through its ramp. Your brand stays on the report, the patient relationship stays yours, and the interpretation engine is someone else's fixed cost to maintain.
We model the numbers with you on the pilot call. See how this plays out for hospitals and labs.