Clinical Laboratory Staffing Shortages: The Hidden Impact on Pre-Analytical Quality

Lab Staffing in 2026: The Numbers

The clinical laboratory workforce shortage is not a temporary disruption. According to the 2026 Annual Salary Survey published by Medical Laboratory Observer (n=406 laboratory professionals), 79% of respondents reported difficulty hiring qualified staff, up from 75% in 2025. 63% reported that personnel shortages have a moderate or large operational impact on their laboratory. The share reporting a “large impact” grew from 16% to 20% in a single year.

These figures align with the findings of the American Society for Clinical Pathology (ASCP) 2024 Vacancy Survey, the most comprehensive longitudinal workforce barometer for US clinical laboratories (1,027 laboratory leaders, 37-year survey history; PMID 41017742). The 2024 survey confirmed that vacancy rates, while lower than the 2022 peak, remain structurally elevated above pre-COVID-19 pandemic levels. Of 17 laboratory departments surveyed, 10 reported rising retirement rates, compounding a pipeline problem that constrained educational capacity alone cannot resolve.

The structural nature of the crisis reached federal legislative recognition in September 2025, when the Medical Laboratory Personnel Shortage Relief Act (H.R. 5444, 119th Congress) was introduced, authorising grants to expand accredited Medical Laboratory Scientist and Medical Laboratory Technician training programmes across the United States. When shortages require an Act of Congress to address, the problem is not a temporary anomaly.

The consequences that follow extend well beyond recruitment budgets and overtime costs.

How Understaffing Reaches the Bench

A laboratory running with fewer full-time equivalents does not simply produce fewer results. It produces the same volume of results with less labour available per sample, and that is where the quality risk begins.

The mechanism is straightforward: fewer technologists covering more instruments and more specimens means less time per task. In that context, the first things to compress are not the analytical steps, which are automated and instrument-monitored, but the surrounding manual steps: sample identification checks at reception, documentation of collection conditions, centrifugation timing, and transport oversight.

ADLM’s Clinical Laboratory News commentary (2026) describes the dynamic directly:

Quality control steps that get deferred under time pressure. Turnaround times that inch upward. Fewer technologists covering more instruments, more specimens, and more documentation.

ADLM Clinical Laboratory News, 2026

The degradation is incremental rather than catastrophic, which is precisely what makes it difficult to detect and easy to underestimate.

It is worth distinguishing two overlapping but distinct shortages. The figures in the preceding section reflect vacancies among Medical Laboratory Scientists and Technicians — the credentialled staff who operate instruments, interpret results, and manage quality inside the laboratory. Phlebotomists, who perform blood collection and represent the first point of contact between patient and sample, are a separate workforce category tracked independently by both the Bureau of Labor Statistics and the ASCP Vacancy Survey. The US Bureau of Labor Statistics projects phlebotomist employment to grow 7% from 2025 to 2035 — significantly faster than average — with approximately 18,000 openings expected per year across the decade (BLS Occupational Outlook Handbook, 2025). Demand is consistently outpacing supply: a study published in the Archives of Pathology and Laboratory Medicine found that phlebotomy had the highest turnover of any laboratory discipline, with a median of nearly 25% of phlebotomists leaving within three years — nearly double the rate for other laboratory roles. When phlebotomist positions remain unfilled, blood collection is redistributed to nurses, medical assistants, or ward personnel: clinicians who may be fully competent in their own roles but who have less specific training in the variables that govern specimen quality at the tube level — needle gauge, tourniquet time, fill volume, mixing technique, and tube-additive sequence. The collection phase, already the highest-risk step in the pre-analytical workflow, becomes more variable at exactly the moment when staffing pressure is greatest.

Understaffing also introduces task-shifting risk. In settings where phlebotomy and sample handling are performed by less experienced personnel, whether because trained phlebotomists are unavailable or because workload pressures require temporary reassignments, the pre-analytical phase is exposed to a different error profile than the one the laboratory’s quality programme was designed to manage.

The Pre-Analytical Phase Under Stress

The pre-analytical phase is already the most error-prone stage of the total testing process. Current literature places the share of laboratory errors originating before the sample reaches the analyzer at approximately 60 to 70% of all laboratory errors (PMC10981510, 2024 PRISMA review). Among all the phases of laboratory testing, the pre-analytical is the most dependent on manual steps, human judgment, and personnel continuity.

What makes this phase particularly vulnerable to staffing pressure is the nature of the work it requires. Reception check-ins, barcode verification, centrifugation timing, and transport documentation are not analytical outputs that an instrument validates. They are procedural steps that a person performs, and their reliability depends on that person having the time and attention to complete them correctly.

Under understaffing conditions, specific failure modes become more likely. Delayed centrifugation is one of the most documented: when reception is backed up and samples sit unprocessed, the window before haemolysis begins to accelerate is lost. Sample identification errors, missed documentation of collection conditions, and inconsistent logging of transit times all become more common when fewer staff are covering more samples. For a detailed look at how pre-analytical handling conditions affect specimen quality at the analyte level, the article on haemolysis in laboratory samples covers the mechanisms and the prevention framework.

What the Research Shows

The quantitative link between workload and pre-analytical error frequency is documented in current peer-reviewed literature.

A 2026 study published in PLOS One (PMC12959673) analysed 195 pre-analytical errors at a high-volume clinical diagnostic centre over a two-month period. The finding was statistically clear: 70.8% of pre-analytical errors occurred on high-workload days (those processing 931 or more samples), compared to 29.2% on low-workload days. The association was highly significant (chi-squared = 121.093, p < 0.001).

The study’s limitation should be noted: it is a single-centre analysis conducted over a two-month window at a high-volume diagnostic centre in Bangladesh, which limits direct generalisability to other institutional contexts. It remains, however, the strongest available primary data linking workload level, a direct operational proxy for understaffing, to pre-analytical error frequency in a controlled observational design.

At the operational scale, the consequences are visible in rejection rates. An analysis of 231,008 CBC blood samples processed over four months at a high-volume haematology laboratory found that 5.15% of samples (11,897 samples) were rejected due to pre-analytical errors (PMC9979861, 2023). The global pooled rejection rate from a meta-analysis across 26 studies and over 16 million specimens is 1.99%, placing the 5.15% figure at the high end of the documented range but well within it. At a laboratory processing 200,000 samples annually, a pre-analytical rejection rate at that level represents 10,000 redraws per year.

The Sample Visibility Gap

Understaffing creates a second-order problem that is often harder to quantify than error rates alone: the loss of visibility into what is happening to the sample before it arrives at the laboratory.

When a laboratory is fully staffed, informal quality checkpoints are distributed across the workflow. A technologist at reception notices a tube without a collection timestamp. A courier coordinator identifies that a sample has not been picked up within the expected window. A senior staff member queries why a particular batch shows an unusually high rejection rate for that shift. None of these are formalised processes, but together they form a continuous layer of human surveillance that catches deviations before they become rejections.

When that layer thins, samples move through the pre-analytical phase with less human observation at each step. The chain of custody is documented less consistently. By the time a problematic sample reaches the analyzer, its pre-analytical history is incomplete and largely unrecoverable.

This is the visibility gap: not just more errors, but fewer opportunities to detect them. It is the aspect of understaffing-related quality risk that standard rejection rate monitoring is least equipped to capture.

What Labs Can Do About It

The workforce shortage is unlikely to resolve quickly. The structural conditions, including constrained training pipelines and compounding retirements, are not amenable to short-term fixes. What laboratory managers can address in the near term is not the headcount problem but the quality consequences of running lean.

Five interventions reduce pre-analytical risk without requiring additional personnel.

1. 1
   Map where errors are actually occurringRejection rate by error type, by collection site, by shift, and by day of week reveals patterns that aggregate statistics conceal. If the majority of rejection events originate from a single collection point or a specific shift, that is a targetable problem, but only if the data distinguishes it.
2. 2
   Formalise the minimum non-negotiable checkpointsIdentify which pre-analytical steps are currently being deferred under time pressure and separate them into two categories: those that can be de-prioritised without quality impact, and those that cannot. Make the second category explicit, documented, and non-negotiable regardless of workload level.
3. 3
   Standardise collection protocols across staff levelsTask-shifting introduces variance when procedures are informal. Written, unambiguous protocols for tube handling, centrifugation timing, and transport conditions reduce error rates for less experienced personnel and accelerate training for new or reassigned staff.
4. 4
   Automate sample identity and trackingManual verification steps that depend on available personnel are exactly the steps most likely to be skipped under pressure. System-level verification, including barcode scanning at collection, timestamped check-ins at each handoff point, and automated alerts for samples not received within expected windows, provides the same oversight without requiring a person to be physically present at each stage.
5. 5
   Use real-time exception alerts for outliersA sample sitting uncollected 45 minutes after phlebotomy, or a courier delayed past the centrifugation window, cannot be corrected once it arrives damaged. An alert triggered before the damage occurs converts a quality incident into a preventive intervention.

The underlying logic of steps 4 and 5 is the same: to build into the laboratory’s infrastructure the visibility that a fully-staffed team would provide informally. When the human surveillance layer thins, the gap it leaves has to be addressed by systems. For a broader view of how sample tracking operates in the pre-analytical phase, the article on sample identification in the pre-analytical phase provides the foundational framework.

Key Takeaways

• Laboratory staffing shortages are structural and worsening: 79% of lab professionals report difficulty hiring in 2026, and ASCP data confirms vacancy rates remain above pre-COVID baseline despite a partial recovery from the 2022 peak.
• Pre-analytical quality is the first casualty of running lean, because the pre-analytical phase is the most manual and personnel-dependent stage of the testing process.
• Current data confirms the link: on high-workload days, pre-analytical error rates are significantly higher, with 70.8% of pre-analytical errors clustered on days with elevated sample volumes (PLOS One, 2026).
• Understaffing creates a visibility gap beyond the error rate itself: fewer people means fewer informal checkpoints and less complete documentation of each sample’s pre-analytical history.
• The path forward is not waiting for the workforce crisis to resolve. It is building quality systems that provide the oversight a fully-staffed team would, automatically and consistently, regardless of headcount.