SIFT - Semantic Interpretation of Free Text
Meaningful clinical information is trapped as unstructured text in various clinical documents and physician notes. SIFT unlocks hidden data by identifying and returning the coded, actionable information you need.
Through advanced normalisation, mapping, and clinical natural language processing capabilities, SIFT returns coded information by analysing unstructured text to empower better data exchange, decision support, population health, and analytics.
What does SIFT do?
- Analyses unstructured text, targets and identifies clinical information, and returns structured, actionable data.
- Recognizes full, partial, or synonymous terms and matches them to targeted terminologies to impact true semantic meaning.
- Uses advanced text analysis and targeted, rules-based matching to go beyond data extraction to meaningful interpretation and analytics based on the structure of the content itself.
- Unlocks valuable insights, expanding the actionable data available to healthcare applications and providers.
- Fast enough to dynamically process provider notes as they are entered and scalable enough to process a full repository of documents.
Use Cases for SIFT
- Population Health/Data Analytics
- Medication Reconciliation
- Computer Assisted Coding
- Clinical Documentation Improvement
- Clinical Decision Support
- Clinical Trial Recruitment
- Case Management