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Healthcare marketplace search

Patients do not search using specialty names. They search using symptoms, in their own words. Bridging that gap is a retrieval problem with a clinical vocabulary sitting in the middle of it.

The problem

Sector
Healthcare marketplace
Query type
Symptoms, plain language
Retrieval
BM25 + dense, fused
Role
Search and AI

Someone types "chest pain when I climb stairs". Keyword search finds nothing useful, because no cardiologist profile contains that sentence. Pure semantic search does better on the phrasing but becomes unreliable the moment a query contains an exact clinical term like a named procedure.

Both failure modes are real and they pull in opposite directions, so the answer is to run both and fuse the scores, with a medical terminology layer expanding symptoms, aliases and abbreviations before either channel sees the query.

Architecture

QUERY EXPAND RETRIEVE FUSE RANK RESULT Patient query plain language Terminology layer symptoms · aliases Keyword search BM25 Semantic search embeddings Score fusion weighted merge Rank approved · available Specialists with suggestions neither channel alone handles both a described symptom and a named procedure
Medical report analysis · Provider onboarding pipeline · RBAC and audit logging

Decisions worth defending

The vocabulary is the product

A hand-built terminology map covering specialties, symptoms and aliases does more for result quality than any embedding model choice. It is unglamorous, domain-specific work, and it is the part that cannot be bought off a shelf.

Fuse, do not choose

Picking keyword or semantic means accepting one of two failure modes. Running both and merging costs a few milliseconds and removes the choice entirely.

Suggestions where confidence is low

When a query matches weakly, the honest response is a prompt toward better terms rather than a confident list of loosely related doctors. In healthcare specifically, a wrong confident answer is worse than an admitted gap.

Stack

Search

  • BM25 keyword
  • Dense embeddings
  • Score fusion
  • Prefix expansion

Backend

  • Python API
  • PostgreSQL
  • In-memory index
  • Scheduled reindex

AI features

  • Medical report analysis
  • Onboarding automation
  • Notification routing

Compliance

  • Role-based access
  • Admin audit logs
  • Activity trails
  • Payment records
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