Affinda's AI medical record processing platform can capture data from any field on a patient record. Standard data fields include:

Patient & Provider Information

• Patient Name / ID / Date of Birth: Full patient identification including legal name, medical record number and DOB.
• Patient Address / Contact Details: Residential address, phone number and emergency contact information.
• Insurance Information: Policy number, insurance provider, group ID and coverage details.
• Provider Name / NPI / Specialty: Treating physician or healthcare provider's name, National Provider Identifier and medical specialty.
• Facility / Department: Hospital name, clinic location or specific department where care was provided.

Clinical Information

• Diagnoses / ICD Codes: Primary and secondary diagnoses with corresponding diagnostic codes.
• Treatments / Procedures / CPT Codes: Medical procedures performed, treatments administered and associated procedural codes.
• Medications / Prescriptions: Drug names, dosages, frequencies and administration instructions.
• Lab Results / Test Values: Laboratory findings, diagnostic test results and reference ranges.
• Vital Signs: Blood pressure, temperature, heart rate and other recorded vital measurements.
• Allergies / Adverse Reactions: Known patient allergies and documented adverse reactions.
• Clinical Notes / Provider Observations: Free-text clinical narratives and provider documentation.

Administrative & Reference Data

• Visit / Encounter Date: Date of service, admission date and discharge date.
• Reference Numbers / Case ID: Encounter numbers, authorization codes and claim references.
• Document Date / Version: Record creation date and version control information.

Need custom fields or specific clinical templates? Simply define them in the UI or describe them in natural language and Affinda will extract and convert them automatically.

Our AI agents can process any file type. Supported file types include PDF, JPG/JPEG, PNG, TIFF, DOC/DOCX (Word), XLSX (Excel), HTML and TXT/CSV.

Affinda's AI agents read, extract and validate medical record data. Our platform is intuitive to set up and easily integrates into your systems. Simply:

1. Upload your medical records via drag-and-drop, email or API.
2. Configure business logic to match your healthcare organization's rules and workflows.
3. Let Affinda check and validate extracted data against your business logic and EHR, compliance or insurance systems to ensure accuracy.
4. Access structured medical record data directly within your systems.

The result? Faster, more accurate medical record processing with minimal manual input.

Yes. Our AI medical record processing platform combines OCR with retrieval augmented generation (RAG), LLMs, agentic workflows, proprietary reading order algorithms and more, to automatically detect and process data from scanned or photographed invoices (PDF, JPG, PNG). Even low-quality images are automatically enhanced for reliable data capture and processing.

Our AI medical record processing platform can automatically detect and process medical records in 50+ languages, including multilingual medical records containing more than one language. Supported languages include: Afrikaans, Albanian, Amharic, Arabic (Standard, Egyptian, Sudanese, Algerian, Moroccan, Levantine), Bahasa Indonesian, Bengali, Bhojpuri, Bulgarian, Burmese, Chinese (Mandarin [PRC/Taiwan], Cantonese, Wu, Min Nan, Jinyu, Xiang, Hakka), Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Italian, Japanese, Javanese, Kannada, Korean, Latvian, Lingala, Lithuanian, Macedonian, Malayalam, Marathi, Nepali, Norwegian, Odia, Persian (Farsi), Polish, Portuguese, Punjabi (Western and Eastern), Romanian, Russian, Slovak, Slovenian, Somali, Spanish, Swahili, Tagalog, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Yoruba.

Yes. Our medical record data extraction platform integrates with 400+ downstream systems, including major EHR, compliance and insurance platforms. You can build no-code integrations using our AI integrations agent, or use our APIs to create custom connections – whatever fits your healthcare workflow best.

AI medical record processing helps automate and optimize healthcare workflows across departments such as:

• Health Information Management: Accelerate patient record entry and reduce manual data capture by extracting clinical details from any format – including multi-page records and grouped encounters. Business-rule validation catches missing fields, incorrect diagnoses codes and inconsistent treatment documentation.
• Compliance Teams: Use self-learning AI that adapts to evolving clinical documentation standards, simplifying audit preparation and ensuring patient records meet regulatory requirements with consistent accuracy.
• Insurance / Claims Processing: Feed structured medical record data directly into your insurance systems to improve claims validation, expedite reimbursements and reduce processing backlogs.

A medical record optical character recognition (OCR) data extractor scans and accurately captures patient and clinical information from medical records, regardless of format or language. Upload patient record documents in PDF, JPG or PNG formats. The OCR technology first converts the documents into a text layer, ensuring all clinical data (even from scans, photos or handwritten notes) is captured. Affinda's AI models then identify and extract key fields such as patient details, provider information, diagnoses, treatments, dates and reference numbers, transforming them into structured, usable data.

This automation eliminates manual data entry, reduces errors and speeds up healthcare workflows. Once the medical record data is captured and structured, our AI medical record processing pipeline can apply machine validation and post-processing to enable seamless integration with EHR, compliance and insurance systems.

Healthcare organizations across hospitals, clinics and compliance teams rely on Affinda's AI for their health information management, patient record entry and regulatory reporting processes, streamlining workflows and improving overall accuracy.