Diagnostic Support Services
Get Pricing
Guides

Clinical Lab Workflow Optimization: Fixing the Pre-Analytical Bottlenecks

Most of the time a result takes, and most of the errors it carries, happen before the specimen reaches an analyzer. This guide is about that part: six places clinical laboratories lose turnaround before the bench, the evidence for each, the fix, and the number that tells you whether the fix worked. It is about operations, not software.

Published

Go to the six bottlenecksPre-analytical checklist (PDF)
Quick Answers

Four questions, answered first

The rest of the guide is the evidence and the method behind them.

What is a clinical laboratory workflow?

The sequence of steps that turns a test order into a result a clinician acts on: ordering, collection, identification, transport, accessioning and preparation, analysis, reporting, interpretation and action. Lundberg called it the brain-to-brain loop. Only one of those nine steps happens on an analyzer.

Where is turnaround time actually lost?

Before the bench. Depending on the study, 46% to 70% of laboratory errors arise in the pre-analytical phase, and non-analytical delays can account for most of the turnaround time a clinician experiences. The common culprits are collection timing, kit and supply gaps, courier cutoffs that ignore stability windows, accessioning queues, rejection and redraw loops, and manual data handoffs.

What is specimen accessioning?

The step at which a specimen arriving in the laboratory is matched to its order, checked for acceptability, given a unique accession number and barcode labels, and entered into the laboratory information system. CLIA requires the laboratory to record the date and time it receives each specimen. In interfaced systems most of the manual work disappears; in paper systems it is often the hidden queue.

What is lab workflow optimization?

Finding where specimens and information wait, and removing the wait. Most of it is operational rather than technical: when draws are scheduled, how kits arrive at sites, when couriers run, how specimens are received, how rejections are fed back, and where data is re-keyed. Software and automation help once the flow is understood; bought first, they automate the queue.

The Evidence

Where the errors and the minutes go

George Lundberg's brain-to-brain loop, set out in a 1981 JAMA editorial, describes laboratory testing as a cycle that starts and ends in the clinician's head: ordering, collection, identification, transportation, preparation, analysis, reporting, interpretation and action[6][7]. Pre-analytical errors, in Plebani's summary, arise in patient preparation, collection, transport, preparation and storage[5]. Everything a laboratory calls workflow is the middle of that loop, and the analyzer is one step of nine.

The error data has pointed the same way for twenty years. When Carraro and Plebani re-audited a hospital stat laboratory, 61.9% of 160 confirmed errors were pre-analytical, 15% analytical and 23.1% post-analytical[1]. Across the published surveys the pre-analytical share runs from 46% to 68.2%[2], and the European pre-analytical working group puts it at nearly 60% to 70%, most of it mishandling during collection, handling, preparation and storage, with nearly one fifth of those errors leading to inappropriate investigations and cost[4].

Time follows the same shape. Hawkins' review of turnaround time notes that most laboratories measure only the intralaboratory interval, while clinicians experience order to result, and that non-analytical delays can account for up to 96% of total turnaround time[7]. In a 1998 CAP survey, 41% of laboratories defined emergency turnaround as receipt-to-report and only 18% as collection-to-report, while more than 40% of physicians counted from the moment they ordered[7]. The laboratory and its customers are timing different races.

PhaseStepsShare of errors
Pre-pre-analytical and pre-analyticalTest selection and ordering; patient preparation; collection; identification and labeling; transport; receipt and accessioning; centrifugation and aliquoting46% to 70% of errors, depending on the study
AnalyticalThe measurement itselfAbout 13% to 32% of errors; 15% in the Carraro and Plebani re-audit
Post-analytical and post-post-analyticalValidation, reporting, delivery to the record, interpretation and action18.5% to 47% of errors

Sources: Carraro and Plebani 2007[1]; Plebani 2006[2]; Bonini et al. 2002 (analytical share 13% to 32%)[3]; Lippi et al. 2011[4].

Six Bottlenecks

Where turnaround is lost before the bench

Each one with what it looks like on the floor, the evidence that it matters, the fix, and the metric that proves it.

1

Collection scheduling that ignores when results are needed

Morning draws arrive in one wave, the analyzers sit idle at 5 a.m. and queue at 8, and results land after rounds. Outpatient specimens collected late in the day miss the last run.

The evidence

Across 657 institutions, the median early-morning inpatient specimen took 25 minutes to reach the laboratory, 42 minutes to analyze and 73 minutes in total; shorter times went with delivering each specimen as it was collected, direct delivery routes and continuous rather than batch testing[11]. Moving ward phlebotomy earlier at one hospital raised the share of results available before 8:30 a.m. from under 1% to over 95% without changing laboratory turnaround at all, and same-day discharges rose from 52% to 60%[12]. In the outpatient setting, collection-to-receipt time lengthens as the day goes on, which the CAP Q-Probes authors read as growing delays in collection and transport[10].

The fix

Set draw times from the clinical decision backwards, not from the phlebotomy roster forwards. Deliver specimens as they are collected rather than at the end of a round. Stagger site pickups so arrivals spread across the shift. Where in-house phlebotomy cannot cover the hours, a mobile phlebotomypartner scheduled to the laboratory's timetable does.

Measure: Order-to-collection time by site and hour; share of inpatient results available before morning rounds.

2

Kits and supplies that are not ready at the point of collection

The draw site has the wrong tube, an expired transport medium, no requisition, or a patient who was never told to fast. The specimen is collected anyway, or not at all.

The evidence

In a CAP study of 833,289 outpatient phlebotomy encounters, the leading reasons a collection failed were a patient not fasting as directed (32.2%) and orders missing information (22.5%); sites staffed by laboratory-administered phlebotomists had higher success rates[27]. CAP's checklist asks for a collection manual at every site where specimens are collected and for expiration dating and storage of collection supplies[46]; CLIA requires written procedures for patient preparation, labeling and transport[17].

The fix

Treat the kit as the procedure made physical: the right tubes in draw order, a requisition that solicits every element in 42 CFR 493.1241(c), patient instructions, and packaging built for the return leg. Manage component expiry and replenishment centrally rather than site by site. That is what diagnostic kitting and managed consumables are for.

Measure: Failed-encounter rate by reason; expired or missing components found at audit; kit stockouts per site per month.

3

Courier cutoffs set by the van, not the specimen

A route built around traffic lands whole blood at the bench four hours after the draw. Serum sits unseparated in a hot vehicle in July or a cold one in January. The analyzer is fast; the sample is already wrong.

The evidence

Transit accounted for about a third of total stat turnaround time in a 722-institution study, and when couriers carried the specimens the median transit time equaled the laboratory's own testing time[8]. Laboratory control of specimen handling and rapid transport were the two factors associated with faster emergency department turnaround[9]. In blood cultures, prolonged transit, not slower organism growth, drove late detection, and most institutions did not monitor pre-analytical time at all[13]. The chemistry is unforgiving: glycolysis lowers glucose in unseparated blood by about 5% to 7% an hour[40]; potassium climbs in blood held cold before centrifugation, by roughly 0.5 mmol/L at four hours and nearly 1 mmol/L at eight in one 201-donor study[41], and winter transport from general practice produces a seasonal rise in reported potassium that inpatient samples never show[42]. Stability is analyte-specific: most of 81 analytes were stable uncentrifuged for 24 hours, while potassium, phosphorus and glucose were the least stable and are best separated within three hours[43]. When one laboratory network put time and temperature loggers on every courier run, transports above 25 °C fell by about 80% and the differences in potassium, ALT and APTT between its distant and nearby sites disappeared[45].

The fix

Write the stability table first (CLSI PRE04 replaced GP44 in 2023 and is the current reference[44]), then design routes and cutoffs to it: last pickup times that land each specimen type inside its window, bands held with evidence, and a logger on anything temperature-sensitive. The courier RFP template and scorecard turns those requirements into contract terms; the specimen transport page shows how a route is built around them.

Measure: Collection-to-receipt time by site and route; share of specimens inside their stability window on arrival; temperature excursions per band.

4

Accessioning queues the LIS cannot see

Coolers wait on a bench. The LIS clock starts at accession, so the report says turnaround is fine while specimens sat unaccessioned for an hour.

The evidence

A single-institution study found significant delays between specimen arrival and accessioning that LIS-generated accessioning-to-result reports never captured; when those delays were counted the turnaround goal was missed, and more staffing at reception shrank them[14]. Accessioning is also where orders and specimens are reconciled: in a million surgical pathology cases, 6% had identification or accessioning deficiencies, most often a requisition with no clinical history, and laboratories with a written error-detection plan for accessioning did better[15]. The Lean literature is consistent on the remedy. One-piece flow through accessioning, centrifugation and aliquoting cut median pre-analytical processing time from 29 to 19 minutes and held 80% of chemistry results under an hour for eleven months[19]; a core laboratory using the same principles took creatinine from 54 to 23 minutes while volume rose 20%[20]; a university hospital cut stat turnaround from 68 to 59 minutes and removed three hours twenty minutes of non-value-adding reception work a day[21]. A 2024 systematic review found turnaround reductions in every included Lean study and named small batches and one-sample-at-a-time handling as the effective interventions[23].

The fix

Timestamp physical arrival separately from accession, and report both. Receive in small batches or one piece at a time, with standard work at the bench. Pre-register what is coming: when orders arrive electronically and the specimen carries a barcode the LIS already knows, manual accessioning largely disappears[16].

Measure: Arrival-to-accession time (median and 90th percentile); accessions per FTE hour; accessioning deficiencies per 1,000.

5

Rejection and redraw loops

A hemolyzed potassium, an underfilled coagulation tube, a mislabeled urine. The specimen is rejected, the patient is recalled or never recollected, and the clock restarts from zero.

The evidence

Rejection rates look small until you multiply them. In 453 laboratories, 0.35% of 10.7 million chemistry specimens were rejected, with hemolysis the leading reason, five times more frequent than insufficient volume[26]; outpatient draws ran at 0.3% unsuitable, with 11.5% of those lost or never received by the laboratory[27]; a 2023 meta-analysis of 16 million requests found a pooled rate of 1.99%, 0.55% in the Americas[29]. The cost is time: in a 78-laboratory study, rejection produced a median 65-minute delay in result availability and a high rate of tests simply abandoned[28]. Hemolysis accounts for 40% to 70% of unsuitable specimens[30], and the strongest evidence points at draws through intravenous catheters, pneumatic tube transport, non-phlebotomist collectors, underfilled tubes and tourniquet times over a minute[31]. In the one hospital that priced it, 89% of the direct cost of pre-analytical errors arose from errors detected outside the central laboratory[32], and in another, staff time was 60% of the bill[33].

The fix

Count rejections by reason, site and collector, every week, and send the numbers back to the people who can change them. Put professional phlebotomists on the draws that matter. Fill tubes to the line and mix them. Validate any pneumatic system and prefer hand or courier transport where it is practical. The hemolysis and specimen rejection guide covers the criteria and the prevention in detail.

Measure: Rejection rate by reason and site; redraw rate; share of rejected specimens recollected within six hours.

6

Data handoffs done by hand

A requisition is transcribed into the LIS, a label is handwritten at the bedside, a courier event lives in a portal nobody opens, and a result is faxed back to a practice that keys it into the chart.

The evidence

Identification errors are the pre-analytical failure with teeth: across 120 laboratories, 55 patient identification errors per million billable tests reached released results, and one in eighteen led to an adverse event[34]; 147 laboratories reviewing 3.3 million labels found 0.92 labeling errors per 1,000, with lower rates where specimen identification was monitored continuously and inpatient phlebotomy ran around the clock[35]. Barcoding is the best-evidenced fix in laboratory medicine: a CDC-funded systematic review of 17 studies found every one favored it, with manual entry carrying about 4.4 times the odds of an identification error[36], and one pediatric hospital cut mislabeled specimens from 0.03% to 0.005% after introducing bedside barcode identification[37]. Electronic ordering removed transcription errors and cut laboratory result reporting time by 25% at one academic center[38], and in an ICU implementation the median order-to-result time for stat tests fell from 148 to 74 minutes[16].

The fix

Orders in electronically (HL7 ORM or OML messages, or a FHIR ServiceRequest), barcoded labels printed at the draw, results back as ORU or DiagnosticReport, and courier and kit events delivered into the LIS rather than parked in a portal[39]. That is the work of LIS integration and EMR integration; the HL7 and FHIR guide explains the standards.

Measure: Labeling errors per 1,000 labels; identification errors reaching released results per million tests; share of orders and results exchanged electronically.

Definitions

What is specimen accessioning?

Specimen accessioning is the laboratory's intake step. A specimen arrives, is matched to its requisition or electronic order, checked against the acceptability criteria, assigned a unique accession number, labeled with the barcodes the analyzers will read, and entered into the laboratory information system[16]. The accession number identifies the laboratory's record of that receipt; it is not always the same as the specimen identifier printed at the draw.

Two regulatory facts sit underneath it. CLIA requires the laboratory to document the date and time it receives each specimen, and to keep in the test record the specimen's identification, that receipt time, and the condition and disposition of anything that fails the acceptability criteria[17]. And the requisition the accessioner checks against must solicit the elements listed in 42 CFR 493.1241(c), including the patient identifier, the orderer, the tests, the specimen source and the collection date and time[18].

Accessioning becomes a bottleneck for a plain reason: the LIS clock usually starts here, so the queue in front of it is invisible to the turnaround report[14]. The fixes are to timestamp arrival, receive in small batches, and move order entry upstream so the specimen arrives already known to the system[16]. A specimen accessioner's day is detail work under time pressure, which is why the labs that wrote down how accessioning errors would be detected had fewer of them[15].

Measure It

The pre-analytical scorecard

Eight numbers that describe the part of the workflow the analyzer never sees. Most laboratories already hold the timestamps for the first four.

MetricDefinitionWhy it matters
Order-to-collectionMinutes from order entry to the collection timestamp, by location and hour.Shows whether draw scheduling follows clinical need. Pair with the share of results available before rounds.
Collection-to-receiptMinutes from collection to the laboratory's documented receipt time, by site and route.The transport interval. CLIA requires the receipt timestamp, so the data exists. Watch the afternoon tail.
Arrival-to-accessionMinutes from physical arrival to accession in the LIS.The queue most LIS reports hide. Report the median and the 90th percentile.
Accession-to-resultThe intralaboratory interval most laboratories already report.Necessary, but it is the smallest share of what the clinician experiences.
Outlier rateShare of results exceeding a defined time, or the 90th percentile time, by test.Turnaround distributions are skewed; averages hide the tail that generates complaints.
Rejection rate by reasonRejected specimens as a share of accessions, split by hemolysis, volume, clotting, labeling, lost in transit.Benchmarks: 0.3% to 0.35% in US Q-Probes studies; 0.55% in a meta-analysis of the Americas.
Labeling errors per 1,000Mislabeled, unlabeled and requisition-mismatch specimens per 1,000 labels.Benchmark 0.92 per 1,000 across 147 laboratories. Barcoding at the draw is the lever.
Temperature excursionsMonitored shipments outside their band, by band and route.Only measurable with loggers. One network cut hot transports by 80% once it could see them.

Benchmarks from Jones et al. 1997[26], Dale and Novis 2002[27], Getawa et al. 2023[29], Wagar et al. 2008[35] and Zaninotto et al. 2012[45]. Hawkins' case for outlier rates over means is in his 2007 review[7].

Method

Lean works here, and it works before the bench

The laboratory Lean literature is small but consistent, and its gains cluster in the pre-analytical phase. Persoon and colleagues moved chemistry specimens in one-piece flow through accessioning, centrifugation and aliquoting and cut median pre-analytical processing from 29 to 19 minutes[19]. Henry Ford's molecular laboratory found its largest delays in specimen collection and transport and cut tissue test turnaround 44%, from 2.7 to 1.5 days[22]. One laboratory applied a Kaizen event to the pre-analytical phase and took emergency glucose turnaround from 84 to 73 minutes with no new staff[25]. The 2024 systematic review found reductions in every study it included, ranging from under 2% to 87%[23].

The method matters more than the label. Villa's argument is that large batches raise both error potential and lead time, and that process mapping should precede any automation decision; one laboratory he describes cut its reception batch from 30 specimens to 8 and tripled hourly capacity[24]. That is the reason this guide is about operations rather than software. An interface that removes re-keying is a genuine workflow fix. An automation line bought before the flow is understood simply automates the queue.

Getting Started

A thirty-day pre-analytical audit

Four weeks, no capital request, and a baseline you can defend.

Week 1

Map the real flow

Walk one specimen of each type from order to result, including the sites, couriers and the bench. Note every place it waits. Lean mapping before any purchase decision is the point Villa makes about automation: understand the flow, then automate what remains.

Week 2

Get the timestamps

Pull order, collection, receipt, accession and result times for a month. Add arrival time by hand at reception if the LIS does not hold it. Plot collection-to-receipt and arrival-to-accession by hour and site.

Week 3

Pick the three biggest waits

They will usually be a late-day transport tail, a reception queue and a rejection loop at one or two sites. Quantify each in minutes per day and specimens affected.

Week 4

Change the schedule before the system

Move a draw time, add a pickup, split a batch, send rejection data to a site. Measure the same timestamps for the next month. Only then decide whether an interface or an automation line is the next constraint.

The inspection-ready pre-analytical checklist from our CLIA vs CAP vs COLA guide covers the records side of the same ground.

Where DS2 fits

The pre-analytical layer, run as one operation

Four of the six bottlenecks above live outside the laboratory building, which is where DS2 works. We schedule mobile phlebotomists to the laboratory's timetable, build and replenish collection kits so the right tube and requisition are at the draw, run courier routes designed to stability windows with chain of custody and temperature data at every handoff, and connect orders, specimen events and results through LIS integration so nothing is re-keyed. The collection-to-receipt and rejection numbers in the scorecard above are the ones we report back.

Talk About Your Pre-Analytical FlowCourier RFP Template
FAQ

Laboratory workflow questions

What is a clinical laboratory workflow?

The sequence of steps from a clinician's decision to order a test to the clinician acting on the result: ordering, collection, identification, transport, accessioning and preparation, analysis, reporting, interpretation and action. The pre-analytical steps happen mostly outside the laboratory and account for the majority of errors and much of the elapsed time.

What are the three phases of laboratory testing?

Pre-analytical (everything from the order to the specimen being ready on the analyzer), analytical (the measurement) and post-analytical (validation, reporting and the clinician's use of the result). Studies of error distribution put 46% to 70% of errors in the pre-analytical phase, roughly 13% to 32% in the analytical phase and 18.5% to 47% in the post-analytical phase.

What is specimen accessioning?

Accessioning is the laboratory's intake step: the arriving specimen is matched to its order, checked against acceptability criteria, assigned a unique accession number, labeled with barcodes and entered into the laboratory information system. CLIA requires the laboratory to document the date and time it receives each specimen and to keep that in the test record. In interfaced systems, where orders arrive electronically and specimens carry known barcodes, most of the manual work is eliminated.

What does a specimen accessioner do?

Receives and unpacks specimens, checks that each one matches a requisition or electronic order and meets acceptability criteria, resolves discrepancies such as missing information or mismatched identifiers, assigns accession numbers and labels, enters the specimen into the LIS, routes it to the right bench and logs anything rejected. It is detail work under time pressure, and it is where many pre-analytical errors are either caught or committed.

What causes most pre-analytical errors?

Mishandling during collection, handling, preparation, transport and storage: wrong or underfilled tubes, hemolysis from difficult draws or rough transport, mislabeling, missing requisition information, delayed centrifugation and temperature excursions. Hemolysis alone accounts for 40% to 70% of unsuitable specimens, and identification errors are the ones most likely to harm a patient.

How can a lab reduce turnaround time without new analyzers?

Start outside the laboratory. Align draw times with when results are needed, deliver specimens as they are collected, redesign courier routes to stability windows, receive specimens one piece at a time instead of in coolers, timestamp arrival separately from accession, and move orders and results onto interfaces. The published Lean studies achieved their turnaround gains largely in the pre-analytical phase, often with no new equipment.

What is a good specimen rejection rate?

US multi-institution studies report 0.3% to 0.35% for chemistry and outpatient specimens, with the best-performing laboratories under 0.1%; a 2023 meta-analysis found 0.55% across studies from the Americas and 1.99% worldwide. The number matters less than the trend by reason and site, because that is what tells you which collector, kit or route to fix.

Is lab workflow optimization a software project?

Not first. An LIS, middleware or automation line processes what reaches it; it cannot move a cutoff that lands specimens after the shift, or stop a site sending hemolyzed samples. Map the flow, fix the schedule, kits, transport and handoffs, and then automate the steps that remain. Interfaces that remove re-keying are the exception: they are both a workflow fix and a software one.

What is a lean laboratory?

One that has organized its work around flow rather than batches: specimens move continuously through receipt, preparation and analysis with standard work at each step, visual controls, small batch sizes and a habit of measuring waits and removing them. The published results include pre-analytical processing cut from 29 to 19 minutes, creatinine turnaround from 54 to 23 minutes, and molecular test turnaround from 2.7 to 1.5 days.

References

Figures are quoted from the cited abstracts and open-access texts and checked November 2026. Several CAP Q-Probes studies are 20 to 30 years old; they remain the largest multi-institution datasets on these questions.

  1. 1.Carraro P, Plebani M. Errors in a stat laboratory: types and frequencies 10 years later. Clin Chem. 2007;53(7):1338-1342. Of 160 confirmed errors, 61.9% were pre-analytical, 15% analytical and 23.1% post-analytical.
  2. 2.Plebani M. Errors in clinical laboratories or errors in laboratory medicine? Clin Chem Lab Med. 2006;44(6):750-759. Pre-analytical factors account for 46% to 68.2% of total errors.
  3. 3.Bonini P, Plebani M, Ceriotti F, Rubboli F. Errors in laboratory medicine. Clin Chem. 2002;48(5):691-698. Most errors occur in the pre- or post-analytical phases; the analytical share is 13% to 32% across studies.
  4. 4.Lippi G, Chance JJ, Church S, et al. Preanalytical quality improvement: from dream to reality. Clin Chem Lab Med. 2011;49(7):1113-1126. Pre-analytical errors account for nearly 60% to 70% of all problems in laboratory diagnostics.
  5. 5.Plebani M. Quality indicators to detect pre-analytical errors in laboratory testing. Clin Biochem Rev. 2012;33(3):85-88. Lundberg's brain-to-brain loop and the sources of pre-analytical error.
  6. 6.Lundberg GD. Acting on significant laboratory results. JAMA. 1981;245(17):1762-1763. The original brain-to-brain loop; revisited in Plebani M, Laposata M, Lundberg GD. Am J Clin Pathol. 2011;136(6):829-833.
  7. 7.Hawkins RC. Laboratory turnaround time. Clin Biochem Rev. 2007;28(4):179-194. Definitions of turnaround time, the gap between how laboratories and clinicians measure it, and the case for outlier rates over means.
  8. 8.Howanitz PJ, Steindel SJ, Cembrowski GS, Long TA. Emergency department stat test turnaround times: a CAP Q-Probes study for potassium and hemoglobin. Arch Pathol Lab Med. 1992;116(2):122-128.
  9. 9.Steindel SJ, Howanitz PJ. Physician satisfaction and emergency department laboratory test turnaround time: a CAP Q-Probes study of 690 laboratories. Arch Pathol Lab Med. 2001;125(7):863-871.
  10. 10.Steindel SJ, Jones BA. Routine outpatient laboratory test turnaround times and practice patterns: a CAP Q-Probes study of 614 laboratories. Arch Pathol Lab Med. 2002;126(1):11-18.
  11. 11.Dale JC, Steindel SJ, Walsh M. Early morning blood collections: a CAP Q-Probes study of 657 institutions. Arch Pathol Lab Med. 1998;122(10):865-870.
  12. 12.Cornes MP, Danks G, Elgaddal S, et al. Early availability of laboratory results increases same day ward discharge rates. Clin Chem Lab Med. 2018;56(11):1864-1869.
  13. 13.Procop GW, Nelson SK, Blond BJ, Souers RJ, Massie LW. The impact of transit times on the detection of bacterial pathogens in blood cultures: a CAP Q-Probes study of 36 institutions. Arch Pathol Lab Med. 2020;144(5):564-571.
  14. 14.Stotler BA, Kratz A. Determination of turnaround time in the clinical laboratory: accessioning-to-result time does not always accurately reflect laboratory performance. Am J Clin Pathol. 2012;138(5):724-729.
  15. 15.Nakhleh RE, Zarbo RJ. Surgical pathology specimen identification and accessioning: a CAP Q-Probes study of 1,004,115 cases from 417 institutions. Arch Pathol Lab Med. 1996;120(3):227-233.
  16. 16.Baron JM, Dighe AS. Computerized provider order entry in the clinical laboratory. J Pathol Inform. 2011;2:35. Describes accessioning and the effect of interfaced orders.
  17. 17.42 CFR 493.1242: written policies for specimen collection, labeling, transport, processing and acceptability; the laboratory must document the date and time it receives a specimen (paragraph (b)). 42 CFR 493.1283: test records, including date and time of specimen receipt and the condition and disposition of rejected specimens.
  18. 18.42 CFR 493.1241: test request; the elements a requisition must solicit, and accurate transcription into the record system.
  19. 19.Persoon TJ, Zaleski S, Frerichs J. Improving preanalytic processes using the principles of lean production (Toyota Production System). Am J Clin Pathol. 2006;125(1):16-25.
  20. 20.Rutledge J, Xu M, Simpson J. Application of the Toyota Production System improves core laboratory operations. Am J Clin Pathol. 2010;133(1):24-31.
  21. 21.Inal TC, Goruroglu Ozturk O, Kibar F, et al. Lean six sigma methodologies improve clinical laboratory efficiency and reduce turnaround times. J Clin Lab Anal. 2018;32(1):e22180.
  22. 22.Cankovic M, Varney RC, Whiteley L, et al. The Henry Ford Production System: Lean process redesign improves service in the molecular diagnostic laboratory. J Mol Diagn. 2009;11(5):390-399.
  23. 23.Cherie N, Berta DM, Tamir M, et al. Improving laboratory turnaround times in clinical settings: a systematic review of the impact of lean methodology application. PLoS One. 2024;19(10):e0312033.
  24. 24.Villa D. Automation, Lean, Six Sigma: synergies for improving laboratory efficiency. J Med Biochem. 2010;29(4):339-348.
  25. 25.Letelier P, Guzmán N, Medina G, et al. Workflow optimization in a clinical laboratory using Lean management principles in the pre-analytical phase. J Med Biochem. 2021;40(1):26-32.
  26. 26.Jones BA, Calam RR, Howanitz PJ. Chemistry specimen acceptability: a CAP Q-Probes study of 453 laboratories. Arch Pathol Lab Med. 1997;121(1):19-26.
  27. 27.Dale JC, Novis DA. Outpatient phlebotomy success and reasons for specimen rejection: a CAP Q-Probes study of 210 institutions. Arch Pathol Lab Med. 2002;126(4):416-419.
  28. 28.Karcher DS, Lehman CM. Clinical consequences of specimen rejection: a CAP Q-Probes analysis of 78 clinical laboratories. Arch Pathol Lab Med. 2014;138(8):1003-1008; data also reported in CAP Today, July 2012.
  29. 29.Getawa S, Aynalem M, Melku M, Adane T. Blood specimen rejection rate in clinical laboratory: a systematic review and meta-analysis. Pract Lab Med. 2023;33:e00303.
  30. 30.Lippi G, Blanckaert N, Bonini P, et al. Haemolysis: an overview of the leading cause of unsuitable specimens in clinical laboratories. Clin Chem Lab Med. 2008;46(6):764-772.
  31. 31.McCaughey EJ, Vecellio E, Lake R, et al. Key factors influencing the incidence of hemolysis: a critical appraisal of current evidence. Crit Rev Clin Lab Sci. 2017;54(1):59-72.
  32. 32.Eker P. Direct cost analysis for 32,783 samples with preanalytical phase errors. North Clin Istanb. 2022;9(4):391-400.
  33. 33.Hjelmgren H, Heintz E, Ygge BM, Andersson N, Nordlund B. Direct costs of blood drawings with pre-analytical errors in tertiary paediatric hospital care. PLoS One. 2023;18(8):e0290636.
  34. 34.Valenstein PN, Raab SS, Walsh MK. Identification errors involving clinical laboratories: a CAP Q-Probes study of patient and specimen identification errors at 120 institutions. Arch Pathol Lab Med. 2006;130(8):1106-1113.
  35. 35.Wagar EA, Stankovic AK, Raab S, Nakhleh RE, Walsh MK. Specimen labeling errors: a Q-Probes analysis of 147 clinical laboratories. Arch Pathol Lab Med. 2008;132(10):1617-1622.
  36. 36.Snyder SR, Favoretto AM, Derzon JH, et al. Effectiveness of barcoding for reducing patient specimen and laboratory testing identification errors: a Laboratory Medicine Best Practices systematic review and meta-analysis. Clin Biochem. 2012;45(13-14):988-998; CDC Laboratory Medicine Best Practices summary.
  37. 37.Hayden RT, Patterson DJ, Jay DW, et al. Computer-assisted bar-coding system significantly reduces clinical laboratory specimen identification errors in a pediatric oncology hospital. J Pediatr. 2008;152(2):219-224.
  38. 38.Mekhjian HS, Kumar RR, Kuehn L, et al. Immediate benefits realized following implementation of physician order entry at an academic medical center. J Am Med Inform Assoc. 2002;9(5):529-539.
  39. 39.HL7 International: Version 2 messaging (ORM, OML and ORU messages) and the FHIR ServiceRequest and DiagnosticReport resources; HL7 Version 2 to FHIR implementation guide message maps.
  40. 40.Sacks DB, Arnold M, Bakris GL, et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin Chem. 2023;69(8):808-868. Glycolysis lowers glucose by about 5% to 7% per hour in unseparated blood.
  41. 41.Reuter T, Müller M, Stelter F, Durner J, Kramer J. Time as a significant factor in the release of potassium from lithium heparin plasma and serum. PLoS One. 2024;19(12):e0313572.
  42. 42.Harper A, Lee W, Cook A, et al. Reproducibility of serum potassium values in serum from blood samples stored for increasing times prior to centrifugation and analysis. J Clin Lab Anal. 2016;30(3):244-247; and Sinclair D, Briston P, Young R, Pepin N. Seasonal pseudohyperkalaemia. J Clin Pathol. 2003;56(5):385-388.
  43. 43.Oddoze C, Lombard E, Portugal H. Stability study of 81 analytes in human whole blood, in serum and in plasma. Clin Biochem. 2012;45(6):464-469; and Zhang DJ, Elswick RK, Miller WG, Bailey JL. Effect of serum-clot contact time on clinical chemistry laboratory results. Clin Chem. 1998;44(6):1325-1333.
  44. 44.CLSI PRE04, Handling, Transport, Processing, and Storage of Blood Specimens for Routine Laboratory Examinations, 1st edition (2023), which replaced GP44-A4; CLSI PRE06, Evaluation of External Transport Systems, 1st edition (2026).
  45. 45.Zaninotto M, Tasinato A, Padoan A, et al. An integrated system for monitoring the quality of sample transportation. Clin Biochem. 2012;45(9):688-690; and Effects of sample transportation on commonly requested laboratory tests. Clin Chem Lab Med. 2012;50(10):1755-1760.
  46. 46.College of American Pathologists, Laboratory General Checklist (2025 edition): GEN.40100 Specimen Collection Manual Elements, GEN.40460 Specimen Collection Supplies, GEN.40530 Specimen Tracking.

Where does your turnaround go before the bench? Let's map it.

Tell us your sites, volumes and the tests that matter most. We will come back with the collection, kitting and transport options and the numbers to measure them by. No commitment; we reply within 24 business hours.

Request a Consultation

or download the pre-analytical checklist