Savana Clinical NLP (cNLP) is a scientifically validated, AI-powered solution designed to extract and structure clinically relevant information from unstructured clinical notes. Leveraging advanced Clinical Natural Language Processing (cNLP) models exclusively trained on multilingual Electronic Health Records (EHRs), Savana NLP enables real-time extraction of structured variables that can be computationally actioned for secondary use, research, or clinical decision support.

This capability supports a wide range of applications — from enriching real-world data repositories to enhancing AI-driven analytics and dynamic clinical registries — ultimately accelerating precision medicine and improving patient care.

Key Features & Benefits

• Multilingual capability: automatically processes and structures clinical notes in multiple languages, trained on diverse and systematically curated multilingual EHR corpora
• Semantic mapping to standards: extracted variables are mapped to recognised medical terminologies, such as SNOMED CT and MedDRA, ensuring semantic consistency and interoperability
• Oncology pack (Optional): includes domain-specific ontologies and logic for extracting key oncology variables (e.g. tumour staging, histology, biomarkers)
• High accuracy on short-form texts: demonstrated ability to structure data from short clinical texts, including device outputs or EMR notes, with accuracy rates of up to 90%
• Real-time integration: supports immediate update of Next Generation Registries and other dynamic data assets with new, clinically meaningful information
• On-the-fly structuring: offers low-latency structuring of incoming clinical reports for downstream analytics in data lakes, dashboards, or AI pipelines
• Flexible input handling: compatible with a wide range of input formats and systems, enabling seamless integration across diverse healthcare environments
• Custom output formats: CSV, JSON, XML,…. Clinical standard formats like OMOP, FHIR, ….

By adopting Savana NLP, healthcare providers can streamline their data processing, improve clinical insights, and enhance decision-making capabilities, leading to more efficient and effective patient care.