CLINICAL.AI is an advanced platform that utilizes medical language processing (MLP) to unlock the value of unstructured medical data, transforming it into actionable clinical insights. It extracts critical clinical information from diverse health records with over 95% accuracy, supporting a wide array of healthcare use cases. These include preventing diagnostic errors, providing robust clinical decision support, facilitating clinical trial patient matching, enhancing pharmacovigilance, and aiding medical image analysis. The platform also aims to significantly reduce coding costs and improve claims accuracy by streamlining the processing of medical documentation. At its core, BUDDI.AI, the underlying engine, aggregates longitudinal medical records from numerous formats such as CCDs, unstructured physician notes, PDF-images, PDF-text, XML, Word, JSON, and HL7. It employs proprietary vision-based algorithms for tabular column extraction, OCR for image-based documents, and sophisticated post-OCR clean-up to ensure data veracity. Furthermore, BUDDI.AI applies industry-leading NLP and Knowledge Graph algorithms to parse and extract deep clinical context, identifying over 1000 clinical named entity objects and weaving relationships into a "Clinical Contextual Graph" for a comprehensive patient episode view. Additional capabilities include identifying and summarizing duplicate documents, generating timeline representations of patient events, and auto-generating concise summaries of medical records for quick physician review.

Features & Benefits

• Aggregate Medical Records
  • BUDDI.AI aggregates a patient’s longitudinal medical records from various formats.
  • CCDs - Continuity of Care Documents
  • Unstructured physician notes
  • PDF-Images
  • PDF-Text
  • XML
  • Word
  • JSON
  • HL7
• Tabular Column Extraction
  • Applies proprietary vision-based algorithms to identify and parse tables, columns, and rows in medical and lab records, re-stitching them into a machine-searchable electronic format.
• Optical Character Recognition (OCR)
  • Utilizes proprietary OCR algorithms to accurately extract characters from multi-page PDF-image documents.
• Post-OCR Processing
  • Applies advanced clean-up algorithms to improve accuracy by predicting missing clinical keywords, fixing typos using a proprietary clinical dictionary, and cleaning up formatting.
• Parse & Extract Clinical Context
  • Leverages best-in-class NLP and Knowledge Graph algorithms to semi-structure documents by tagging over 1000 clinical named entity objects and forming a "Clinical Contextual Graph" representing the entire patient episode.
• De-Duplication
  • Identifies duplicate documents, highlights duplicate sections, and provides a clinical named entity level summary of all duplicates.
• Timeline Identification
  • Processes longitudinal CCDs and unstructured physician notes to generate timeline representations of medications, procedures, symptoms, or allergies across patient episodes, providers, and time.
• Summarization
  • Auto-generates concise summaries of medical records, enabling physicians to quickly glean critical clinical information without reviewing extensive documents, powered by the Clinical Contextual Engine.