A growing healthtech platform in India was facing a data quality problem.

Its product depended on accurate medicine information across multiple workflows, including medicine search, prescription mapping, procurement references, and patient facing product display. But the platform’s internal medicine data had become fragmented over time.

The same product often existed in multiple formats. Brand names were inconsistent. Strength and pack size data were not normalized. Some products were hard to match across pharmacy, doctor, and back office systems.

As the platform scaled, these inconsistencies started creating operational friction.

The challenge

The company needed to solve five core issues:

  • Duplicate medicine records across systems
  • Inconsistent naming conventions
  • Weak search relevance for medicine lookup
  • Poor mapping between branded and generic products
  • Difficulty maintaining a reliable medicine master

This was slowing down product teams, confusing end users, and increasing manual effort for operations teams.

The solution

The platform adopted DrugSetu as a centralized medicine intelligence layer.

Instead of managing medicine data separately across internal tools, the company used DrugSetu as the reference foundation for structured and standardized medicine information.

DrugSetu helped the team organize data around:

  • Standard medicine names
  • Strength and dosage details
  • Pack size normalization
  • Manufacturer references
  • Searchable structured attributes
  • Cross platform consistency

Implementation approach

The rollout was done in phases.

Phase 1: Medicine master cleanup
The team mapped existing product records against a more structured medicine dataset and removed duplicate logic.

Phase 2: Search and discovery improvement
Medicine search started using cleaner attributes and standardized references, making product discovery more accurate.

Phase 3: Operational alignment
Procurement, catalog, and patient facing interfaces began using the same medicine foundation instead of separate data interpretations.

Outcomes

After implementation, the platform saw measurable operational improvements:

  • Faster medicine search and matching
  • Better consistency across internal systems
  • Lower manual correction effort
  • Cleaner medicine display for end users
  • Improved confidence in procurement and catalog workflows

Most importantly, teams across product, operations, and data were now working from the same source of truth.

Business impact

The company did not just improve data quality. It improved scalability.

With a centralized medicine intelligence foundation in place, the business could launch new features faster, reduce reconciliation issues, and support more reliable healthcare workflows across its platform.

Why this matters

For Indian healthtech companies, medicine data is not just backend content. It directly affects user trust, workflow efficiency, and product scalability.

DrugSetu gave this team a way to move from fragmented medicine records to a structured, reusable, interoperable data layer.

That made it easier to build for growth.