A single sample mix-up can undermine an entire laboratory workflow.
In a College of American Pathologists (CAP) Q-Probes study on specimen labeling errors from more than 3.3 million specimen labels across 147 laboratories, 0.92 labeling errors were identified for every 1,000 labels reviewed. The study also found that laboratories with ongoing specimen-identification quality monitoring had fewer errors, highlighting the value of continuous quality controls.
Sample loss and misidentification can occur at almost any stage, from collection and labeling to registration, transportation, storage, and testing. The consequences go beyond a misplaced tube. A lost sample can lead to recollection, delayed turnaround times, and additional costs, while a misidentified sample can produce a result that appears valid but is linked to the wrong record. The World Health Organization (WHO) laboratory quality management guidance emphasizes the importance of proper sample management to maintain the reliability of laboratory results.
For laboratories handling high sample volumes, managing these risks requires more than asking staff to be careful. A structured approach is needed to reduce errors, maintain sample traceability, and protect the integrity of laboratory data.
Why Sample Loss and Misidentification Are Significant Risks
Sample management involves multiple handoffs and activities, including collection, labeling, transportation, registration, preparation, testing, storage, and disposal. Each transition creates an opportunity for something to go wrong.
Common risks include:
- Samples being labeled incorrectly or left unidentified
- Sample labels not matching request forms or electronic records
- Samples being placed in the wrong storage location
- Samples being lost during internal or external transportation
- Duplicate or incorrect sample IDs being entered into a system
- Samples being associated with the wrong test request
- Poor documentation making it difficult to trace a sample’s history
- Samples being retained beyond or disposed of before the required retention period
- Damaged or compromised samples being accepted for testing
These risks can have downstream consequences. A lost sample may require recollection, delaying turnaround time and increasing workload. A misidentified sample can be more serious because the resulting data may appear valid while actually belonging to another sample or individual.
A CDC systematic review found that standardized specimen-labeling protocols and improved communication between laboratory and clinical staff were associated with substantial reductions in specimen identification errors.
This highlights an important point: sample identification is not simply a labeling issue. It is a process-control issue.
Where Sample Loss and Misidentification Can Occur
Understanding where failures happen is the first step toward controlling them.
1. During Sample Collection
Collection is one of the earliest points at which sample identification can fail.
An operator may select the wrong container, write incorrect information on a label, use a label intended for another sample, or fail to label the sample immediately. Similar problems can occur when several samples are collected or processed at the same time.
Clear collection procedures should define:
- What information must be recorded
- How the sample should be identified
- When labeling should take place
- Which containers should be used
- How collection information should be documented
- What should happen when identification is incomplete or uncertain
WHO’s sample collection guidance specifically recommends that sample collection procedures include instructions for patient identification and other pre-collection requirements.
2. During Sample Reception
The reception stage provides an important opportunity to catch errors before a sample enters the testing workflow.
Staff should verify that the sample and accompanying documentation agree. This may include checking the sample ID, requester information, collection date, sample type, quantity, condition, and requested examination.
A laboratory should also have clearly defined acceptance and rejection criteria. Sample accepting criteria from WHO recommends checking factors such as correct packaging, transport conditions, sample quality, adequate volume, completeness of the request form, and agreement between request-form and sample details.
If identification cannot be confirmed, the sample should not simply move forward because the laboratory is busy or the sample is difficult to replace.
3. During Transportation
Samples can pass through several locations before reaching the testing area. External couriers, collection centers, receiving areas, and internal laboratory staff may all be involved.
Without appropriate tracking, it can become difficult to determine:
- When a sample was collected
- When it left the collection point
- Who received it
- Where it is currently located
- Whether it arrived within the required timeframe
- Whether the sample experienced conditions that could affect its integrity
WHO’s laboratory quality management guidance emphasizes the importance of sample handling systems covering collection, transportation, storage, and disposal.
4. During Storage and Retrieval
Even when a sample arrives correctly labeled, it can still become difficult to locate later.
Manual storage systems can make retrieval particularly challenging when laboratories handle large numbers of samples. A sample may be placed on the wrong shelf, refrigerator rack, freezer box, or storage area. Inconsistent naming conventions can make the problem worse.
A well-controlled storage process should link the sample ID with its physical or digital location so staff can determine where the sample should be and identify when it was moved.
5. During Testing and Data Entry
Misidentification can also occur when the sample is linked to the wrong test order, instrument record, or result.
For example, an analyst may select the wrong sample from a batch, manually enter an incorrect identifier, or transfer information between systems incorrectly.
This is why sample traceability needs to continue throughout the entire workflow rather than stopping once a sample has been received.
Key Risk Management Strategies for Sample Loss and Misidentification
1. Establish Clear Sample Identification Procedures
The foundation of sample management is a standardized identification process.
Laboratories should define exactly what information is required for each sample type and how that information should be recorded. Procedures should also explain what staff must do when information is missing, inconsistent, or unclear.
The goal is to eliminate unnecessary interpretation.
Instead of allowing individual employees to decide whether a sample is “probably” identifiable, the SOP should establish a clear rule for acceptance, rejection, escalation, and documentation.
2. Use Unique Sample Identifiers
Every sample should have a unique identifier that connects the physical sample with its corresponding record.
Where appropriate, barcode-based identification can reduce manual data entry and help maintain the connection between samples, test requests, and results. CDC research on laboratory medicine has identified electronic barcoding as an effective means of linking patients, specimens, and laboratory testing throughout the testing process.
However, technology should support the identification process rather than replace good procedures. Even barcode systems depend on correct label generation, application, scanning, and verification.
3. Introduce Verification at Critical Handoffs
A sample may pass through several people and locations. Each handoff represents a potential control point.
Verification can be built into critical stages such as:
Collection → Reception → Registration → Processing → Testing → Storage → Disposal
At each stage, staff should be able to confirm that the sample ID and associated information are correct before continuing.
This creates multiple opportunities to detect an error before it affects later stages of the workflow.
4. Define Sample Acceptance and Rejection Criteria
Not every sample should enter the testing process.
Laboratories should establish criteria for conditions such as:
- Missing identification
- Incorrect or inconsistent identification
- Insufficient sample volume
- Damaged or leaking containers
- Incorrect sample type
- Inappropriate transport conditions
- Excessive transport delays
- Incomplete documentation
This information is valuable for risk management because repeated rejection for the same reason may indicate a weakness earlier in the process.
5. Track Samples Throughout Their Lifecycle
A sample should not disappear from the system simply because it has moved from one location to another.
Effective sample tracking should provide visibility into key events such as:
- Sample collection
- Receipt
- Registration
- Processing
- Testing
- Transfer
- Storage
- Retrieval
- Disposal
This creates a traceable history that can help staff locate samples and investigate discrepancies.
For laboratories handling high volumes, digital sample tracking can also reduce dependence on manually maintained logs and spreadsheets.
6. Control Sample Storage Locations
A tracking system is only useful if the laboratory knows where samples are physically stored.
Storage locations should be standardized and clearly identified so staff can quickly locate samples when needed. A laboratory inventory management system can support this by keeping storage information organized and easier to track.
For retained samples, laboratories should also define retention periods and disposal procedures. This prevents samples from being discarded prematurely while avoiding unnecessary storage of samples that no longer need to be retained.
7. Monitor Sample Rejection and Error Trends
Recording an error is not enough. Laboratories should look for patterns.
Useful metrics may include:
- Number of rejected samples
- Misidentified samples
- Unlabeled samples
- Samples with mismatched documentation
- Lost or untraceable samples
- Sample recollection rates
- Errors by collection location
- Errors by sample type
- Errors by process stage
- Repeat errors involving the same cause
Trend analysis can reveal whether an issue is isolated or systemic.
For example, if most identification errors originate from one collection point, simply retraining the entire laboratory may not address the underlying problem. The collection procedure, labeling process, workload, or handoff at that specific location may need to be investigated.
8. Investigate Incidents Using Root Cause Analysis
When a sample is lost or misidentified, correcting the immediate problem is only the first step.
The laboratory should ask:
What happened?
Where did the process fail?
Why was the error not detected earlier?
What conditions contributed to the failure?
What control could prevent the same event from happening again?
Root cause analysis can help distinguish between an individual mistake and a process weakness.
For example, if an employee repeatedly enters incorrect sample IDs, the issue may not simply be a lack of attention. The process may involve excessive manual entry, unclear identifiers, poor system design, inadequate verification, or unrealistic workload.
Corrective actions should therefore address the cause rather than only reminding employees to “be more careful.”
9. Train Staff and Assess Competency
Even well-designed procedures can fail if employees do not understand them or apply them consistently.
Training should cover:
- Sample identification
- Labeling requirements
- Acceptance and rejection criteria
- Sample handling
- Transportation
- Storage
- Documentation
- Incident reporting
- Use of tracking systems
Training should not be limited to new employees. Refresher training and competency assessments can help identify recurring weaknesses and reinforce critical procedures.
10. Use Risk-Based Controls Rather Than Treating Every Sample the Same
Not every sample presents the same level of risk.
A laboratory can assess risks based on factors such as sample stability, test criticality, replacement difficulty, storage requirements, volume, and consequences of misidentification.
Higher-risk processes may require additional verification steps or tighter controls.
The recently published ISO 22367:2026 specifically addresses risk management in medical laboratories and describes a process for identifying, estimating, evaluating, controlling, and monitoring risks across pre-examination, examination, and post-examination activities.
This supports a more proactive approach to laboratory risk management: identify where failures could occur before waiting for an incident to expose them.
From Incident Management to Preventive Risk Management
A common mistake is to treat sample loss or misidentification as isolated incidents.
A sample goes missing, staff search for it, the sample is eventually found or recollected, and the workflow continues.
But if the same problem occurs repeatedly, the laboratory is dealing with a process failure rather than a one-time mistake.
A stronger approach connects risk identification, incident reporting, root cause analysis, corrective action, and monitoring.
For example:
Sample misidentified → Incident recorded → Root cause investigated → Corrective action assigned → Effectiveness reviewed → Process updated
This creates a feedback loop where individual errors can contribute to broader process improvement.
How Digital Sample Management Can Strengthen Risk Controls
Manual records can make sample tracking difficult when laboratories manage high volumes, multiple storage locations, or complex workflows.
A digital laboratory management system like QISS LAB can help centralize sample information and track its movement throughout the workflow. Depending on the system, laboratories can maintain sample records, monitor sample locations, manage storage information, track workflow status, and keep associated documentation connected in one environment.
The objective is not simply to replace paper with software. The greater benefit is creating a more traceable workflow in which sample information remains connected as the sample moves through different stages.
This can make it easier for laboratory teams to identify where a sample is, determine what happened to it, and investigate deviations when something goes wrong.
A Practical Checklist for Reducing Sample Loss and Misidentification
Laboratories can use the following questions to assess their current controls:
- Does every sample receive a unique identifier?
- Are identification requirements clearly defined for each sample type?
- Are samples checked against their request forms or electronic records?
- Are acceptance and rejection criteria documented?
- Is there a defined process for unidentified or mismatched samples?
- Can staff determine where a sample is currently located?
- Are sample movements recorded?
- Are storage locations standardized?
- Are rejected and lost samples recorded and categorized?
- Are recurring errors analyzed for trends?
- Are incidents investigated using root cause analysis?
- Are corrective actions assigned and monitored?
- Is corrective action effectiveness reviewed?
- Are staff regularly trained and assessed for competency?
- Are high-risk sample processes subject to additional controls?
If several of these questions cannot be answered confidently, there may be opportunities to strengthen the laboratory’s sample management process.
Conclusion
Sample loss and misidentification are often the result of small process failures that can become bigger quality problems. The key is to make samples easy to identify, track, and verify at every important stage of the workflow.
For laboratories handling growing sample volumes, moving away from fragmented records can make this easier. A centralized digital system can keep sample information, locations, and related records connected, giving teams better visibility when they need to find a sample or investigate an issue.
With better visibility and traceability, laboratories can spend less time searching for samples and more time focusing on reliable results. If you’re ready to strengthen your sample management process, request a free demo to see how a more connected laboratory workflow can support your team.
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