Top 5 OCR tools to automate healthcare documents

Key takeaways of this article

1 – OCR in healthcare

This section explains in a few minutes how AI-powered OCR turns prescriptions, reports, claim forms, and scanned records into structured data that can flow into your EHR or practice management system. Reading time: ~3 minutes.

2 – The 5 leading solutions on the market

We present five OCR / IDP solutions that make sense in healthcare contexts (including Koncile, ABBYY, Amazon Textract, Docsumo, DocuWare), with their strengths and typical users. Reading time: ~4 minutes.

3 – Comparison and how to choose

We compare solutions based on healthcare specialization, integration, data residency, and ease of deployment, then give a few decision scenarios. Reading time: ~3 minutes.

4 – The next wave of AI solutions

We open on what’s coming next: ambient listening assistants, AI in radiology, triage, multimodal models, and end-to-end automation—plus a closer look at two concrete solutions (Dragon Copilot and Nabla Copilot). Reading time: ~4 minutes.

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OCR in healthcare

Healthcare organizations handle a massive volume of documents: prescriptions, discharge summaries, imaging reports, lab results, intake forms, insurance documents, billing records… A large part of this information still arrives on paper or as “image-only” PDFs, which are hard to use in any clinical or billing system.

OCR (Optical Character Recognition), combined with AI, can read these documents, extract key data points (patient, date, prescriber, exam, diagnosis, procedure, codes) and turn them into structured data that can be injected into an EHR, LIS, RIS, or billing platform. Companies like Koncile specialize specifically in sensitive healthcare documents such as prescriptions and complex medical paperwork.

A dedicated page, OCR AI for prescriptions, shows how prescription OCR can automate the capture of patient, doctor, and drug information for practices, pharmacies, or labs.

What it enables

In practical terms, OCR in healthcare is about transforming raw documents (handwritten prescriptions, scanned reports, claim forms) into structured data that software can actually work with. Where a human used to read, interpret, and retype everything, an AI OCR like Koncile reads the document, extracts the relevant fields, and returns clean, structured data ready to be pushed into the patient record or billing tools.

In everyday workflows, this makes it possible to:

• automatically detect patient, prescriber, date, and drugs on a prescription
• extract procedures, amounts, and codes required for billing from claim forms
• auto-file documents into the right patient chart or work queue (radiology, lab, admissions)
• build quality and activity dashboards from documents that were previously “silent” data

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What it implies

Rolling out a serious OCR strategy in a hospital or clinic network is not as simple as “plugging in a recognition engine”. It has a few implications:

• Rethinking certain workflows: when exactly is the document scanned, where do the files land, who validates extractions, how are exceptions handled?
• Some level of document standardization: the more consistent your templates (prescription layouts, intake forms, standard letters), the more accurate and stable your OCR will be.
• A validation loop: for sensitive documents, it’s healthy to keep human validation for critical fields like patient identity, drugs, dosage, or billable amounts.
• Quality measurement: track recognition rate, reject rate, processing time, and error patterns. OCR should become a controlled process, not a magic black box.

The ideal approach is to start with a focused scope (for example, prescriptions only, or one key type of report), stabilize the workflow and KPIs, then expand to additional documents.

What you need to ensure reliability and security

In healthcare, the bar is not only about accuracy. You also need to make sure the solution respects regulation and data security best practices:

• Compliance and data protection: where data is stored, legal basis for processing, retention periods, and patient rights.
• Secure hosting: encryption at rest and in transit, logging, access management, support for the right kind of healthcare-compliant environments.
• Traceability: who submitted which document, when, and what data was extracted or corrected.
• Built-in quality controls: confidence scores per field, the ability to force validation on sensitive fields or low-confidence extractions, and a history of corrections.
• Clean integration with existing systems: authentication, permissions, auditability, error handling (unreadable scans, duplicates, incomplete documents).

To learn more about how a specialized healthcare OCR can combine accuracy, customizable fields, and API/SDK integration without compromising on security, see: Healthcare OCR extraction use case.

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The 5 leading and reliable solutions on the market

There are dozens of OCR and IDP products out there. For a healthcare provider or a health-tech vendor, the real question is not “who exists?”, but “which tool actually fits my documents, my scope, and my constraints?”.

Here are five representative solutions that are reliable and already used on sensitive documents, including healthcare.

Koncile

Koncile is an AI OCR solution specialized in extracting data from complex documents, with a strong focus on healthcare. It lets you upload prescriptions, claim forms, and other medical documents as PDFs or images, and returns a table or JSON payload ready to consume.

Key points:

• 🔍 Healthcare specialization: ready-made models for prescriptions, claim forms, and medical reports, with customizable fields.
• 📦 Supported formats: PDFs, scanned images, photos, with the ability to handle handwriting and varied layouts.
• 🧩 Integration: available as an API and SDK, easy to plug into existing healthcare software or workflows.

Docsumo

Docsumo is a general-purpose Intelligent Document Processing (IDP) platform, used to extract data from forms, insurance documents, invoices, and more. In healthcare, it shines on the administrative and insurance side.

Key points:

• 📑 Very strong on structured forms (claims, administrative files, insurance documents).
• 🧮 A good fit for health insurers, TPAs, and back-office teams managing large volumes of paperwork.

ABBYY

ABBYY is one of the historical leaders in OCR and IDP, widely used in regulated industries. Its platform can cover a broad range of documents, including healthcare-related ones.

Key points:

• 🏛️ A very complete, mature IDP platform that adapts to many document types.
• 🧱 A good choice for groups that want a transversal document-processing backbone across healthcare, finance, and operations.

Amazon Textract

Amazon Textract is AWS’s OCR/IDP service. It extracts text, tables, and key-value pairs from scanned documents and integrates seamlessly into a cloud-native stack.

Key points:

• ☁️ Ideal for teams already heavily invested in AWS that want to build their own ingestion and analytics pipelines.
• 📊 Fits projects where you process high volumes and want to combine OCR with analytics and other AWS services.

DocuWare

DocuWare is primarily a document management (DMS/ECM) solution with built-in OCR capabilities. In healthcare, it’s relevant for digitizing records and setting up document-centric workflows.

Key points:

• 🗂️ Combines document management, workflows, and OCR in one platform.
• 🏥 A good fit for organizations that primarily need to organize, archive, and retrieve healthcare documents.

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Comparing the solutions

These five solutions don’t play in exactly the same category. To oversimplify:

• Koncile: healthcare-focused AI OCR, strong on prescriptions and medical documents, API-first.
• Docsumo: general-purpose IDP, excellent for administrative and insurance workflows around healthcare.
• ABBYY: large, enterprise-grade IDP platform, great if you want to cover many document types.
• Amazon Textract: highly scalable cloud service, ideal in an AWS-heavy stack.
• DocuWare: DMS + OCR, solid if your main challenge is document management.

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Best practice: start from your real documents (10 to 50 examples) and test each solution’s accuracy, integration effort, error handling, and field customization.

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Which one should you choose, and why?

There is no “absolute best OCR”, only tools that are more or less aligned with your situation.

A few typical scenarios:

• Medical practice, imaging center, lab
  You mainly deal with prescriptions, reports, claim forms, and patient forms.
  → A specialized solution like Koncile, with ready-made healthcare models and strong handwriting handling, is usually the best compromise.
• Hospital group / health insurer / large health organization
  You also process contracts, insurance documents, vendor invoices, and multi-country forms.
  → A broader IDP platform like ABBYY or Docsumo can make sense, potentially combined with a more specialized healthcare OCR.
• Cloud-native, data-driven organization
  You are heavily on AWS and have an engineering/data team.
  → Amazon Textract can be a powerful building block inside a custom pipeline.
• Organization primarily looking for a DMS
  Your main challenge is to organize, archive, and retrieve documents, with OCR as a built-in feature.
  → DocuWare is a good option if you want DMS, workflows, and OCR in a single platform.

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What’s coming next in healthcare automation

Intelligent OCR is no longer “the future”—it’s already live in many hospitals and clinics. But it’s also laying the groundwork for a much broader wave of automation in healthcare.

On top of this first layer of “document reading”, a new set of AI tools is emerging that will change how clinical and administrative work is done.

The main types of upcoming solutions

1. Ambient listening AI assistants
   Tools that listen to the consultation and automatically generate a clinical note for the physician to review and approve.
2. Radiology-focused AI
   Models that help detect anomalies, pre-read images, and prioritize which exams need attention first.
3. AI for triage and prioritization
   Systems that help emergency departments and hospital units identify critical cases faster based on patient data.
4. Multimodal models (OCR + voice + image)
   Solutions that can combine scanned documents, medical conversations, and imaging data to get a more complete view of the patient case.
5. Software automation (RPA + IDP)
   Orchestrations that chain all these bricks—OCR, AI assistants, clinical systems—to minimize manual re-entry and trigger the right actions automatically.

Closer look at 2 ambient listening assistants

To make this next wave more concrete, let’s look at two “ambient” AI assistants already in use in hospitals and clinics: Dragon Copilot and Nabla Copilot.

Dragon Copilot

Dragon Copilot is a clinical AI assistant built on top of medical dictation and ambient listening technology. It listens to the consultation, transcribes the conversation, and automatically generates a structured note directly in the patient record.

In practice:

• the physician conducts the consultation normally, without changing how they speak
• the assistant “listens” in the background
• a specialty-specific clinical note is proposed for review and approval
• some tasks can be automated (letters, summaries, sometimes prescription drafts, etc.)

Nabla Copilot

Nabla Copilot is a European ambient AI assistant designed from the ground up to reduce clinicians’ paperwork. It listens to the consultation and generates a structured note in a few seconds, tailored to the specialty (family medicine, pediatrics, cardiology, etc.).

Nabla focuses on:

• note quality (clear structure, relevant fields, correct codes)
• smooth integration with existing EHRs and practice tools
• a privacy-by-design posture aligned with European expectations

Quick comparison of the two assistants

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These “ambient” assistants don’t replace OCR—they extend it. Tomorrow, patient records will be built both from scanned documents (processed by AI OCR solutions like Koncile) and from oral consultations (captured by these AI copilots), all tied together by end-to-end automation workflows.

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Conclusion

Healthcare document automation is no longer just about “scanning PDFs”. AI-powered OCR now makes it possible to move toward structured, measurable data that flows directly into patient records, billing systems, and analytics.

The five tools we’ve covered—Koncile, Docsumo, ABBYY, Amazon Textract, DocuWare—cover different needs, from small practices to large health systems and insurers. The right choice depends on your documents, your tech stack, and how much customization you want.

What’s emerging is a continuum:

• at the entry point, paper or scanned documents processed by AI OCR
• in parallel, ambient listening assistants turning spoken clinical encounters into structured notes
• above, automation workflows (RPA + IDP) orchestrating everything

These ambient assistants don’t replace OCR—they complement it. In the near future, patient records will be fed both by scanned documents (handled by OCR engines like Koncile) and by oral consultations (captured by AI copilots), all orchestrated by end-to-end automation.

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FAQ