Convert patient records to organized data
Digitize patient intake forms, medical histories, lab results, and prescriptions. PaperAI handles handwritten notes, scanned forms, and faxed documents — the formats healthcare still relies on.
Healthcare organizations generate massive volumes of paper documents: patient intake forms, physician notes, lab requisitions, prescriptions, insurance authorizations, and consent forms. Despite decades of EHR adoption, many clinical workflows still start on paper — especially in smaller practices, specialty clinics, and facilities that receive records from external providers via fax or mail.
The stakes are high. Misread patient data can lead to medication errors, missed diagnoses, and compliance violations. Manual transcription is not only slow but introduces a 1–4% error rate that compounds across thousands of records. PaperAI's premium AI models are trained to handle the unique challenges of medical documents: illegible handwriting, non-standard abbreviations, and complex multi-section forms.
- Handwritten physician notes are difficult to read and transcribe
- Faxed records arrive as low-quality scans
- Manual transcription introduces errors in patient data
- Compliance requires traceable document history
- Multiple document formats from different providers and facilities
Collect patient documents
Scan paper intake forms, receive faxed records, or upload digital copies. PaperAI handles scanned, photographed, and faxed documents with equal capability.
Set up your medical records settings
Tell PaperAI what details to pull out: patient demographics, diagnoses, medications, allergies, and visit details. Use premium AI models for handwriting-heavy documents.
Process and extract
PaperAI converts documents to clean, readable text and simultaneously pulls out key details. Accuracy scores highlight anything the AI is less sure about so you can double-check.
Clinical review
Medical staff review extracted data in the side-by-side interface. Verify patient identifiers, medication dosages, and diagnosis codes against the original document.
Integrate with EHR
Export organized data to your electronic health record system. Use JSON output for direct API integration with Epic, Cerner, or other EHR platforms.
PaperAI automatically pulls out these details from your documents, organized and ready to use:
| Field | Type | Example |
|---|---|---|
| Patient Name | string | Maria L. Chen |
| Date of Birth | date | 1987-03-22 |
| Medical Record Number | string | MRN-004829 |
| Primary Diagnosis | string | Type 2 Diabetes Mellitus |
| Medications | array | Metformin 500mg, Lisinopril 10mg |
| Allergies | array | Penicillin, Sulfa drugs |
| Provider Name | string | Dr. James Whitfield |
| Visit Date | date | 2024-10-08 |
0% faster intake processing
Digitize patient forms in seconds instead of manually transcribing each field into the EHR system.
Near-zero transcription errors
AI extraction with human verification catches errors that manual-only transcription misses.
Full audit trail
Every document version is tracked — critical for HIPAA compliance and clinical documentation integrity.
Every document is converted to clean, readable text and organized data you can use in other tools. Here's an example of what PaperAI produces for a typical medical records digitization document:
Sample Output for: Medical Records Digitization
## Patient Intake — Maria L. Chen
**MRN:** MRN-004829
**DOB:** March 22, 1987
**Visit Date:** October 8, 2024
**Provider:** Dr. James Whitfield
### Diagnoses
- Type 2 Diabetes Mellitus (E11.9)
- Essential Hypertension (I10)
### Current Medications
| Medication | Dosage | Frequency |
|-----------|--------|----------|
| Metformin | 500 mg | 2x daily |
| Lisinopril | 10 mg | 1x daily |
### Allergies
- Penicillin — rash
- Sulfa drugs — anaphylaxisHealthcare administrators, medical records teams, clinical research
Further Reading
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Start automating medical records digitization with PaperAI.
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