Pre-Analytical Errors in Pathology Labs: Root Causes, Rejection Management, and Digital Solutions

A pathology report can be analytically perfect and still be clinically unreliable if the specimen was wrong before it ever reached the analyzer.

That is the problem with pre analytical errors in pathology lab operations. They often happen before laboratory staff begin testing—during patient identification, collection, labeling, handling, storage, or transportation. Because these steps involve multiple people and locations, they can be harder to monitor than the analytical process itself.

Research has consistently shown how significant the problem is. A widely cited review estimates that 46–68% of laboratory errors occur in the pre-analytical phase. More recent evidence reinforces the point: a 2025 study analyzing approximately 11 million specimens and 37.7 million billable results found that 98.4% of recorded laboratory errors were pre-analytical. Hemolysis alone represented 69.6% of all documented errors in that study.

For a busy diagnostic laboratory, preventing these errors is therefore not simply a quality-control exercise. It is an operational priority.

The Hidden Cost of Pre-Analytical Errors

Why samples get rejected

Sample rejection is rarely caused by one dramatic mistake. More often, it is the result of small process failures repeated throughout the day.

Common examples include:

• Hemolyzed blood caused by inappropriate collection or handling

• Clotted samples submitted for tests requiring anticoagulated blood

• Insufficient sample volume

• Incorrect or missing patient identification

• Wrong collection tube

• Delayed transportation or improper storage

• Leaking or contaminated containers

• Improperly collected urine or other body-fluid specimens

The proportions vary considerably between laboratories and departments. For example, one study of 254,810 specimens reported a 0.67% rejection rate. Among rejected specimens, hemolysis accounted for 41.6%, clotting for 22.5%, and insufficient volume for 12.6%.

Another hematology study reported a much higher overall rejection rate of 5.15%, with transportation delays, incorrect medical records, diluted samples, incorrect tubes, and hemolysis among the leading causes.

The lesson is important: there is no universal “normal” rejection rate. Laboratories need to establish their own baseline and monitor trends by test, collection location, shift, and staff member.

The real cost goes beyond another tube

When a specimen is rejected the laboratory might need to contact the collection team to set up a collection. This means going through the accessioning process over again sending another specimen and possibly delaying the reporting timeline.

For the patient, it can mean another needle stick, another visit, and delayed clinical decisions.

For the laboratory, repeated recollections consume staff time and can affect turnaround time and patient satisfaction. WHO guidance also emphasizes that every stage of blood collection affects specimen quality and, ultimately, diagnosis.

Build Sample Rejection Criteria Around the Test, Not Guesswork

A strong rejection process begins with a written SOP. Instead of allowing technicians to make subjective decisions, define acceptance and rejection criteria for each specimen type and test.

WHO's Laboratory Quality Stepwise Implementation tool recommends criteria covering packaging, leakage, transportation conditions, sample quality, adequate volume, request-form completeness, and agreement between specimen and request details.

Don't just record “sample rejected”

A rejection log becomes much more valuable when it explains why the error happened.

A simple 5-Why approach can uncover the real process problem.

Example:

Problem: CBC sample clotted.
Why? The tube was not mixed adequately.
Why? The phlebotomist was unsure about the required inversion procedure.
Why? The collection SOP was not readily available at the collection point.
Root cause: Process and training gap.

This is much more actionable than simply recording a “clotted sample.”

Where Digital Workflows Can Make a Difference

Technology cannot replace trained phlebotomy staff, but the right lab management software can reduce opportunities for avoidable mistakes.

At registration or test ordering, digital systems can associate tests with the required specimen type and collection instructions. This creates an opportunity to guide staff before collection rather than discovering an error after the sample reaches the laboratory.

Barcode-based identification adds another layer of control. Instead of manually entering accession numbers repeatedly, a barcode can connect the patient, order, specimen, and subsequent workflow.

A useful workflow can look like:

Patient Registration → Test Selection → Collection Guidance → Barcode Generation → Sample Collection → Accession Verification → Processing → Reporting

The important principle is that identification should happen at more than one checkpoint. WHO specifically recommends a system for identification and tracking so that the specimen remains correctly matched to the patient and result.

Digital rejection workflows can also standardize what happens after an unacceptable sample is identified. The technician can select a predefined rejection reason, record comments, notify the responsible team, and create a recollection requirement without relying entirely on paper registers or informal communication.

For laboratories evaluating these capabilities, a practical can be used to assess how a lab management software platform fits into registration, accessioning, reporting, and day-to-day workflow management.

The goal should not be “more software.” The goal should be fewer opportunities for avoidable errors.

Measure the Process: Quality Indicators That Actually Help

A laboratory cannot improve what it does not measure. Useful pre-analytical quality indicators include:

• Sample rejection rate = rejected specimens ÷ total specimens × 100

• Hemolysis rate

• Clotted-sample rate

• Insufficient-volume rate

• Wrong-tube rate

• Mislabeling or unlabeled-sample rate

• Recollection rate

• Recollection turnaround time

• Rejections by collection location or department

• Rejections by reason and shift

For example if a laboratory processes twenty thousand samples per month and rejects two hundred the laboratory’s overall rejection rate is one percent. If ninety of those rejections are due, to hemolysis hemolysis alone accounts for percent of all rejected specimens.

That number immediately tells the quality manager where corrective action may have the greatest impact.

The objective isn't zero rejection

Some specimens will inevitably be unsuitable. The quality objective is not to force the rejection rate to zero by accepting questionable samples.

It is to identify unacceptable specimens consistently, prevent avoidable recollections, and understand why failures are happening.

Pre-analytical quality is, at the heart of safety, laboratory efficiency, staff training and technology. A designed SOP sets the rules. Trained staff carry out those rules. Digital systems help make those rules easier to follow and track.