Solution
Luma Analytics designed and implemented an AI-driven data pipeline powered by Large Language Models (LLMs) to extract and structure information from unstructured consultation notes. The solution combined domain-specific natural language processing (NLP) with secure, cloud-based architecture to ensure scalability, compliance, and performance. This approach enabled the transformation of complex text-based data into structured datasets that could be easily queried and analysed to identify patterns and trends in key clinical measures.
Outcomes
For the first time, the healthcare provider could systematically analyse its doctors’ notes to generate reliable, data-driven insights on care quality and patient outcomes. The structured data revealed measurable improvements in health results and demonstrated the effectiveness of the organisation’s care model. By automating data capture, clinicians were freed from completing manual surveys, spending less time on administration and more time delivering care to patients.
Featured Results
- AI-powered pipeline transforming unstructured consultation data into insights
- Large Language Models (LLMs) applied for domain-specific clinical text analysis
- Secure, scalable, and compliant cloud architecture
- Measurable improvements in patient outcomes identified through structured data
- Reduced clinician admin time and improved operational efficiency
