Types of Sample Tracking Systems — and the First Mistake Labs Make When Implementing Them

Laboratories invest in sample tracking systems to improve traceability, reduce errors, and gain better visibility across the diagnostic workflow.

But there is a recurring issue.

Even after implementing a laboratory sample tracking system, many workflows don’t actually improve.

Why?

Because systems change how data is captured — not how processes operate.
This is the first mistake many laboratories make:
assuming that implementing a tracking system will automatically fix the workflow.

As discussed in our previous article on choosing a laboratory sample tracking system, selecting the right solution is only the first step.

Why sample tracking systems don’t automatically improve workflows

Sample tracking systems are designed to answer a very specific question:

What happened to the sample?

They do this very well.

They capture:

• scan events
• timestamps
• user actions
• sample location

This enables sample traceability in laboratories, which is also aligned with requirements defined in standards such as ISO 15189 for medical laboratories.

However, they do not answer:

Was the process correct, efficient, or consistent?

This is a critical distinction.

Tracking systems improve visibility.
They do not enforce workflow control.

The main types of sample tracking systems used in laboratories

Not all specimen tracking systems are built for the same purpose. Understanding their differences helps clarify what to expect from each.

1. Barcode sample tracking systems

These are the most common systems used in laboratories.

They rely on barcode generation and scanning to identify and track samples across steps.

Strengths:

• simple and scalable
• reliable sample identification
• improved traceability

Limitations:

• no control over process execution
• dependent on operator behavior

A barcode system ensures that a sample is identifiable — not that the workflow is optimal.

2. LIS-integrated tracking systems

Many laboratories use tracking features embedded within their LIS.

These systems connect tracking data to orders, results, and patient records.

Strengths:

• centralized data environment
• integration with lab operations

Limitations:

• limited real-time operational visibility
• weak support for pre-analytical workflow variability

3. Standalone sample tracking software for laboratories

These platforms are designed specifically for tracking samples across multiple steps or locations.

Strengths:

• more flexible than LIS-based systems
• broader visibility across workflows

Limitations:

• often disconnected from workflow decision logic
• still focused on event tracking rather than process control

4. Chain-of-custody and transport tracking systems

These systems focus on logistics, compliance, and external sample flows.

They are widely used for:

• multi-site networks
• external collection centers
• regulated transport environments

Strengths:

• strong documentation and compliance
• visibility across locations

Limitations:

• limited control over internal laboratory operations

What all sample tracking systems actually do (and don’t do)

Regardless of the type, all sample tracking systems share the same core function:

They record what happens.

They:

• log events
• capture timestamps
• create a traceable history

But they do not:

• enforce process consistency
• prevent delays or bottlenecks
• ensure correct sequencing
• standardize behavior across teams

They make workflows visible, not reliable.

The first mistake: assuming tracking equals workflow control

This is where most implementations fail.

Barcode tracking in laboratories is often seen as a way to “secure” the process.

In reality, it secures the data, not the workflow.

A sample can be:

• correctly labeled
• properly scanned
• fully traceable

…and still be:

• delayed
• misrouted
• processed too late

Tracking systems expose problems.
They do not fix them.

Where gaps appear in the pre-analytical workflow

The limitations of tracking systems are most visible in the pre-analytical workflow, where variability is highest.

Several studies show that the majority of laboratory errors occur in the pre-analytical phase.

Learn more about pre-analytical workflow challenges

Sample collection and identification

Even with barcode systems, inconsistencies remain:

• manual labeling practices
• local variations
• human error

Tracking records the step — it does not standardize it.

Transport and handover points

Between collection and the lab:

• delays are often invisible
• batch transport reduces granularity
• real-time tracking is limited

A system confirms arrival — not the journey.

Accessioning and routing

Inside the lab:

• bottlenecks still occur
• prioritization is often manual
• workload is not dynamically managed

Tracking logs these issues — but does not resolve them.

Why workflow structure matters more than the system

A system reflects the workflow it supports.

If the process is:

• inconsistent
• undefined
• dependent on manual decisions

the system will simply document that complexity.

Improvement requires:

• clear process design
• standardized steps
• defined responsibilities
• controlled handovers

Without this, even the best laboratory sample management software will generate data — but not improvement.

From traceability to workflow intelligence

Many laboratories are now moving beyond tracking toward a more operational model.

The evolution looks like this:

• Tracking → data collection
• Visibility → understanding
• Workflow intelligence → action

What changes at this stage is the ability to:

• react in real time
• trigger alerts
• support operational decisions

This is where tracking alone is no longer sufficient.

Final takeaway: a system is necessary — but never sufficient

Choosing the right system matters.

But implementation success depends on something else entirely.

As highlighted in our guide to choosing a laboratory sample tracking system, system selection alone does not guarantee operational improvement.

Systems improve how data is captured.

Workflows determine how operations perform.

Understanding this difference is what allows laboratories to move from traceability to true operational control.