Sample Identification Errors in the Pre-Analytical Phase: Common Challenges and Best Practices

Sample Identification vs Sample Tracking

The terms sample identification and sample tracking are often used interchangeably, but they describe two different concepts.

Sample identification focuses on establishing and preserving the identity of a specimen. It answers a simple question:

What sample is this?

Sample tracking focuses on monitoring the movement and status of a specimen throughout the workflow. It answers a different question:

Where is the sample and what has happened to it?

Both are essential, but they are not the same thing. A laboratory may be able to track a sample from collection to analysis while still experiencing identification-related issues. If a specimen is mislabeled, linked to the wrong record, or associated with conflicting identifiers, the tracking process may continue successfully while the underlying identity problem remains unresolved.

This distinction becomes increasingly important in complex laboratory workflows involving multiple sites, systems, and handoffs.

Where Sample Identification Challenges Commonly Appear

While collection remains a critical control point, identification-related risks can emerge at multiple stages of the pre-analytical phase.

Sample Identification at Collection Sites

Collection sites often represent the first opportunity for identification errors. Challenges may include:

• manual labeling activities
• incomplete patient verification
• local process variations
• inconsistent collection practices

Even minor inconsistencies at this stage can propagate throughout the workflow.

Transportation and Handoffs

Once a sample leaves the collection site, maintaining its identity becomes dependent on labeling integrity, documentation, and chain of custody processes.

Every handoff introduces risk. This is one of the reasons why the pre-analytical phase is often more complex than it initially appears.

Samples may move between collection centers, couriers, hospitals, laboratories, and external partners. If transfer procedures are not standardized, maintaining confidence in sample identity becomes more difficult.

Laboratory Reception

At reception, samples are typically verified, registered, and linked to laboratory workflows. This stage often involves interactions between physical samples and laboratory information systems. Potential issues include:

• duplicate records
• identifier mismatches
• manual data entry
• missing collection information

Multiple Systems

Many organizations rely on separate systems for collection, transport, laboratory operations, and quality management. When information does not flow consistently between systems, maintaining a unified view of sample identity becomes increasingly challenging.

When Sample Identity Must Adapt to the Workflow

Many sample identification challenges do not result from a single mistake. Instead, they emerge gradually as the sample moves through the workflow.

At different stages, the specimen may encounter new identifiers, documentation requirements, or system-specific processes. In effect, the sample is often required to adapt to the workflow around it.

• A collection site may assign one identifier.
• A transport provider may introduce another.
• The LIS may generate an accession number.
• Additional identifiers may be applied to support local procedures, interoperability requirements, or workflow-specific needs.

Individually, these steps often make sense. Collectively, however, they can create hidden complexity.

In many organizations, sample identification challenges emerge not only when a specimen was identified incorrectly, but also when its identity must be repeatedly interpreted across different systems and workflow steps.

The additional challenge to identifying the sample once is ensuring that the same identity is preserved and correctly interpreted throughout the entire pre-analytical journey. This is particularly relevant in distributed collection networks where samples move across multiple locations, organizations, and information systems before analysis begins.

The Hidden Complexity of Sample Identification

Let’s take a look at the operational factors interacting over time.

Relabeling Events

Relabeling may occur for operational reasons, damaged labels, local procedures, or workflow requirements. While often necessary, every relabeling event introduces additional complexity. Unless carefully managed, relabeling can create uncertainty between original and replacement identifiers.

Multiple Identifiers

Many laboratory workflows rely on several identifiers throughout the sample journey. A specimen may be associated with:

• a patient identifier
• a collection identifier
• a transport identifier
• a laboratory accession number
• workflow-specific identifiers used by external partners

In some environments, additional identifiers may be introduced through tube standardization initiatives or interoperability requirements.

The challenge is not the existence of multiple identifiers. The challenge is maintaining clear relationships between them. When those relationships become unclear, organizations often experience duplicate records, manual reconciliation efforts, reduced visibility, and increased traceability risks.

Interoperability Gaps

Disconnected systems remain one of the most common sources of hidden complexity. Even when individual systems function correctly, information may be interpreted differently across platforms.

Interoperability is not only about exchanging data. It is about preserving meaning and context as information moves between systems.

The result can be fragmented visibility, duplicated information, and additional manual work to maintain consistency across the workflow.

Best Practices for Consistent Sample Identification

While every organization operates differently, several practices consistently support stronger specimen identification and traceability.

1. 1
   Standardize sample identification proceduresPatient verification, labeling practices, and exception handling should follow clear and consistent rules across all collection sites and workflow participants.
2. 2
   Define ownership at workflow transitionsEvery handoff should have a clearly defined owner. Teams should know who is responsible for verifying identity, documenting transfers, and managing exceptions throughout the workflow.
3. 3
   Improve workflow visibilityOrganizations cannot manage what they cannot see. Understanding how samples move between locations, teams, and systems helps identify risks before they become larger problems.
4. 4
   Maintain end-to-end traceabilityTraceability should extend beyond collection. Organizations should be able to reconstruct the complete journey of a sample, including collection, transport, reception, and processing events. See our guide to end-to-end traceability.
5. 5
   Focus on identity preservationThe most effective identification strategies do not focus solely on creating an identifier. They focus on preserving that identity consistently as the sample moves through the workflow.

Conclusion

Sample identification is often treated as a single event that occurs at collection. In reality, maintaining sample identity is an ongoing operational challenge that extends throughout the pre-analytical phase.

As samples move across locations, systems, teams, and workflow transitions, the risk is not simply that they become lost. The risk is that their identity becomes repeatedly reinterpreted.

For laboratories managing increasingly complex workflows, preserving a consistent sample identity requires more than accurate labeling. It also requires strong operational processes and quality management practices.

The most resilient laboratory workflows are not those that identify samples once, but those that preserve sample identity every time the workflow changes.

Key Takeaways

• Sample identification answers “what sample is this?”, while sample tracking answers “where is the sample?”. A sample can be tracked successfully while its identity is wrong.
• Identification risks do not end at collection. Transport handoffs, laboratory reception and disconnected systems all introduce new ones.
• As a sample moves through the workflow it can pick up several identifiers, and the risk lies in losing the relationships between them.
• Relabeling events and interoperability gaps add hidden complexity that no single system shows on its own.
• Standardized procedures, clear ownership at every handoff, workflow visibility and end-to-end traceability help preserve one consistent sample identity.