Healthcare Technology
Clinical NLP Platform for a HealthTech Company
Client: UK HealthTech Company
A high-growth HealthTech company needed to automate the extraction of structured data from unstructured clinical notes — thousands of documents processed manually each week.
The Challenge
The client processed thousands of clinical documents weekly, with trained reviewers manually extracting key data points from unstructured notes. This was slow, expensive, and inconsistent — different reviewers extracted different fields, and error rates increased with volume. The company needed an automated pipeline that could handle clinical terminology, abbreviations, and the inherent ambiguity of medical language, while meeting strict GDPR and data residency requirements.
The Solution
We designed and built an NLP pipeline purpose-built for clinical text. Rather than fine-tuning a general-purpose language model, we developed a domain-specific approach that combined clinical terminology databases with contextual extraction models. The system was deployed on UK-hosted infrastructure to meet data residency requirements, with a human-review interface for low-confidence extractions.
Our Approach
- 1Conducted a detailed analysis of clinical note formats, identifying key entity types, common abbreviations, and domain-specific language patterns
- 2Built a clinical NLP pipeline combining named entity recognition, relation extraction, and terminology normalisation against standard medical ontologies
- 3Developed a confidence scoring system that routes low-confidence extractions to human reviewers, maintaining quality while maximising automation
- 4Deployed on UK-hosted infrastructure with encryption at rest and in transit, meeting GDPR and data residency requirements
- 5Created a feedback loop where reviewer corrections improved model accuracy over time
- 6Built comprehensive audit logging for regulatory compliance, tracking every extraction decision and human override
Outcomes
- Automated the majority of clinical data extraction, reducing manual review burden significantly
- Processing time per document reduced from minutes to seconds for automated extractions
- Consistent extraction quality across all document types, eliminating inter-reviewer variability
- Full GDPR compliance with UK data residency, encryption, and audit trail
- Continuous improvement: model accuracy increased measurably in the first three months through the reviewer feedback loop
Technologies & Capabilities
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