One warehouse for bookings, results and billing,
and a real picture of lab throughput
A diagnostic lab usually has bookings in one system, results turnaround in the LIS, and billing in a third, which makes it hard to say which test type or branch is actually profitable once turnaround delays and no-shows are counted. We connect it all into one warehouse and give you the real numbers per branch and test type.
Why medical labs and diagnostics lose money today
A diagnostic lab typically tracks bookings in one system, turnaround and sample status in its LIS, and billing or insurance claims in a third, three systems that rarely talk to each other. A lab deciding whether to add capacity for a specific test type is often deciding on a gut feeling rather than real revenue and turnaround data, because nobody has joined the three together.
The deeper gap is turnaround time, which almost never gets tracked per test type or branch in a way that is visible day to day. A test type that looks fine on average can be badly delayed at one branch specifically, and averaging the two together hides exactly the signal that matters.
A third problem is no-shows, which cost a lab a slot that could have gone to another patient and are rarely tracked precisely enough to show which test types or time slots lose the most to them.
A fourth, specific to a lab billing insurers directly, is the lag between a test being performed and a claim actually being paid. Without a warehouse joining the clinical and billing sides, a lab can look busy and still be bleeding cash to slow-paying claims that nobody is actively chasing, because the data needed to flag a stalled claim lives in a system nobody checks against the booking calendar.
What we build
A warehouse joining bookings, LIS and billing. Your booking system, your LIS for sample status and turnaround, and your billing or insurance claims system, reconciled into one database with a daily sync.
Turnaround-time tracking per test type and branch. Not a blended average, broken out so a delay at one location or in one test type is visible immediately.
No-show and rebooking dashboards. Which test types and time slots lose the most to no-shows, so scheduling can adjust where it actually matters.
Revenue and cost per test type. So a decision about adding capacity is based on real numbers, not a guess.
Claims and payment-lag tracking. For labs billing insurers, a dashboard showing how long a claim has sat unpaid, broken out by payer, so a stalled claim gets flagged and chased instead of aging silently in a system nobody checks against the booking calendar.
An AI analyst for ongoing questions, answering questions like “which branch has the slowest turnaround this month” in Telegram, with the query it ran.
Typical integrations: your booking system, LIS, billing or insurance claims system, and a daily sync job with historical backfill.
What stays with humans
The AI analyst’s access excludes patient-identifying fields by design, and any decision about capacity, staffing or pricing stays with your team. The warehouse surfaces the numbers; people make the calls.
Price and timeline
| Model | Price | What you get |
|---|---|---|
| Agency runs it | from $2,500 | We build, sync and maintain the warehouse and dashboards |
| Full control, handover-ready | from $4,250 | Same build, full source code, documentation and the warehouse schema transferred, paid edits available as an option |
Both run on the same 3 to 6 week build.
Related
Pair this with an AI agent for medical labs and diagnostics so booking and FAQ data feed the same warehouse, or a website for medical labs and diagnostics if the booking flow itself needs fixing first. See the full analytics service page. A two-brand warehouse with an AI analyst is detailed in the analytics hub case study, and a multi-location clinic build is in the medical centres network case study. Get in touch and we will reply with a fixed price and plan.
FAQ
How much does this cost and what do we get for it?
From $2,500 for a warehouse connecting bookings, your LIS and billing, with dashboards on turnaround, no-shows and revenue per test type. An AI analyst layer on top is $5,000 and up. A tracking audit alone starts around $800.
How long does it take?
3 to 6 weeks for the warehouse and dashboards. Adding the AI analyst extends that to 6 to 10 weeks.
Which systems do you connect?
Your booking system or calendar, your LIS where an API exists, your billing or insurance claims system, and a spreadsheet export where no API is available.
Can an AI analyst really be trusted with real lab data?
It runs read-only SQL on a prepared data mart, one query at a time, with forced limits, and it shows the query it ran rather than a black-box answer. Patient-identifying fields are excluded from what it can access by design.
Can it show which branch or test type is actually profitable?
Yes, that is the main point. Revenue and turnaround time per test type and branch, not a blended total, usually reveal that one test type or location is quietly underperforming once the real numbers are separated out.