Case study · Healthcare · medicodiagnostics.in

Medico Diagnostics — healthcare that reports in hours.

A diagnostics laboratory’s entire catalogue and booking flow, moved online: 3,291+ tests published with transparent pricing, 4–6 hour reporting and 99% accuracy — for patients who used to learn all of this at a front desk.

• 3,291+tests published
• 4–6hreporting
• 99%accuracy

The problem

Diagnostics is an information business wearing a laboratory coat. Patients choose labs on three questions — what tests exist, what they cost, how fast results arrive — and most labs answer none of them online. Medico Diagnostics wanted its full catalogue, pricing and turnaround published, bookable and searchable.

Challenges

• Catalogue scale. 3,291+ tests with panels, packages and preparations — structured data that must stay correct as the lab’s offerings evolve.
• Health-information gravity. Content about medical tests must be precise and legible to worried people on phones; ambiguity has a human cost.
• Operational truth. “4–6 hour reporting” is a promise a website makes and a laboratory must keep — the platform had to reflect operations, not decorate them.

Architecture & stack

The platform runs on our standard production architecture: a mobile-first React front end over Node.js services, PostgreSQL modelling the test catalogue (tests, panels, packages, preparation rules, pricing) with Redis caching the hot catalogue queries, Nginx at the gateway, and a GitHub Actions pipeline gating every release with tests and AI review. The catalogue is fully server-rendered and structured for search engines — a diagnostics catalogue is exactly the content patients search for.

Results

• The full catalogue — 3,291+ tests — is public, priced and bookable.
• Turnaround expectations (4–6 hours) are published per test, aligning patient expectations with lab operations.
• The lab’s accuracy standard (99%) is stated where patients decide, not discovered after.

Lessons learned

In healthcare software, the database schema is a promise to a patient. Modelling the catalogue rigorously — not as CMS pages but as structured clinical data — is what made scale, search and trust possible. That discipline now anchors our platform work in every regulated domain.

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