Outcreating Manual Data Stewardship with LLM-powered HCP Data Deduplication for a Global Pharma Major

Our Client

The client is a global healthcare company operating across pharmaceuticals, medical devices, and consumer health, with a strong focus on improving patient outcomes at scale.

Reliable HCP master data underpins the company's engagement with physicians and institutions worldwide, making data quality a strategic priority and a prerequisite for scaling AI in life sciences.

Industry Trends

Life sciences organizations are investing in trusted, AI-ready data foundations as adoption of AI in life sciences accelerates. Leading companies are increasingly:

• Using AI and LLMs to improve entity resolution and record matching in master data management

• Building golden records as a single source of truth for HCP and customer data

• Reducing manual data stewardship through intelligent automation

• Strengthening data governance to support compliance and commercial effectiveness

• Preparing master data to power downstream analytics and AI use cases

Challenges

The company faced several HCP data deduplication challenges that increased effort and reduced confidence in its HCP data.

• Duplicate HCP records: Healthcare professional data was duplicated between source systems and the MDM platform, fragmenting the view of each professional.

• Missed matches in MDM: Many records flagged as unique by the MDM platform were in fact duplicates, allowing errors to persist undetected.

• Difficulty establishing golden records: Without reliable identification of unique records, the company struggled to generate accurate golden records.

• Heavy manual stewardship: Resolving potential duplicates depended on data stewards reviewing records one by one, a slow and resource-intensive process.

These challenges limited data trust, increased operational costs, and slowed the business teams that rely on accurate HCP information.

LTM's Solution

LTM designed an LLM-powered HCP data deduplication approach that combined efficient record grouping with semantic similarity matching.

• Intelligent bucketing: Records were grouped into buckets to minimize the number of comparisons required, keeping the process fast and scalable.

• LLM-based vectorization: LTM leveraged the language understanding capabilities of LLMs to convert HCP records into vectors, capturing meaning beyond exact text matches.

• Threshold-driven merging: The team identified similarity thresholds for merging records, enabling confident automated decisions while routing edge cases for review.

• Golden record generation: Unique records were determined and consolidated into golden records, creating a single, reliable view of each healthcare professional.

Business Benefits

The AI-driven HCP data deduplication solution delivered measurable improvements in data quality and stewardship efficiency.

• Automated deduplication of about 1,300 of 1,500 sampled records that had previously required full human stewardship.

• Identified and merged duplicates for 70% of data that the MDM platform had previously classified as unique.

• Substantially reduced manual stewardship effort, freeing data teams for higher-value governance work.

• Improved the accuracy and reliability of HCP golden records for downstream business use.

Conclusion

By applying LLM-powered semantic matching to master data management, LTM helped the pharma company turn a manual, error-prone stewardship process into a scalable, AI-infused one. The result is cleaner HCP data, lower operational effort, and a stronger foundation for AI in life sciences across commercial, medical, and analytics initiatives.

Ready to infuse AI into manual data processing for trusted HCP data management?  

Contact us at info@ltm.com