Make accessioning the fastest part of your lab.

Data entry at intake sets the pace for your entire lab. DocKnow hyperautomates it. Eliminate errors, boost operational efficiency, and accelerate the revenue cycle.

Lab technician reviewing requisition paperwork at a lab bench
Processing time per TRF (pre-DocKnow)
14m
Processing time per TRF (post-DocKnow)
2m 52s
Faster document processing
5x

* Average across all Onymos customers

/ The challenge /

Every sample starts as paperwork.

Test requisition forms / physician notes / insurance cards / pathology reports / more

  • 01

    Multiple, complex doc types

    Dozens of forms across paper and digital, combining structured and unstructured data.

  • 02

    Manual entry doesn’t scale

    Volume spikes turn into backlogs, overtime, and delayed results.

  • 03

    Errors travel downstream

    A mistyped ID or missing code means rejected claims and rework.

/ How it works /

From intake to clean claim in four steps.

  • 01 Dot-matrix illustration of scattered document data condensing into a clean, structured block

    Extract and validate

    Automatically extract sensitive data from TRFs and supporting documents, no matter where they enter your workflow.

  • 02 Dot-matrix illustration of data fields with gaps bracketed and filled in purple

    Fix and enrich

    Fix and enrich data at the source. Detect and resolve gaps at intake, increasing throughput while cutting QA overhead.

  • 03 Dot-matrix line chart with a purple trend line rising above a flat baseline

    Track trends

    Track trends, performance, and variability across your own team and ordering providers.

  • 04 Dot-matrix illustration of an irregular data shape restructured into an orderly grid, ready to hand off

    Structure and hand off

    Use configurable business rules to structure data and enable auditable handoffs to LIMS, billing, and connected systems.

Purple dot-matrix portrait of Stephen Fairclough, Chief Technology Officer of Dinamo Biotechnologies

Beyond the accuracy of DocKnow, the traceability of their solution differentiates them from other players in the space.

Stephen Fairclough Chief Technology Officer, Dinamo Biotechnologies

/ Capabilities /

Built for the accessioning bench.

Everything needed to move a requisition from inbox to billing.

  • Handwriting recognition

    Reads typed and handwritten structured and unstructured data.

  • System integration

    HL7, FHIR, and API connections to the systems you already run.

  • Rules-based validation

    Normalize data and govern system and user behavior with configurable business rules.

  • Human-in-the-loop review

    Use confidence thresholds to flag exceptions and prioritize critical data fields.

  • Data reconciliation

    Cross-check extracted data against existing records and supporting documents.

  • CLIA/CAP readiness

    Encryption in transit and at rest, with role-based access and auditable changelogs.

/ FAQ /

Questions, answered.

Still have questions?

Talk to our team
01

Will we need to change our accessioning workflows to use DocKnow?

Workflows

DocKnow is designed to accommodate a wide range of laboratory accessioning workflows. Our team works directly with yours to understand your requirements and configure the solution accordingly.

That doesn’t mean nothing will change. Moving from manual accessioning to an automated, human-in-the-loop system can significantly alter how your team works. But that’s also the point. You’ll reduce manual effort, eliminate unnecessary steps, and free your team to focus on work that requires their expertise. Our goal is to make that transition as smooth as possible, adapting DocKnow to your operations rather than forcing your operations to adapt to DocKnow.

02

How does DocKnow handle errors or uncertainty during document processing?

Accuracy

DocKnow uses confidence scoring to identify information that may require additional review. When confidence falls below a configurable threshold, the system can flag the information for human verification. Laboratories can also designate critical data fields that must always be reviewed, regardless of confidence score.

This gives your team control over where and when human oversight is required, helping prevent uncertain or inaccurate information from moving downstream without verification.

03

What types of documents can DocKnow process?

Documents

DocKnow processes test requisition forms (TRFs), medical records, lab reports, insurance cards, and other clinical documents. They can be typed or handwritten, single pages or multi-page packets, and arrive by upload, fax, email, SFTP, or API.

04

What makes DocKnow “lab-native”?

Lab-native

Unlike general-purpose document processing platforms, DocKnow is purpose-built for the complexity of laboratory operations.

At its core is Synapse, our domain-specific intelligence layer, developed through experience processing more than one million completed forms across 500+ variations. Synapse augments AI-generated results with laboratory-specific knowledge and programmatic logic to help ensure extracted information is ready for downstream workflows.

That specialization extends to built-in capabilities, including integrations with ICD and CPT code sets for interpreting and validating laboratory-related information.

05

Where is DocKnow deployed?

Deployment

Inside your infrastructure, either in your cloud VPC or on-premises. Documents and patient data never leave your environment, so you stay in control of security and access.

For labs that want or need it, Onymos also offers secure hosting.

06

How does DocKnow connect to our other systems?

Integrations

Through APIs, HL7 / FHIR interfaces, and SFTP. Validated, structured data is written back to your LIS, EHR, and billing systems automatically.

07

How does pricing for DocKnow work?

Pricing

DocKnow pricing is tailored to your processing volume, workflow complexity, and deployment requirements. We work with your team to understand your needs and develop a pricing structure that accounts for both the software and the infrastructure costs associated with running DocKnow in your environment.

As your document volumes grow, we can also structure pricing to make scaling more economical.

08

How long does implementation take?

Onboarding

Most teams start with a single test line and expand from there. Timelines depend on the number of document types and integrations, and our team scopes them with you up front. A typical timeline is 3-4 weeks.

/ Get started /

See accessioning without the backlog.

We’ll build a custom demo using your own requisition forms, so you can see exactly how intake would work for your lab.