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FAQ

The DocKnow Knowledge Base.

Explore the technology, workflows, integrations, security, and real-world use cases behind the DocKnow intelligent document processing and intake platform.

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HIPAA Compliant

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SOC 2 Type II

FAQ

The DocKnow Knowledge Base.

Explore the technology, workflows, integrations, security, and real-world use cases behind the DocKnow intelligent document processing and intake platform.

Shield checkmark icon

HIPAA Compliant

Shield checkmark icon

SOC 2 Type II

60%

less rework for ops teams

84%

reduction in intake errors

99.7%

data validation accuracy

60%

less rework for ops teams

84%

reduction in intake errors

99.7%

data validation accuracy

Intelligent intake goes beyond simply reading a document. DocKnow uses AI-powered extraction, validation rules, confidence scoring, and external system checks to identify the data that matters, flag missing or conflicting values, and transform unstructured information into structured, workflow-ready data.

The goal is not just to digitize documents, but to make the data inside them accurate, usable, and ready for downstream systems.

Traditional OCR converts images or scanned documents into machine-readable text. DocKnow goes further by identifying the specific data that matters, understanding where it belongs, validating it, and structuring it for downstream systems.

In other words, OCR helps you read a document. DocKnow helps you do something with the data inside it.

No. DocKnow fills gaps in an existing technology stack.

Many core healthcare and life sciences systems are excellent at managing structured data but less suited to dealing with raw, unstructured information and variable inputs. They’re also typically not designed to validate incoming data for completeness, consistency, or accuracy before it enters the system of record.

DocKnow handles that surrounding work while leaving the organization’s existing systems of record in place.

DocKnow uses Onymos’ No-Data Architecture. By default, sensitive data remains within the customer’s own cloud or on-prem environment rather than being stored by Onymos.

This approach is particularly valuable for healthcare organizations that need greater control over protected or sensitive information.

Most platforms and services capture and store their customers’ data. It’s lucrative (for the vendors). But it’s risky (for the customers). In healthcare, over 55% of data breaches happen through third-party vendors.

That’s why we do things differently. Using No-Data Architecture means fewer points of failure, less data exposure overall, and simpler compliance. And in a post-AI world, where data is more valuable than ever, this isn’t just a technical choice.

It’s a strategic one.

Yes. Different document types, departments, or use cases can have different extraction and validation requirements. DocKnow allows those rules to be configured at the workflow level rather than forcing every process into the same logic.

DocKnow combines two approaches to balance accuracy and flexibility. For high-volume or business-critical document types (such as a test requisition form), models are trained specifically on those formats to maximize extraction accuracy. For supporting documents and less predictable inputs, DocKnow can use more dynamic extraction methods that adapt to variation without requiring a dedicated model for every layout.

That hybrid approach gives organizations the precision of document-specific training where it matters most, while still supporting the long tail of variable documents that show up in real-world workflows.

DocKnow helps reduce denials by strengthening the front end of the revenue cycle. It can validate intake data, apply payer-specific rules, check for missing documentation, and route exceptions for review so cleaner, more complete information reaches the billing system in the first place.

Large language models can be powerful components of a document-processing workflow, but production-scale intake usually requires much more than prompting a model.

The real challenge is getting consistent, reliable results across thousands of documents with different layouts, formats, handwriting, terminology, and edge cases. Domain-trained models outperform general-purpose models on industry-specific extraction tasks, especially where terminology, document structure, and edge cases matter.

But beyond raw processing, organizations still need the infrastructure around the model. DocKnow brings those necessary capabilities together in a single, specialized platform.

If a workflow is already fully structured, highly standardized, and requires little or no manual document handling, DocKnow may not add much value.

It is most useful when important operational data arrives in documents, PDFs, images, forms, or other unstructured formats and must be extracted, checked, or enriched before it can be trusted downstream.

VantaDx logo

Building a next-gen intake system.

Advanced diagnostics require advanced operations to match. As Vanta Diagnostics grew, that balance started to slip, and intake was the first place it showed.

Albertsons logo

Building a next-gen intake system.

Advanced diagnostics require advanced operations to match. As Vanta Diagnostics grew, that balance started to slip, and intake was the first place it showed.

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