You don’t have a LIS problem. You have a data problem.

Look at where issues originate.

LIS platforms can be rigid, difficult to configure, and slow to adapt to changing workflows. But even with those limitations, most systems perform well when they receive clean, structured data.

In most labs, the bottleneck starts before the data ever reaches the system. If you’re dealing with missing field values and using manual data entry, the problem is likely your data.

A LIS (Laboratory Information System) manages samples, workflows, and results. An intake layer like DocKnow operates upstream. It captures, validates, and structures data before it reaches your LIS.

It’s the missing layer in most lab tech stacks, bridging the gap between messy real-world inputs and the clean data your LIS expects. Without clean data, even the best LIS will struggle.

In practice, they are often used interchangeably. Hypothetically, a LIS is intended to manage patient data and a LIMS is intended to manage patient sample data.

No. DocKnow is designed to work alongside your existing LIS, not replace it. It acts as an intelligent intake layer that ensures your LIS receives clean, structured, system-ready data so it can operate more efficiently.

DocKnow integrates with leading platforms like LabVantage, LabWare, and even custom LIS environments built on technologies like Salesforce.

Your LIS doesn’t struggle because it’s flawed. It struggles because it’s fed bad data. Research shows that up to 70% of lab errors originate before testing begins, and only a fraction of errors occur during actual analysis.

If the data entering your system is messy, every downstream workflow inherits that problem.

Yes. DocKnow is designed to be able to integrate with every part of your existing lab ecosystem, including LIS, EHRs, and billing platforms. It delivers data via configurable APIs in standard formats like FHIR and JSON.