Intelligent document and lab ingestion
Turn faxed records, scanned PDFs, and phone snapshots of lab reports into structured, EHR-ready data — without manual transcription.
This is an automated, high-throughput ingestion and extraction pipeline. Optical character recognition and natural language processing parse unstructured medical artifacts — faxed paper records, scanned PDFs, and smartphone snapshots of lab reports — into clean, structured data that can land in the record.
Staff and patients can submit files through a secure portal or a mobile upload. The pipeline classifies the document, extracts results, maps them to standard vocabularies, and writes into the EHR through FHIR or HL7 connectors.
Who this is for
Chief information officers, practice managers, and lead physicians at mid-size clinics and multi-specialty outpatient networks.
When it is needed
Administrative burnout from manual data entry, transcription errors on external records, and legacy systems that cannot ingest image-based or unstructured documentation.
Technical workflow
Ingestion layer
Multi-channel gateways for PDFs, TIFFs, and camera snapshots through a secure web portal or mobile app extension.
Extraction engine
Computer-vision and NLP pipelines that detect page boundaries, correct skew, extract text, and classify document types such as lipid panels, pathology reports, and discharge summaries.
Normalization and validation
Extracted values are mapped to LOINC for labs and SNOMED or ICD-10 for conditions. Out-of-range or unmatched values are flagged for human review.
Integration layer
FHIR and HL7 connectors deposit structured fields into the target EHR — including platforms such as athenahealth or eClinicalWorks — without re-keying.
Example use
Automated multi-format lab ingestion for an outpatient network
For a multi-specialty outpatient network, the team deployed an OCR and computer-vision pipeline that ingested non-standard PDF lab sheets and mobile image snapshots. The system classified lab panels, extracted numeric and text results, checked them against reference ranges, and mapped them into structured clinical fields so the transcription backlog did not have to be worked by hand.
Capabilities used: Laboratory results, diagnostic reports, PDF and snapshot parsing, document templates.
See if this pathway fits
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