Parse patient records, lab reports, discharge summaries, and clinical documents into structured data—any format, any source—without manual review.
Upload any document — PDF, scan, or photo — and get structured data back immediately. No setup, no templates, no waiting.
“We receive patient records from over 200 referring providers, each with different document formats. Automated parsing gives us structured data within minutes of receiving a referral package.”
“Lab report processing was consuming four hours per day for our clinical staff. The parser handles reports from every reference lab we work with, no configuration needed.”
“The medication extraction from discharge summaries has been transformative for our medication reconciliation process. Catching discrepancies that were previously missed during manual review.”
Audited controls over a sustained period, not a point-in-time check.
Bank-grade encryption at rest and TLS 1.2+ in transit.
Documents deleted within 24 hours. No copies retained.
Drag and drop files, connect a cloud drive, or set up email auto-forwarding. Any file format works—PDF, JPEG, PNG, TIFF, or digital documents.
The AI identifies fields by context and meaning, not fixed coordinates. Names, dates, amounts, and custom fields are extracted automatically.
Get structured output in Excel, Google Sheets, CSV, or JSON. Use the REST API for direct integration into your systems.
Medical documents contain critical patient information locked in unstructured formats. Discharge summaries arrive as narrative text. Lab reports use proprietary layouts that vary by laboratory. Progress notes mix free-text clinical observations with structured vital signs and medication lists. Medical document parsing transforms this heterogeneous collection of formats into consistent, structured data that can be analyzed, compared, and integrated into clinical systems.
The parsing challenge in healthcare goes beyond simple text extraction. A medical document parser must understand clinical context to correctly identify patient identifiers, separate medication names from dosages, distinguish between current and historical diagnoses, and recognize lab values with their reference ranges. This requires domain-specific AI that has been trained on medical terminology, abbreviations, and document conventions that are unique to healthcare.
Interoperability is the driving force behind medical document parsing adoption. When a specialist receives records from a primary care physician, those records arrive as PDF files or faxes rather than structured HL7 or FHIR messages. Lido bridges this gap by parsing incoming medical documents into structured data that can be mapped to standard healthcare data formats, reducing the manual work required for care coordination across providers and systems.
Organizations evaluating medical document parsing solutions should consider the breadth of document types supported, accuracy on clinical terminology, ability to handle narrative text alongside structured forms, and compliance certifications. Lido provides HIPAA-eligible processing with SOC 2 Type 2 compliance and handles the full spectrum of medical documents from structured lab reports to unstructured clinical narratives.
Medical document parsing handles patient records, lab reports, discharge summaries, progress notes, radiology reports, pathology reports, medication lists, and referral letters. The AI adapts to each document type automatically, extracting the relevant clinical and administrative data fields.
AI-powered parsing reads lab reports contextually, identifying test names, values, units, and reference ranges regardless of which laboratory produced the report. This eliminates the need for lab-specific templates and handles the format variation that exists across thousands of reference laboratories.
Yes. The AI identifies medication names, dosages, frequencies, and routes of administration from both structured medication lists and narrative clinical text. This is valuable for medication reconciliation and pharmacy integration workflows.
AI-powered parsing achieves 95 to 99 percent accuracy on structured document types like lab reports and achieves 90 to 95 percent on unstructured narrative text. Confidence scoring identifies uncertain extractions for clinical review, ensuring that critical patient data is verified before use.
Parsed data can be exported to Excel, Google Sheets, CSV, or JSON. The REST API returns structured JSON that can be mapped to HL7 or FHIR message formats for integration with EHR systems.
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Built on Lido’s OCR engine
Built on Lido’s OCR engine
Built on Lido’s OCR engine
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