Unleash clinical insights
and improve patient care
Outcomes
Leverage medical language processing
to unlock the value of unstructured medical data
CLINICAL.AI extracts important clinical information from health records with over 95% accuracy, powering an array of use cases including, but not limited to: diagnostic error prevention, clinical decision support, clinical trial patient matching, pharmacovigilance, and medical image analysis.
Features
- Aggregate:
- BUDDI.AI aggregates a patient’s longitudinal medical records in numerous formats such as:
- CCDs - Continuity of Care Documents
- Unstructured physician notes
- PDF-Images
- PDF-Text
- XML
- Word
- JSON
- HL7
- Tabular Column Extraction:
- BUDDI.AI applies proprietary vision based algorithms to assess the presence of objects similar to tables or columns/rows or boxes in medical records or lab records. BUDDI.AI then automatically parses out the datasets and maps the relationships of datasets between rows and columns. The BUDDI.AI platform then re-stitches the medical records into a machine searchable electronic format.
- Optical Character Recognition (OCR):
- BUDDI.AI applies our proprietary OCR algorithms if needed to PDF-image documents and extracts the characters from the image based documents across multiple pages.
- Post-OCR Processing:
- Most traditional OCR solutions do not yield optimal accuracy. Hence, BUDDI.AI applies post-OCR clean-up algorithms like predicting missing clinical keywords, fixing typos (powered by our proprietary clinical dictionary), and cleaning up the formatting to improve the veracity of the medical record.
- Parse & Extract Clinical Context:
- BUDDI.AI applies the industry’s best-in-class NLP + Knowledge Graph algorithms to first semi-structure the electronic document by tagging north of 1000+ clinical named entity objects. Then, BUDDI.AI applies our Clinical Contextual algorithms to weave the relationships across elements within the medical record to form a “Clinical Contextual Graph,” which better represents the context of the entire patient episode.
- De-Duplication:
- BUDDI.AI can further identify if any two documents are duplicates. If duplicates are identified, BUDDI.AI can highlight the various sections of documents where duplicates appear, and present a clinical named entity level a summary of all duplicates.
- Timeline Identification:
- BUDDI.AI can process longitudinal CCDs and/or unstructured physician notes to generate timeline representations of medicines prescribed to a given patient, procedures conducted, and/or symptoms identified or allergies across patient episodes, providers, and time.
- Summarization:
- Finally, BUDDI.AI can auto-generate a summary of a given medical record for physicians to quickly glean the most critical clinical information about a patient without having to pore through tens or hundreds of pages of documents. This is achieved thanks to our best-in-class Clinical Contextual Engine.
Learn more about CLINICAL.AI
Schedule a demo today to see medical language processing in action.
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