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Improving Diagnostic Turnaround Time and Analytical Reliability in
Clinical Biochemistry Laboratories: A Quality Management
Framework
Alasi Bashirat Ololade
No 2, Adeniyi Close, behind SMA, New Bodija Estate, Ibadan
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.150600243
Received: 12 July 2026; Accepted: 17 July 2026; Published: 01 August 2026
ABSTRACT
Clinical biochemistry laboratories play a key role in diagnosis, triage, therapy monitoring and patient-flow
decisions. Ongoing delays and inconsistent test reliability weaken their clinical value and cannot be corrected
simply by increasing analyser speed. These problems often arise at poorly managed interfaces across the total
testing process, including test requesting, patient identification, specimen collection and transport, accessioning,
centrifugation, analysis, validation and result reporting.
Objective: To create a comprehensive quality-management framework that shortens diagnostic turnaround time
(TAT) while maintaining analytical reliability in clinical biochemistry laboratories.
Methods: A narrative review was conducted using international laboratory quality standards, consensus
guidance and peer-reviewed research on TAT, quality indicators, risk-based control, sigma metrics,
autoverification and the total testing process.
Findings: Sustainable improvement depends on defining TAT from the user perspective, decomposing it into
measurable components, redesigning bottleneck-prone pre- and post-analytical steps, and applying analytical
controls matched to method risk, clinical purpose and performance. In one published single-institution blood-
gas implementation, rule-based autoverification reduced TAT from 27 to 18 minutes while the reported
verification error rate fell from 2.0% to 0.05%, illustrating that speed and reliability can improve together when
safeguards are validated and monitored. This review offers an integrated framework linking TAT metrics, risk
controls and quality-management-system governance across the full testing cycle.
Conclusion: A high-quality clinical biochemistry laboratory is not merely fast. It is a well-controlled socio-
technical system that produces timely, traceable, clinically interpretable and analytically reliable results. Quality
management provides the governance needed to improve speed and reliability simultaneously rather than trading
one against the other.
Keywords: clinical biochemistry; turnaround time; analytical reliability; ISO 15189; quality indicators; risk-
based quality control; total testing process; laboratory quality management; autoverification
INTRODUCTION
The clinical biochemistry laboratory has become one of the most information-dense units in modern healthcare.
Electrolytes, renal profiles, liver function tests, cardiac biomarkers, glucose indices, endocrine assays,
therapeutic drug monitoring and emergency chemistry panels inform diagnosis and treatment across emergency
medicine, intensive care, surgery, obstetrics, oncology, nephrology and primary care. When a potassium result
is delayed, when a creatinine value is analytically unreliable, or when a critical result is not communicated
promptly, the laboratory failure is no longer an internal technical inconvenience; it becomes a clinical-risk event
[5-7].
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Turnaround time is often restricted to analyser processing time or to the interval between laboratory receipt and
technical validation. This restricted definition is operationally inadequate because it ignores major pre-analytical
and post-analytical sources of diagnostic delay. From the patient and clinician perspective, TAT is the interval
between the clinical decision to request a test and the moment an actionable result is available. In practice, order-
entry errors, specimen collection delays, transport batching, centrifugation queues, sample rejection, delayed
validation, information-system downtime and poor critical-result communication may dominate the true
diagnostic delay [5,6,8,9].
Analytical reliability is equally vulnerable to oversimplification. Daily internal quality control alone does not
prove that the laboratory is reliable. Reliability requires method verification, calibration traceability, internal
quality control, external quality assessment, lot-to-lot verification, maintenance, staff competence, risk
assessment, nonconformity management, corrective action and continual improvement [1-3]. A quality-
management system (QMS) is therefore not an administrative ornament for accreditation visits. It is the operating
logic of a safe diagnostic service.
This article argues that TAT and analytical reliability should not be managed as competing objectives. Poorly
designed attempts to reduce TAT can encourage premature release of results, inadequate governance of repeat
testing, or weakened review of abnormal findings. Conversely, excessive analytical conservatism can delay the
delivery of clinically urgent results. A mature QMS resolves this tension by defining acceptable risk, designing
standardised workflows, using quality indicators and adopting risk-based quality control that matches the clinical
consequence of error [1,3,7,8]. The contribution of this review is an integrated framework that connects TAT
measurement, analytical risk control, and QMS governance across the entire testing process.
Picture 1. Original illustration of a quality-managed clinical biochemistry workflow linking barcoded specimens,
automated analysis, QC acceptance and LIS-based TAT monitoring.
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Methods: narrative review approach
This manuscript was developed as a narrative review and practice-oriented quality-improvement framework.
Relevant literature was identified through searches of PubMed, Google Scholar, Scopus/Web of Science and
institutional guideline repositories using combinations of the terms clinical biochemistry, turnaround time,
laboratory quality indicators, ISO 15189, analytical quality, risk-based quality control, sigma metrics,
autoverification and total testing process. The literature window prioritised publications from 2007 to 2025,
while retaining foundational international standards and guidance documents regardless of publication year.
Priority was given to international standards, consensus guidance and peer-reviewed studies addressing TAT,
analytical reliability and laboratory quality management. ISO 15189:2022, the WHO Laboratory Quality
Management System handbook, CLSI EP23 guidance and IFCC quality-indicator work were used as core
normative sources [1-4,7,8]. Empirical and review literature on laboratory TAT, pre-analytical and post-
analytical quality indicators, sigma metrics, automation and benchmarking was used to translate these principles
into an operational framework [5-13]. Sources that focused exclusively on non-clinical laboratory systems,
research-only assay development or unvalidated local opinions without transferable quality-management content
were not prioritised. Because the purpose was to build a clinically usable QMS framework rather than to estimate
pooled intervention effects, no meta-analysis was performed. Instead, the synthesis organised the literature
around five questions: how should diagnostic TAT be defined, which phases generate measurable delay, which
analytical controls protect the reliability of results, how can speed and reliability be governed together, and which
indicators should be used to verify sustained improvement.
Screening transparency: the original narrative search was not prospectively registered, and database exports were
not retained in a form that permits a reliable reconstruction of the total number of records initially identified. For
this revision, the 13 full-text sources in the working evidence set were re-screened against the stated eligibility
criteria, and all 13 were included in the final synthesis: four standards or consensus resources and nine peer-
reviewed publications. No meta-analysis or formal risk-of-bias assessment was undertaken. These figures should
therefore be interpreted as an audit of the evidence actually used in the manuscript, not as a PRISMA-style
systematic-review flow.
Conceptual basis: quality management as a diagnostic-control system
A useful way to conceptualise a clinical biochemistry laboratory is as a diagnostic-control system. Inputs are test
requests and patient specimens; processes include pre-analytical, analytical, and post-analytical activities;
outputs are validated diagnostic results; feedback is provided through quality indicators, internal audits, external
quality assessments, incident reviews, user complaints, and management reviews. Without feedback, the
laboratory merely performs tests. With feedback, the laboratory learns. ISO 15189:2022 emphasises competence,
risk, impartiality, patient focus and the management system needed to sustain valid examination results [1]. The
WHO LQMS model similarly organises laboratory quality around coordinated essentials such as personnel,
equipment, purchasing and inventory, process control, documents and records, occurrence management,
assessment and continual improvement [2]. These are not simply accreditation headings. Each one maps directly
onto failures that delay results or damage analytical credibility.
For example, equipment management affects both reliability and TAT through preventive maintenance,
downtime planning, backup analysers, service-level agreements and calibration schedules. Personnel
management affects the quality of results through competency assessment and authorisation to perform or
validate tests. Process control governs collection instructions, sample acceptance criteria, QC rules, auto-
verification protocols, and corrective action thresholds. The QMS is therefore the architecture through which
laboratory speed becomes safe speed.
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Figure 1. Quality-managed total testing process with explicit timestamps for component-level TAT control. The
timestamp structure separates total TAT, intra-laboratory TAT and phase-specific TAT so that delay can be
located rather than averaged away.
Table 1. Quality-management system elements and their practical relevance to TAT and analytical reliability.
QMS element
Operational contribution
Failure mode controlled
Leadership and
governance
Defines TAT policy, escalation rules, quality
objectives, risk appetite and accountability for
delayed or unreliable results.
Conflicting priorities, poor ownership of
delays and weak incident learning.
Personnel
competence
Ensures staff are trained, assessed, authorised
and reassessed for collection, analysis,
validation and critical-result communication.
Mislabeling, wrong-container errors, unsafe
manual dilution and inconsistent result review.
Equipment and
maintenance
Controls analyser uptime, calibration status,
preventive maintenance, backup capacity and
downtime protocols.
Unexpected downtime, extended queues, QC
failure and reagent instability.
Process control
Standardises specimen acceptance,
centrifugation, prioritisation, QC, validation
and repeat-testing logic.
Uncontrolled variation, rework, unnecessary
repeats and delayed verification.
Information
management
Links HIS, LIS, middleware, analyser
interfaces, autoverification, alerts, dashboards
and audit trails.
Lost requests, transcription errors, delayed
report release and weak traceability.
Occurrence
management
Captures nonconformities, performs root-cause
analysis, assigns corrective action and verifies
effectiveness.
Repeated failures disguised as isolated
mistakes.
Internal audit and
management review
Tests whether the QMS works in practice and
whether indicators are improving.
Accreditation compliance without operational
improvement.
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Turnaround time: definition, measurement and managerial control
The first error in many laboratories is to measure the wrong TAT. If a laboratory reports only analyser TAT or
laboratory-reception-to-validation TAT, it may show acceptable internal performance while clinicians continue
to experience unacceptable diagnostic delay. A serious laboratory should define at least three TAT levels: total
TAT from request to result availability, intra-laboratory TAT from specimen receipt to report release, and phase-
specific TAT for pre-analytical, analytical and post-analytical subprocesses [5,6].
The second error is over-reliance on averages. Mean TAT is insensitive to patients who experience the longest
delays. A laboratory may have an acceptable average while its 90th- or 95th-percentile TAT is clinically
unacceptable. Quality management therefore requires median TAT, percentile TAT, breach rate and
stratification by priority, ward, test group, time of day, analyser and sample route [5,6,12]. A service that cannot
identify the source of the delay cannot credibly claim to be improving.
TAT targets must also differ by clinical context. Emergency results for potassium, glucose, lactate, or troponin
require targets that reflect acute diagnostic and treatment decisions. In contrast, routine lipid profiles or stable
chronic disease monitoring tests may tolerate longer turnaround times. Therefore, clinical urgency, test purpose,
patient location and risk of delayed action should determine the service-level matrix. A single TAT target for all
chemistry tests is administratively simple but clinically crude.
The third error is to ignore demand variability. Clinical biochemistry demand is not uniform across the day.
Morning inpatient collections, outpatient peaks, emergency department surges, analyser maintenance windows,
staff handovers, and transport schedules create queueing effects. Improvement, therefore, requires not only faster
equipment but also flow design: staggered collection, priority lanes for urgent specimens, transport redesign,
centrifuge capacity matching, middleware autoverification, and clear escalation rules when TAT begins to
deteriorate [6,11].
Table 2. Recommended TAT indicators for clinical biochemistry laboratories.
Indicator
Definition
Suggested measurement
Total TAT
Time from electronic test
request to the authorised
clinician's result availability.
Median, 90th percentile and
breach rate by location and
priority.
Pre-analytical
TAT
Request for specimen receipt in
the laboratory, with optional
sub-steps for collection and
transport.
Order-to-collection,
collection-to-receipt and
rejected-specimen rate.
Analytical TAT
Specimen receipt or loading to
analytical completion, including
centrifugation and QC
interruptions.
Receipt-to-analysis, analyser
queue time, QC-failure delay
and repeat rate.
Post-analytical
TAT
Analytical completion to
authorised result release and
critical-value communication.
Autoverification rate,
validation delay and critical-
result notification time.
TAT breach rate
Percentage of specimens or test
requests exceeding the agreed
service target.
Number delayed divided by
number eligible, reported
weekly or monthly.
Analytical reliability: beyond daily quality control
Analytical reliability means that a reported result is sufficiently accurate, precise, traceable and clinically fit for
its intended use. The phrase “fit for intended use” is important. A sodium result in a critically ill patient, a
troponin result in chest pain triage, and an HbA1c result for long-term diabetes monitoring do not carry the same
immediate clinical risk. Quality control should therefore be matched to clinical consequence and method
vulnerability rather than applied as a ritual [1,3,10]. A robust analytical-reliability programme begins before
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routine testing. Method selection and verification should assess the intended clinical use, manufacturer
performance claims, local precision, bias, linearity, analytical measuring range, reportable range, carryover,
interference, reagent stability, calibration traceability, reference intervals, decision limits, and measurement
uncertainty, where relevant [1,3]. New methods should not be released simply because the analyser has been
installed and the supplier has provided training. Verification must demonstrate local performance under local
conditions.
Routine reliability control then requires an integrated package: internal QC, external quality assessment or
proficiency testing, calibration verification, lot-to-lot comparison, temperature monitoring, maintenance records,
reagent inventory control, operator competence, delta checks, autoverification rules and incident review [1-
3,7,8]. Risk-based QC, as reflected in CLSI EP23 principles, asks what can go wrong, how likely it is, how
harmful it would be and what control measure is proportionate [3]. Sigma metrics can help rationalise QC rules
and frequency, but they should be interpreted within clinical risk and method context rather than used
mechanically [10].
Table 3. Analytical-reliability metrics and their quality-management use.
Metric
Meaning
Typical failure detected
Quality-management
action
Bias
Systematic deviation from a
reference or peer-comparison
value.
Calibration error, lot
change or method
interference.
Trend EQA/PT and patient
medians; investigate
persistent directionality.
Imprecision
Random analytical variation,
usually expressed as the
coefficient of variation.
Pipetting variation,
unstable reagent or
instrument noise.
Monitor internal QC CV by
analyte and level; compare
with allowable
imprecision.
Linearity/AMR
verification
Evidence that the method
performs across the clinically
relevant measuring range.
Nonlinearity, dilution error
or inappropriate reporting
beyond the verified range.
Verify analytical
measuring range and define
repeat/dilution rules.
Total error
Combined effect of bias and
imprecision against a defined
allowable error.
Multiple small defects that
accumulate into a clinical
error.
Use allowable total error or
biologically derived
specifications where
available.
Sigma metric
Performance index relating
allowable error, bias and
imprecision.
Over- or under-controlled
QC if rules are not matched
to performance.
Use sigma to rationalise
QC frequency, rules and
number of control
measurements.
Patient-result
plausibility
Clinical and longitudinal
checks on released results.
Wrong specimen, dilution
error or ignored analyser
flag.
Use delta checks, critical-
value rules and review
thresholds.
Integrating speed and reliability: a practical QMS framework
The proper objective is not simply faster results. The objective is the faster release of correct, traceable and
clinically actionable results. The QMS must therefore impose a sequence of controls that remove waste without
removing safeguards. The proposed framework has six connected components: define service requirements,
measure the process, diagnose dominant failure modes, redesign bottlenecks, verify that analytical validity has
been protected and sustain gains through governance.
First, service requirements should be negotiated with clinical users. Emergency, ICU, outpatient, ward-round
and chronic-disease monitoring contexts may require different TAT targets. The laboratory should document
which tests are urgent, which specimens have priority routing and which results require immediate
communication. An urgent label that applies to everything is not a useful risk-control category. Second,
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laboratories should map the total testing process in sufficient detail to identify controllable delays. A high-level
process map is useful for governance, but operational improvement requires timestamps for the following:
request, collection, receipt, centrifugation, loading, analytical completion, validation, authorisation, and clinician
notification. The LIS and middleware should be configured to capture these points wherever possible.
Third, analytical safeguards should be redesigned intelligently rather than relaxed. Autoverification is not a
shortcut for weak staff review; it is a rule-based control system that releases results only when defined quality
criteria are met. Suitable criteria include analyser flags, QC status, critical values, delta checks, analytical
measuring range, sample indices, panic limits and consistency with related analytes [11].
Fourth, improvement must be tested through Plan-Do-Study-Act (PDSA) cycles. New workflows should be
preceded by baseline measurement, tested under controlled conditions, evaluated using pre-specified indicators
and reviewed for unintended consequences. Reduced TAT accompanied by increased sample rejection, QC
failure, or amended reports is not an improvement; it is risk displacement.
Figure 2. Fishbone analysis of common root causes of delayed TAT and unreliable results. The categories are
ordered as People, Process and Equipment/IT above the spine, and Specimen, Measurement/QC and
Environment below the spine. Each branch should trigger measurable corrective actions.
Table 4. Six-step QMS framework for improving TAT and analytical reliability.
Step
Core activities
Expected output
1. Define
Agree clinical TAT targets, critical tests, rejection
policy, analytical quality specifications and escalation
rules.
Approved service-level matrix and risk register.
2. Measure
Capture timestamps and reliability indicators across
the total testing process.
Baseline dashboard with median TAT, 90th
percentile TAT, breach rate, QC failures, repeats
and amended reports.
3.
Diagnose
Use Pareto charts, process maps, fishbone analysis and
incident review to locate dominant failure modes.
Prioritised list of bottlenecks and reliability
hazards.
4. Redesign
Introduce barcoding, priority routing, centrifuge
scheduling, autoverification, backup testing and risk-
based QC.
Controlled intervention protocol with staff
training and change control.
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5. Verify
Compare pre- and post-intervention indicators and
check for unintended analytical or safety
consequences.
Evidence of reduced delay without increased
unreliability.
6. Sustain
Embed indicators into management review, audit,
competency assessment and corrective-action
tracking.
Monthly governance report and continuous-
improvement register.
Recommended interventions for clinical biochemistry laboratories
Pre-analytical interventions
Pre-analytical improvement should begin with demand and specimen quality. Electronic order sets should reduce
duplicate and inappropriate requests. Request forms and electronic test catalogues should specify sample type,
tube type, volume, fasting requirement, transport conditions, stability, and rejection criteria. Phlebotomy should
use positive patient identification and barcode labelling at the bedside or collection point. This is a central control
point for preventing wrong-patient and wrong-specimen errors [7-9]. Transport should be treated as a process,
not as an afterthought. Scheduled courier routes may be appropriate for routine outpatient work but may be
unsuitable for emergency specimens. Pneumatic-tube systems, where available and validated for the analytes in
question, may reduce transport time; however, the risk of haemolysis and specimen integrity must be monitored.
A transport intervention that shortens time while increasing haemolysis should not be considered successful.
Analytical interventions
Analytical improvements require capacity discipline. Laboratories should match analyser throughput, centrifuge
capacity, staffing, maintenance windows and QC schedules to known demand. Performing QC, maintenance or
reagent replacement during predictable demand peaks is a preventable scheduling failure that reflects weak
alignment between operational planning and demand patterns. Risk-based QC should be adopted for major
chemistry assays. Analytes with high sigma performance may be controlled with simpler rules and a lower false-
rejection burden. In comparison, assays with weaker performance require stricter rules, more frequent controls,
or method improvements [3,10]. The laboratory should document the rationale rather than applying the same
multirule package to every analyte. The goal is to detect medically important errors with acceptable operational
efficiency.
Post-analytical interventions
Post-analytical delay is frequently underestimated because the sample has already been analysed. Middleware
autoverification can reduce avoidable manual review when rules are validated and periodically audited.
However, autoverification must exclude results with analyser flags, unacceptable QC status, critical values
requiring communication, clinically implausible patterns, sample-index problems or delta-check failures [11].
Critical-result communication should be governed by a written policy defining analytes, thresholds, responsible
staff, notification route, read-back requirements, documentation and escalation if the clinician cannot be reached.
A critical potassium result that is passively available in an electronic record has not necessarily been
communicated. The laboratory must be able to prove who was contacted, when, by whom and what was
communicated.
Table 5. Practical interventions and evaluation indicators.
Intervention
Main phase
Primary failure targeted
Evaluation indicator
Specimen barcoding
at collection
Pre-analytical
Mislabeling, reception delays
and manual transcription.
Mislabeling rate; specimen-
reception processing time.
Priority lanes for
urgent chemistry
Pre-/analytical
Urgent specimens trapped in
routine batch flow.
Urgent median and 90th
percentile TAT.
Centrifuge schedule
redesign
Analytical
Batching delay before analyser
loading.
Receipt-to-load time; queue
length by hour.
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Middleware
autoverification
Post-analytical
Manual validation backlog.
Autoverification rate;
amended-report rate;
validation delay.
Risk-based QC plan
Analytical
QC false rejection or missed
medically important error.
QC rejection rate; EQA
performance; sigma metrics.
Critical-result
escalation protocol
Post-analytical
Delayed communication of
life-threatening results.
Critical-result notification
time; failed-contact rate.
Dashboard and
management review
All phases
Improvement not sustained or
invisible to leadership.
Monthly indicator trend;
corrective-action closure
rate.
Figure 3. Illustrative quality-indicator dashboard. Values are example values only and must be replaced by local
baseline data, test menu, urgency categories and agreed clinical targets before institutional use or publication.
Implementation model for a tertiary clinical biochemistry laboratory
A practical implementation model starts with gathering data. During the first 30 days, establish a baseline
dashboard that tracks key metrics such as total TAT, intra-laboratory TAT, 90th-percentile TAT, urgent TAT
breaches, sample rejection rate, haemolysis rate, QC rejection rate, calibration failures, repeat-analysis rate, EQA
performance, amended reports, and critical-value notification time. This data should be broken down by location,
test group, urgency, shift, and analyser because aggregated data can hide local issues [7-9,12]. The following 60
to 90 days should target two or three main bottlenecks. Trying multiple improvements at once can prevent
accurate measurement. If specimen receipt from wards is slow, focus on redesigning transport and phlebotomy.
If result validation causes delays, improve autoverification and staffing patterns. If QC failures are common,
examine method performance and reagent management before blaming staff. Afterwards, the QMS should
embed continuous improvement through monthly quality meetings, internal audits, verification of corrective
actions, competency reviews, and annual management evaluations. The key question for leadership is not
whether a dashboard exists, but whether it influences decision-making.
Table 6. Twelve-month implementation roadmap expanded into an implementable governance tool.
Period
Focus
Key activities
Responsible
owner
Data source
Review
frequency
Deliverable
Month
1
Baseline
mapping and
Confirm TAT
definitions; extract
LIS timestamps;
Quality
manager with
LIS,
middleware,
Weekly
during
baseline
Validated
baseline
dashboard.
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timestamp
validation
audit sample
rejection, QC
events, and
critical-result
notifications.
laboratory
lead
QC records,
incident log
Months
2-3
Bottleneck
intervention
Implement two
high-yield
changes, such as
priority routing,
centrifuge
scheduling, or
autoverification.
Section lead
and
operations
supervisor
Timestamp
dashboard
and staff rota
Fortnightly
Pilot report
comparing
baseline and
intervention
periods.
Months
4-6
Analytical-
risk
strengthening
Create risk-based
QC plans for high-
volume or high-
risk analytes and
review EQA/PT
results and sigma
performance.
Senior
scientist and
quality
manager
IQC,
EQA/PT,
calibration
and lot
records
Monthly
Documented
QC plans and
method-risk
register.
Months
7-9
Scale and
standardise
Expand successful
interventions to
additional sections
and shifts; revise
SOPs and provide
staff training.
Laboratory
manager and
training lead
SOPs,
competency
records and
KPI
dashboard
Monthly
Revised SOPs,
competency
records and
sustained KPI
improvement.
Months
10-12
Governance
and
accreditation
alignment
Integrate
indicators into
internal audit,
management
review and quality
objectives.
Laboratory
director and
quality
committee
Audit reports,
management
review
minutes, KPI
trends
Quarterly
Annual quality
report and
next-cycle
improvement
plan.
Published single-institution case example
Wu et al. implemented CLSI-guided autoverification rules for arterial blood-gas testing at the First Affiliated
Hospital of Shantou University Medical College. They validated the rules in a live environment using 1,248
patient results before go-live. The autoverification pass rate was 75.5%. Reported TAT fell by 33.3%, from 27
to 18 minutes, while the verification error rate decreased from 2.0% before implementation to 0.05% afterwards;
agreement with senior-technician verification was high (κ = 0.92, p < 0.01) [13]. Although this was a single-
institution blood-gas service rather than an entire biochemistry department, it demonstrates the framework’s
central claim: time savings should be assessed together with reliability safeguards. The intervention combined
predefined QC, instrument-flag, range, delta-check and logic rules with live validation, exception routing and
continued monitoring rather than merely removing manual review.
DISCUSSION
The central argument of this review is that TAT and analytical reliability improve when laboratories manage the
total testing process as a controlled system. This conclusion is not technologically fashionable, but it is
operationally correct. Many laboratories pursue isolated technological fixes while leaving poor governance
untouched. Automation can be valuable, but when applied to poorly defined workflows, it may accelerate poor
practice. The correct sequence is process definition, risk assessment, workflow redesign, validation and then
automation [12,11]. Evidence from laboratory quality-indicator literature shows that errors and delays are not
confined to the analytical bench. Pre-analytical and post-analytical processes remain major sources of
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vulnerability because they span professional boundaries among clinicians, nurses, phlebotomists, transport staff,
reception staff, biomedical scientists, pathologists, and information-system teams [7-9]. The QMS is useful
precisely because it creates ownership across these boundaries.
Clinical users frequently demand shorter TAT, but the laboratory must respond scientifically. Some delays are
a waste and should be eliminated. Other delays are protective controls and should be redesigned rather than
removed. Repeating analysis after a flagged result, a delta-check review, or a critical-value read-back may
lengthen a single result pathway but reduce patient risk. The mature laboratory distinguishes unnecessary waiting
from necessary verification. One important opportunity is using dashboards that combine time and reliability
indicators. A TAT dashboard alone may incentivise speed at any cost. A QC dashboard alone may encourage
analytical caution without service responsiveness. A balanced dashboard makes trade-offs visible: TAT, sample
rejection, haemolysis, QC rejection, autoverification rate, amended reports, EQA performance and critical-value
notification should be reviewed together.
LIMITATIONS
This article is a narrative, practice-oriented review rather than a systematic review. The search was not
prospectively registered, the complete number of originally identified and excluded records cannot be
reconstructed, and no formal risk-of-bias or certainty-of-evidence assessment was applied. Source selection may
therefore reflect author judgement, database coverage and publication bias, and the framework should not be
interpreted as a pooled estimate of intervention effectiveness. The single-institution case example demonstrates
feasibility but remains context-specific; laboratories should establish local baselines, validate rules prospectively
and monitor balancing measures such as sample rejection, QC failure, amended reports and critical-result
communication before wider implementation.
CONCLUSION
Improving diagnostic turnaround time and analytical reliability in clinical biochemistry laboratories is
fundamentally a quality-management problem. Faster analysers, larger test menus and electronic reporting are
useful only when embedded in a disciplined QMS that controls the total testing process. The laboratory must
define clinically meaningful TAT, measure component delays, remove bottlenecks, protect analytical validity
and govern improvement through indicators, audit, risk management and management review. The central
standard is simple: a result should be available when clinically needed and reliable enough to act upon. Any
laboratory unable to satisfy both conditions has not yet achieved diagnostic quality; it has merely produced
numbers.
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026
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