Laboratory Quality Indicators (QIs): KPIs, ISO 15189 & Quality Management Guide (2026)
Professional Laboratory Guide — 2026
Quality indicators are measurable tools that help medical laboratories evaluate the performance, safety, effectiveness, and reliability of the total testing process. A well-designed quality indicator program goes far beyond monitoring internal quality control. It measures what happens before testing, during analysis, after testing, and at critical points where laboratory processes interact with patients and clinical teams.
This comprehensive guide explains how medical laboratories can select, calculate, monitor, interpret, investigate, and improve laboratory quality indicators while integrating them into a modern quality management system.
Modern laboratory medicine depends on much more than producing analytically correct numbers. A result may be technically accurate yet still fail to benefit the patient if the wrong patient was identified, the specimen was hemolyzed, the result was delayed, a critical value was not communicated promptly, or a corrected report was required because of an avoidable error.
For this reason, laboratory quality management increasingly evaluates the entire testing process: pre-pre-analytical activities, specimen collection and transportation, analytical testing, result validation, reporting, communication, and post-post-analytical processes.
Quality indicators, commonly abbreviated as QIs, convert these processes into measurable performance data. When appropriately designed, they help laboratories identify weaknesses, compare performance over time, prioritize risk, evaluate corrective actions, and demonstrate continuous improvement.
Important: A quality indicator is not merely a number reported each month. It should be linked to a defined process, measurement method, denominator, target, review frequency, responsible personnel, investigation pathway, and improvement action when performance becomes unacceptable.
1. What Are Laboratory Quality Indicators?
A laboratory quality indicator is a measurable characteristic used to evaluate the performance of an important laboratory process. It converts an operational or quality-related event into numerical information that can be monitored over time.
Examples include the percentage of rejected specimens, percentage of hemolyzed serum or plasma samples, frequency of patient identification errors, proportion of laboratory reports completed within a defined turnaround time, rate of unacceptable internal quality control runs, frequency of corrected reports, and percentage of critical results communicated within the laboratory's established time requirement.
The major value of a quality indicator is that it transforms quality from an abstract concept into measurable evidence.
Simple example
If a chemistry laboratory received 20,000 specimens during one month and 100 specimens were rejected because they did not meet acceptance criteria, the laboratory can calculate a specimen rejection indicator rather than simply saying that "some specimens were rejected."
Specimen Rejection Rate = (Rejected Specimens ÷ Total Specimens Received) × 100
In this example:
(100 ÷ 20,000) × 100 = 0.5%
The value becomes more powerful when compared with previous months, individual departments, collection locations, specimen types, defined internal targets, and relevant external benchmarking programs where appropriate.
2. Quality Indicators vs Key Performance Indicators (KPIs)
The terms quality indicator and key performance indicator are sometimes used interchangeably, but they are not necessarily identical.
A quality indicator specifically measures an aspect of quality, safety, reliability, or performance. A KPI is generally a strategically important measure selected by management to evaluate whether an organization is achieving an important objective.
Therefore, many laboratory KPIs are also quality indicators, but a laboratory may collect dozens of QIs while selecting only a smaller group as management-level KPIs.
| Term Primary Purpose Example | ||
|---|---|---|
| Quality Indicator | Measure quality or safety of a laboratory process | Hemolyzed specimen rate |
| KPI | Measure strategically important laboratory performance | Emergency chemistry TAT compliance |
| Quality Objective | Define a desired improvement or achievement | Reduce specimen rejection by 20% |
| Target | Define the intended performance level | ≥95% of STAT potassium results within defined TAT |
3. Why Quality Indicators Matter in Medical Laboratories
Quality indicators provide a structured method for monitoring processes that cannot be fully evaluated by internal quality control materials or external proficiency testing alone.
Internal QC can demonstrate whether an analytical system is operating within established control limits, but it does not tell the laboratory whether specimens are being labeled correctly, whether blood cultures are contaminated during collection, whether potassium specimens are frequently hemolyzed, whether urgent results are reported on time, or whether clinicians receive critical values promptly.
Similarly, successful external quality assessment does not guarantee that the entire testing pathway is safe.
A comprehensive QI system supports several important laboratory objectives:
- Detection of recurring errors.
- Identification of high-risk processes.
- Measurement of laboratory performance over time.
- Evaluation of patient safety risks.
- Monitoring effectiveness of corrective actions.
- Identification of training needs.
- Comparison of collection sites or laboratory sections.
- Support for management review.
- Evidence of continual improvement.
- Objective evaluation of service quality.
Importantly, indicators allow laboratory management to identify deterioration before it becomes a major incident.
For example, if the monthly hemolysis rate gradually increases from 0.8% to 1.1%, then 1.5%, 2.0%, and eventually 2.8%, an effective monitoring system may detect the trend before the problem results in significant delays, recollections, complaints, or patient risk.
4. Quality Indicators Across the Total Testing Process
Laboratory quality should be evaluated across the complete testing pathway rather than only within the analytical instrument.
The total testing process can be conceptually divided into:
Pre-pre-analytical phase
This occurs before the specimen physically enters the laboratory workflow and may include test selection, test ordering, patient preparation, patient identification, specimen collection, and initial handling.
Pre-analytical phase
This includes specimen transport, reception, accessioning, centrifugation, aliquoting, storage, sample integrity evaluation, and preparation for analysis.
Analytical phase
This includes calibration, internal quality control, reagent and analyzer performance, analytical measurement, instrument flags, interference detection, method performance, and result validation processes.
Post-analytical phase
This includes verification, report generation, turnaround time, critical result communication, amended reports, and release of results.
Post-post-analytical phase
This may include interpretation, communication with clinical services, appropriate utilization of results, and the effectiveness of information delivered by the laboratory.
Key principle: The quality of a laboratory result depends on the entire pathway. Excellent analytical performance cannot compensate for an incorrectly identified patient or an unsuitable specimen.
5. Quality Indicators and ISO 15189:2022
ISO 15189:2022 specifies requirements for quality and competence in medical laboratories and is also applicable to point-of-care testing within its scope. A modern laboratory quality management system therefore requires laboratories to think beyond analyzer performance and systematically manage processes, risks, nonconformities, improvement activities, personnel competence, equipment, information, and patient-related processes.
Quality indicators provide useful objective evidence for evaluating whether laboratory processes remain effective.
A laboratory preparing for accreditation should not simply maintain a spreadsheet titled "KPIs." Instead, indicators should be incorporated into an organized quality management framework.
For each QI, the laboratory should ideally define:
- Indicator name.
- Purpose.
- Process being measured.
- Numerator.
- Denominator.
- Calculation formula.
- Data source.
- Frequency of data collection.
- Target or performance specification.
- Alert/action threshold.
- Person responsible.
- Method of review.
- Required action when performance is unacceptable.
- Documentation of investigation and follow-up.
6. How to Select Useful Laboratory Quality Indicators
A common mistake is selecting indicators simply because they are easy to count. A good indicator should provide meaningful information that can lead to action.
When selecting laboratory quality indicators, consider the following characteristics.
Clinical relevance
The indicator should be associated with a process that could influence patient care, safety, or clinical decision-making.
Measurability
Reliable numerator and denominator data must be available. Indicators based on incomplete or inconsistent reporting can create misleading conclusions.
Actionability
The laboratory should be able to act when performance deteriorates. If no meaningful action can be taken, the value of routine monitoring is limited.
Risk
Processes with high potential severity should receive greater attention. Patient identification errors, delayed critical values, transfusion-related identification problems, and incorrect results may warrant high priority even if they occur infrequently.
Frequency
Frequently occurring problems may produce sufficient data for meaningful trend analysis.
Comparability
Definitions should remain consistent over time so that monthly or annual performance comparisons are valid.
Data quality
The collection method itself must be reliable. Automatically extracted LIS data are often preferable when properly validated, because purely manual reporting may be affected by underreporting.
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7. Pre-Analytical Quality Indicators
The pre-analytical and broader extra-analytical phases deserve particular attention because numerous laboratory errors originate outside the core measurement step.
Important pre-analytical indicators include the following.
7.1 Patient Identification Error Rate
Patient identification is one of the most safety-critical steps in laboratory medicine. Errors may include missing identifiers, incorrect identifiers, label mismatches, specimens collected from the wrong patient, or labels applied incorrectly.
Patient Identification Error Rate = (Identification Errors ÷ Total Relevant Requests or Specimens) × 100
Because identification errors can have severe consequences even when uncommon, laboratories should evaluate individual incidents in addition to monitoring aggregate rates.
7.2 Specimen Rejection Rate
Specimen rejection is one of the most practical indicators because it reflects multiple collection and handling problems.
Specimen Rejection Rate = (Rejected Specimens ÷ Total Specimens Received) × 100
Common rejection reasons include:
- Unlabeled specimens.
- Mislabeled specimens.
- Insufficient volume.
- Incorrect container.
- Clotted anticoagulated specimens.
- Severe hemolysis according to laboratory criteria.
- Leaking specimens.
- Improper transport.
- Excessive delay before receipt or processing.
- Incorrect blood-to-anticoagulant ratio where clinically relevant.
The overall rejection rate should not be the only measurement. Breaking the indicator down by rejection reason and collection location frequently provides much more actionable information.
7.3 Hemolyzed Specimen Rate
Hemolysis is an important pre-analytical problem, particularly in chemistry testing. It can interfere with several laboratory measurements and may result in recollection, delayed testing, or potentially misleading results.
Hemolysis Rate = (Hemolyzed Specimens ÷ Total Specimens Evaluated for Hemolysis) × 100
Where automated serum indices are available, laboratories can use analyzer-generated hemolysis information to improve consistency.
Monitoring by emergency department, inpatient ward, outpatient collection center, phlebotomist group, or collection method may reveal important patterns.
7.4 Clotted Specimen Rate
Unexpected clotting can render anticoagulated specimens unsuitable, especially in hematology and coagulation testing.
Clotted Specimen Rate = (Clotted Anticoagulated Specimens ÷ Total Anticoagulated Specimens Received) × 100
Increasing clot rates may indicate collection technique problems, delayed mixing, difficult venipuncture, incorrect fill technique, or insufficient training.
7.5 Insufficient Sample Volume Rate
Quantity-not-sufficient events can delay patient testing and are especially important in pediatric, neonatal, emergency, and difficult-access populations.
QNS Rate = (Insufficient-Volume Specimens ÷ Total Specimens Received) × 100
7.6 Incorrect Container Rate
Using an inappropriate collection tube or container can invalidate laboratory testing.
Examples include:
- EDTA instead of citrate.
- Citrate instead of serum.
- Incorrect microbiology transport medium.
- Wrong urine collection container.
- Improper blood gas syringe.
Incorrect Container Rate = (Specimens in Incorrect Containers ÷ Total Specimens Received) × 100
7.7 Blood Culture Contamination Rate
Blood culture contamination is a clinically important indicator because contamination may complicate interpretation, increase unnecessary investigation, and affect antimicrobial management.
Blood Culture Contamination Rate = (Contaminated Blood Culture Sets ÷ Total Blood Culture Sets Collected) × 100
The exact definition of contamination should be documented by the laboratory and clinical microbiology service rather than applied inconsistently.
7.8 Specimen Transportation Problems
A laboratory may also monitor specimens affected by delayed transport, temperature excursions, pneumatic tube incidents, container leakage, or failure to meet defined stability requirements.
7.9 Test Request Problems
Depending on laboratory workflow, useful indicators may include incomplete requests, duplicate orders, missing clinical information for specialized tests, or inappropriate ordering patterns.
| Pre-Analytical Indicator Possible Risk Potential Improvement | ||
|---|---|---|
| Patient identification errors | Wrong-patient result | Positive identification and barcode controls |
| Hemolysis rate | Interference and recollection | Phlebotomy training and collection review |
| Clotted EDTA samples | Invalid CBC/platelet result | Immediate mixing and collection competency |
| Incorrect citrate fill | Potential coagulation error | Tube fill training and rejection criteria |
| Blood culture contamination | False-positive culture interpretation | Collection technique improvement |
| QNS | Delayed result | Collection volume guidance |
8. Analytical Quality Indicators
The analytical phase is usually more directly controlled by laboratory personnel and commonly includes formal internal QC, calibration, equipment maintenance, method verification, reagent lot management, and external quality assessment.
Useful analytical quality indicators can complement traditional QC processes.
8.1 Internal QC Failure Rate
QC Failure Rate = (Rejected QC Runs ÷ Total QC Runs) × 100
However, laboratories must define what constitutes a "rejected run." For example, a laboratory using multirule QC should differentiate warning signals from rejection rules according to its established QC procedure.
8.2 Calibration Failure Rate
Frequent calibration failures can indicate reagent instability, calibrator problems, analyzer malfunction, maintenance issues, environmental factors, or procedural weaknesses.
Calibration Failure Rate = (Failed Calibration Events ÷ Total Calibration Events) × 100
8.3 Analyzer Downtime
Analyzer downtime can influence turnaround time and service continuity.
Useful measures include:
- Total downtime hours.
- Number of unplanned downtime events.
- Mean downtime per event.
- Percentage of operating time lost.
- Number of patient tests transferred to backup systems.
8.4 Repeat Analysis Rate
An unusually high repeat rate may indicate analyzer problems, unstable methods, sample quality problems, inadequate autoverification rules, or unnecessary manual practices.
Repeat Testing Rate = (Repeated Tests ÷ Total Tests Performed) × 100
This indicator must be interpreted cautiously because clinically justified dilution, confirmation, reflex testing, or analyzer-required repeats should not automatically be classified as quality failures.
8.5 External Quality Assessment / Proficiency Testing Performance
Laboratories should monitor unsuccessful or unacceptable EQA/PT events and investigate them according to their quality system.
Investigation may include:
- Clerical errors.
- Calibration problems.
- Reagent issues.
- Method bias.
- Instrument problems.
- Incorrect units.
- Transcription errors.
- Staff competency.
8.6 Reagent Lot-Related Events
Laboratories may monitor unexpected shifts after reagent lot changes, lot verification failures, reagent wastage, or repeated lot-related investigations.
8.7 Analytical Error or Result Recall Rate
A result that must be corrected because of an analytical failure is an important quality event even when the absolute frequency is very low.
9. Post-Analytical Quality Indicators
The post-analytical phase begins after measurement and includes validation, release, reporting, result delivery, critical communication, and correction of errors.
9.1 Corrected Report Rate
Corrected Report Rate = (Corrected Patient Reports ÷ Total Patient Reports Released) × 100
Not all report amendments represent laboratory errors. A laboratory should therefore categorize corrected reports.
Possible categories include:
- Transcription error.
- Incorrect patient/result association.
- Incorrect unit.
- Incorrect reference interval.
- Analytical error discovered after release.
- Clinical information added later.
- Authorized interpretive amendment.
Separating error-related corrections from legitimate clinical amendments prevents distortion of the indicator.
9.2 Delayed Report Rate
Delayed Report Rate = (Reports Exceeding Defined TAT ÷ Total Applicable Reports) × 100
9.3 Critical Result Notification Compliance
Critical Notification Compliance = (Critical Results Communicated Within Target ÷ Total Critical Results) × 100
9.4 Critical Result Communication Failure
This indicator may include unsuccessful communication, missing read-back documentation, incorrect recipient, or communication beyond the established escalation period.
9.5 Report Delivery Errors
Laboratories using electronic interfaces should monitor LIS or interface failures that delay or incorrectly transmit results.
10. Turnaround Time as a Laboratory Quality Indicator
Turnaround time is one of the most visible laboratory performance indicators because clinicians directly experience delays in laboratory reporting.
However, TAT must be clearly defined. Several different intervals can be measured.
Order-to-result TAT
Time from test request to result availability.
Collection-to-result TAT
Time from specimen collection to result reporting.
Laboratory TAT
Time from specimen receipt or accession in the laboratory to verified result release.
Analytical TAT
Time from loading or starting analysis to analytical completion.
A laboratory should avoid publishing a TAT KPI without specifying precisely which starting and ending timestamps are used.
Example
Suppose the laboratory's target is for 90% of emergency potassium tests to be released within 45 minutes of specimen receipt.
During one month:
- Total eligible potassium requests: 2,000
- Results released within 45 minutes: 1,860
TAT Compliance = (1,860 ÷ 2,000) × 100 = 93%
The laboratory achieved the defined 90% target.
However, the 140 delayed results should still be categorized where possible. Potential reasons include centrifugation delay, analyzer downtime, specimen recollection, interface delay, QC failure, maintenance, or excessive workload.
11. Critical Value Quality Indicators
Critical value management is highly relevant to patient safety and should be treated as a complete process rather than simply recording whether someone was contacted.
Potential QIs include:
- Percentage of critical results communicated within target time.
- Median critical-result notification time.
- Percentage with documented read-back where required by policy.
- Percentage requiring escalation.
- Unsuccessful communication events.
- Critical results released without complete documentation.
Important distinction: Critical result notification time should be defined precisely. The starting point may be result verification or identification of the critical value, depending on laboratory policy. Changing definitions without documenting the change invalidates trend comparisons.
12. Quality Indicator Calculations and Formulas
Most laboratory QIs are expressed as percentages, rates, ratios, counts, or time-based statistics.
Percentage
Percentage = (Number of Events ÷ Total Opportunities) × 100
Rate per 1,000
Rare events may be easier to interpret when expressed per 1,000 or 10,000 opportunities.
Rate per 1,000 = (Number of Events ÷ Total Opportunities) × 1,000
Example: mislabeled specimens
If 8 mislabeled specimens occur among 40,000 specimens:
(8 ÷ 40,000) × 1,000 = 0.2 mislabeled specimens per 1,000 specimens
Compliance percentage
Compliance % = (Events Meeting Requirement ÷ Total Applicable Events) × 100
Defect percentage
Defect % = (Nonconforming Events ÷ Total Applicable Events) × 100
Median TAT
For turnaround-time data, the median can sometimes be more informative than the mean because extreme delays may strongly affect the arithmetic average.
Percentiles are also useful. For example, a laboratory may monitor median TAT together with the 90th percentile or percentage meeting the defined target.
13. How to Establish Quality Indicator Targets
A target should not be selected arbitrarily simply because a round number looks attractive.
Possible sources for targets include:
- External quality indicator programs.
- Professional recommendations.
- Regulatory or accreditation requirements where applicable.
- Published evidence.
- Validated internal historical performance.
- Risk assessment.
- Contractual or clinical service requirements.
- Peer laboratory benchmarking.
Some indicators represent events for which the ultimate desired state is zero, such as wrong-patient specimen errors. Nevertheless, management should distinguish between the ideal objective and statistical expectations when interpreting rare events.
For other indicators, a laboratory may define:
- Target level: expected acceptable performance.
- Warning level: performance requiring enhanced review.
- Action level: performance requiring investigation and corrective action.
Example
| Hemolysis Rate Interpretation Action | ||
|---|---|---|
| Within laboratory target | Acceptable performance | Continue routine monitoring |
| Above warning level | Potential deterioration | Review trend and collection locations |
| Above action level | Unacceptable performance | Investigate and initiate improvement |
The numerical thresholds should be established by the individual laboratory using appropriate standards, evidence, risk analysis, and/or validated benchmarking rather than copied blindly from an unrelated laboratory.
14. Building a Laboratory Quality Indicator Dashboard
An effective quality dashboard should provide management with a concise view of laboratory performance rather than hundreds of disconnected numbers.
A practical dashboard may contain approximately 10–20 high-priority indicators, while more detailed operational indicators remain available within individual departments.
| Indicator Current Month Previous Month Target Status Owner | |||||
|---|---|---|---|---|---|
| Specimen rejection | 0.72% | 0.65% | Internal target | Review | Pre-Analytical Supervisor |
| Hemolysis | 2.1% | 1.4% | Internal target | Investigate trend | Phlebotomy Lead |
| STAT TAT compliance | 94% | 92% | ≥90% | Acceptable | Chemistry Supervisor |
| Critical notification compliance | 99% | 98% | Defined policy target | Acceptable | Operations |
| Corrected reports | 0.03% | 0.02% | Internal target | Monitor | Quality Manager |
The dashboard should show trends, not just isolated monthly values. A line chart over 12 months can reveal deterioration that would be missed when each month is reviewed separately.
15. How to Investigate a Poor Quality Indicator
An indicator exceeding its target does not automatically identify the root cause. It signals that a process requires evaluation.
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A structured investigation may follow these steps:
Step 1: Verify the data
Ensure that the apparent deterioration is real. Check denominator accuracy, data extraction rules, duplicate records, LIS changes, new coding practices, and changes in indicator definition.
Step 2: Stratify the problem
Break the data into meaningful categories.
For a high hemolysis rate, stratification could include:
- Emergency department versus wards.
- Morning versus evening shift.
- Venipuncture versus intravenous line collection.
- Individual collection areas.
- Adult versus pediatric populations.
- Specific collection devices.
Step 3: Determine when the change started
Look for a specific point at which performance shifted. Consider equipment changes, staff changes, new supplies, new procedures, workload increases, transport changes, or information system updates.
Step 4: Perform root cause analysis
Useful tools include:
- 5 Whys.
- Fishbone/Ishikawa diagram.
- Process mapping.
- Pareto analysis.
- Direct observation.
- Staff interviews.
- Failure mode review.
Step 5: Implement corrective action
Corrective actions should address the identified cause, not merely the observed symptom.
Step 6: Measure effectiveness
Continue monitoring the same quality indicator after intervention to determine whether performance actually improved.
16. Connecting Quality Indicators with CAPA
Quality indicators can function as important triggers for the laboratory's corrective and preventive improvement processes.
Suppose the monthly clotted CBC specimen rate rises significantly.
A weak response would be:
"Staff were reminded to mix tubes properly."
A stronger CAPA-based approach would first establish why clotted samples increased.
The investigation might find that most affected specimens came from a newly opened emergency collection area where recently assigned employees had not completed practical phlebotomy competency assessment.
Actions could then include:
- Temporary supervision.
- Targeted training.
- Observed competency assessment.
- Review of collection supplies.
- Standardized tube-mixing instructions.
- Weekly monitoring of the clot rate.
The laboratory should then evaluate effectiveness using the same indicator.
If the rate falls and remains controlled, the action may be considered effective. If it does not improve, the root cause analysis should be reconsidered.
17. Quality Indicators and Risk-Based Laboratory Management
Not all quality events have equal clinical significance.
For example, a minor delay in a routine outpatient test and a wrong-patient blood bank specimen cannot be treated as equivalent simply because both are counted as one nonconformity.
A risk-based approach considers factors such as:
- Probability of occurrence.
- Severity of potential harm.
- Detectability before reaching the patient.
- Frequency of exposure.
- Availability of effective controls.
Indicators associated with high-risk processes should generally receive greater management attention.
Potential high-risk QIs include:
- Patient identification errors.
- Blood bank labeling discrepancies.
- Incorrect blood component release events.
- Critical value communication failures.
- Incorrect patient reports.
- Significant EQA/PT failures.
- Major analytical failures affecting multiple patient results.
The frequency of an event should therefore never be considered in isolation.
18. Laboratory Quality Indicator Case Studies
Case Study 1: Increasing Hemolysis in the Emergency Department
A hospital laboratory monitors monthly hemolysis rates. For several months, performance remained stable at approximately 1%. Over the following four months, rates increased sequentially to 1.4%, 1.8%, 2.3%, and 3.0%.
Instead of waiting for the indicator to become extremely high, the laboratory initiated an investigation.
Data stratification demonstrated:
- Outpatient hemolysis remained stable.
- Ward hemolysis remained stable.
- The increase occurred mainly in emergency department specimens.
- A large proportion of affected specimens were collected through intravenous access devices.
The laboratory collaborated with emergency nursing leadership, reviewed collection practices, delivered targeted training, and monitored the indicator weekly.
Over subsequent weeks, the hemolysis rate declined substantially.
Lesson: A QI becomes useful when it leads to localization of a problem, identification of contributing factors, intervention, and measurable follow-up.
Case Study 2: STAT Troponin TAT Deterioration
The laboratory target requires a defined percentage of eligible STAT troponin results to be released within its established receipt-to-report interval.
Monthly compliance declines from 94% to 88%.
Initial suspicion focuses on the chemistry analyzer, but analyzer analytical time remains unchanged.
Timestamp analysis demonstrates that the longest delay occurs between sample receipt and centrifugation during evening peak workload.
The laboratory therefore adjusts workflow and specimen prioritization rather than unnecessarily recalibrating or servicing the analyzer.
The next monitoring cycle demonstrates improved TAT compliance.
Lesson: Never assume the location of the problem. Break the total TAT into subintervals.
Case Study 3: Recurrent Clotted CBC Samples
A hematology laboratory identifies a high number of clotted EDTA specimens from one inpatient unit.
The initial overall rejection rate appears only slightly elevated. When rejection reasons are analyzed individually, the clotting rate is disproportionately high.
Observation shows inconsistent mixing immediately after collection.
Training and direct competency reassessment are implemented. Subsequent monitoring shows a sustained reduction.
Lesson: Overall rejection rates can hide important local problems. Always analyze the reason and collection location.
Case Study 4: Critical Potassium Notification Delay
A critical potassium result is verified but communication is delayed significantly because the first clinical contact cannot be reached.
Review identifies that staff know the initial contact process but the escalation pathway is unclear.
The laboratory revises its critical-result procedure to define:
- Primary contact.
- Maximum attempt interval.
- Secondary contact.
- Escalation pathway.
- Documentation.
- Read-back requirements according to local policy.
Critical result notification compliance is subsequently monitored monthly.
Case Study 5: EQA Failure Despite Acceptable Internal QC
A chemistry assay demonstrates acceptable daily internal QC but receives an unacceptable external quality assessment result.
The laboratory does not simply repeat internal QC and close the incident.
The investigation includes:
- Reviewing calibration.
- Checking reagent and calibrator lot information.
- Reviewing instrument maintenance.
- Assessing possible transcription or unit errors.
- Reviewing peer-group or method-related information provided by the EQA program.
- Evaluating patient-result implications where appropriate.
The incident is documented and corrective action is based on the identified cause.
19. Step-by-Step Quality Indicator Implementation Program
Step 1: Map the laboratory process
Document key processes from test ordering through specimen collection, transportation, analysis, validation, and reporting.
Step 2: Identify high-risk points
Use complaints, incident records, audit findings, nonconformities, risk assessments, and staff experience to identify vulnerable processes.
Step 3: Choose a manageable QI set
Do not begin with 100 indicators. Select a focused group covering the most important laboratory processes.
Step 4: Define every indicator
Create a written QI definition sheet containing the numerator, denominator, calculation, source, target, frequency, and responsibility.
Step 5: Establish baseline data
Collect sufficient baseline information before interpreting variation.
Step 6: Establish targets
Use appropriate external specifications where available, validated benchmarking, internal performance, clinical requirements, and risk assessment.
Step 7: Automate data collection where possible
Validated LIS, middleware, analyzer, and quality-management data extraction can reduce manual workload and improve completeness.
Step 8: Review trends
Monthly values should be plotted and compared rather than reviewed as disconnected figures.
Step 9: Investigate unacceptable performance
Determine whether deterioration is random variation, a temporary event, or a systematic process problem.
Step 10: Implement corrective action
Actions should be proportional to risk and supported by root cause findings.
Step 11: Measure effectiveness
Continue monitoring to determine whether the intervention produced sustained improvement.
Step 12: Review indicator usefulness
Indicators that no longer provide meaningful information may be modified or replaced. New risks may require new indicators.
Recommended Core Quality Indicator Set for a Medical Laboratory
| Phase Suggested Indicator | |
|---|---|
| Pre-Analytical | Patient identification error rate |
| Pre-Analytical | Total specimen rejection rate |
| Pre-Analytical | Hemolysis rate |
| Pre-Analytical | Clotted specimen rate |
| Pre-Analytical | Insufficient-volume rate |
| Pre-Analytical | Incorrect-container rate |
| Microbiology | Blood culture contamination rate |
| Analytical | Rejected internal QC run rate |
| Analytical | EQA/PT unacceptable event rate |
| Analytical | Unplanned analyzer downtime |
| Post-Analytical | STAT TAT compliance |
| Post-Analytical | Critical value notification compliance |
| Post-Analytical | Corrected report rate |
| Quality System | Complaint rate |
| Quality System | CAPA effectiveness completion |
| Personnel | Competency assessment completion |
| Equipment | Preventive maintenance completion |
Quality Indicators by Laboratory Department
Clinical Chemistry
- Hemolyzed specimen rate.
- Rejected specimens.
- QC rejection frequency.
- Calibration failures.
- STAT electrolyte TAT.
- Troponin TAT.
- Analyzer downtime.
- Corrected chemistry reports.
Hematology
- Clotted EDTA samples.
- Insufficient-volume CBC specimens.
- Unexpected repeat rate.
- Manual smear review workload.
- CBC critical result notification time.
- Analyzer downtime.
- EQA/PT performance.
Coagulation
- Underfilled citrate tube rate.
- Clotted citrate specimens.
- Rejected coagulation specimens.
- STAT coagulation TAT.
- QC failure frequency.
Blood Bank / Transfusion Laboratory
- Mislabeled specimen events.
- Wrong-patient specimen events.
- Sample rejection rate.
- Emergency blood issue TAT.
- Antibody investigation TAT.
- Corrected blood bank reports.
- Blood component-related laboratory incidents.
Microbiology
- Blood culture contamination rate.
- Specimen rejection rate.
- Critical organism communication time.
- Positive blood culture notification time.
- Culture reporting TAT.
- Identification or susceptibility testing repeat rate where meaningful.
Molecular Diagnostics
- Invalid run rate.
- Internal control failure rate.
- Contamination events.
- Repeat extraction rate.
- Rejected molecular specimens.
- Result TAT.
- EQA/PT performance.
Laboratory Quality Indicator Reporting Template
| Field Example | |
|---|---|
| Indicator | Hemolyzed specimens |
| Department | Clinical Chemistry |
| Numerator | Hemolyzed samples |
| Denominator | Total eligible serum/plasma samples |
| Calculation | Numerator ÷ denominator × 100 |
| Data source | LIS / analyzer hemolysis index |
| Frequency | Monthly |
| Target | Laboratory-defined specification |
| Responsible person | Pre-analytical or chemistry supervisor |
| Action | Trend review and investigation if threshold exceeded |
How to Interpret Trends Correctly
A single abnormal month should be evaluated in context. An unusual value may represent a temporary workload event, supply problem, staff shortage, information-system interruption, or data error.
By contrast, a gradual deterioration over several months may represent a true trend.
Laboratories should consider:
- Absolute value.
- Change from baseline.
- Direction of trend.
- Duration of deterioration.
- Clinical risk.
- Number of events.
- Changes in denominator.
- Operational changes.
One of the most important principles is avoiding interpretation based solely on percentage change.
For example, an increase from one error to two errors is a 100% increase, but the operational meaning depends on whether the denominator is 100 specimens or one million specimens and on the severity of the error itself.
Benchmarking Laboratory Quality Indicators
Benchmarking means comparing laboratory performance with relevant external or peer performance data.
Benchmarking can help answer an important question:
Is our current performance merely better than last month, or is it actually good compared with appropriate laboratory practice?
However, comparisons are valid only when definitions are compatible.
For example, two laboratories may report a "specimen rejection rate," but one may count individual tubes while another counts patient encounters. One may include hemolysis while another reports hemolysis separately.
Therefore, harmonized definitions are essential for meaningful benchmarking.
The International Federation of Clinical Chemistry and Laboratory Medicine Working Group on Laboratory Errors and Patient Safety has developed work specifically focused on laboratory errors, patient safety, and harmonized quality indicators throughout the testing process.
Automation and LIS-Based Quality Indicators
Modern laboratory information systems can significantly improve QI monitoring when data extraction is properly configured and validated.
Automated data sources may include:
- Specimen rejection codes.
- Collection timestamps.
- Receipt timestamps.
- Analyzer completion times.
- Verification timestamps.
- Critical result logs.
- Corrected report logs.
- Analyzer interface records.
- Autoverification exceptions.
Automation reduces manual counting but introduces another quality requirement: the data logic itself must be verified.
For example, TAT data can become inaccurate if specimens lacking valid collection timestamps are automatically assigned a default time.
Quality Indicators and Management Review
QI performance should contribute to laboratory management review and broader improvement planning.
Management should evaluate more than whether a target is "green" or "red."
Useful questions include:
- Which indicators deteriorated?
- Which indicators improved?
- Are recurring failures present?
- Which problems have the highest patient risk?
- Were previous corrective actions effective?
- Are resources contributing to poor performance?
- Do processes require redesign?
- Are new indicators needed?
Quality Indicators and Staff Competency
QI data can reveal training and competency gaps that conventional annual assessments may not immediately identify.
For example, if specimen rejection rates increase after new personnel begin work in a collection center, the quality team should not automatically assume lack of competence. However, training records, supervision, collection observations, and competency evaluation should become part of the investigation.
The same principle applies to analyzer errors, critical-value communication, manual differential discrepancies, blood bank procedures, and microbiology workflows.
24. Common Mistakes in Laboratory Quality Indicator Programs
Mistake 1: Measuring too many indicators
A laboratory may create dozens of indicators that consume staff time without generating improvement.
Better approach: prioritize clinically relevant, risk-based, actionable indicators.
Mistake 2: No denominator
Reporting "25 rejected samples" is difficult to interpret when monthly workload changes.
Better approach: report the appropriate rate or percentage.
Mistake 3: Changing definitions
Changing what counts as hemolysis, rejection, TAT failure, or corrected result invalidates long-term trend interpretation unless properly documented.
Mistake 4: No investigation after failure
A red dashboard alone does not improve quality.
Mistake 5: Training everyone without identifying the cause
"Retrain staff" is often selected as a generic corrective action even when the true cause is equipment, workflow, information system design, workload, or unclear procedures.
Mistake 6: Looking only at monthly averages
A laboratory-wide average can hide a severe problem in one collection location.
Mistake 7: Punishing staff based on QI results
An effective quality system should encourage reporting and investigation of process weaknesses. A punitive approach may increase underreporting.
Mistake 8: Comparing laboratories with different definitions
Benchmarking is meaningful only when indicators, numerators, denominators, exclusions, and data collection methods are comparable.
Mistake 9: Ignoring rare high-severity events
Low frequency does not mean low risk.
Mistake 10: Closing CAPA without effectiveness monitoring
Corrective action should be followed by evidence showing whether the targeted indicator improved.
Practical Monthly Quality Meeting Example
A productive monthly laboratory quality meeting could review:
- Current QI dashboard.
- Indicators exceeding action limits.
- New trends.
- Serious incidents.
- Patient complaints.
- EQA/PT performance.
- Analyzer downtime.
- Corrective action progress.
- Effectiveness of previously completed CAPAs.
- New operational or patient-safety risks.
The discussion should result in clearly assigned actions with responsible personnel and follow-up dates.
Example: Complete QI Investigation
Problem: Increased specimen rejection rate.
Indicator result: Rejection increased from 0.6% to 1.4%.
Data verification: LIS calculation confirmed.
Stratification: Most increase related to clotted EDTA tubes.
Location analysis: 72% originated from one emergency collection area.
Root cause: Staff turnover plus inconsistent specimen mixing practice.
Corrective actions: Practical retraining, competency observation, revised collection visual aid, supervisor monitoring.
Effectiveness: Clotted specimen rate reviewed weekly for eight weeks.
Outcome: Performance returned near baseline and remained stable.
Quality Indicators: What Laboratory Professionals Should Remember
Quality indicators should answer meaningful questions about laboratory performance.
A strong laboratory QI program is characterized by:
- Clear definitions.
- Reliable data.
- Appropriate numerators and denominators.
- Risk-based prioritization.
- Consistent measurement.
- Trend analysis.
- Root cause investigation.
- Corrective action.
- Effectiveness monitoring.
- Management involvement.
- Continuous improvement.
Quality indicators should also span the entire testing pathway instead of concentrating only on internal analytical performance.
25. Frequently Asked Questions
What is a quality indicator in a medical laboratory?
A laboratory quality indicator is a defined measurable parameter used to evaluate the quality, safety, effectiveness, or performance of a laboratory process.
What are examples of laboratory quality indicators?
Common examples include specimen rejection rate, hemolysis rate, clotted specimen rate, patient identification errors, blood culture contamination, internal QC failure rate, EQA/PT performance, turnaround-time compliance, critical-value notification compliance, and corrected report rate.
What is the difference between QC and a quality indicator?
Internal quality control primarily evaluates analytical system stability and performance. Quality indicators can monitor the broader total testing process, including pre-analytical, analytical, and post-analytical activities.
What is a laboratory KPI?
A key performance indicator is a measure selected because it represents an important strategic or operational objective. A laboratory may monitor many quality indicators but report only selected high-priority indicators as management KPIs.
How is specimen rejection rate calculated?
Specimen Rejection Rate = (Rejected Specimens ÷ Total Specimens Received) × 100
How is hemolysis rate calculated?
Hemolysis Rate = (Hemolyzed Specimens ÷ Total Eligible Specimens) × 100
How is TAT compliance calculated?
TAT Compliance = (Results Meeting TAT Target ÷ Total Eligible Results) × 100
Should all laboratory quality indicators have a target?
Indicators should have a defined method of interpretation. Depending on the indicator, this may include a target, benchmark, warning threshold, action threshold, trend criterion, or risk-based review rule.
How often should QIs be reviewed?
The appropriate frequency depends on risk, workload, and the indicator. High-risk or rapidly changing indicators may require daily or weekly monitoring, while others may be reviewed monthly or quarterly.
What should happen when a QI exceeds its target?
The laboratory should verify the data, assess clinical risk, analyze trends, investigate contributing factors or root causes when appropriate, implement corrective action, and monitor effectiveness.
Are quality indicators required only for accredited laboratories?
No. QIs are useful for any clinical laboratory seeking to improve patient safety, reliability, efficiency, and service performance, regardless of accreditation status.
Conclusion
Laboratory Quality Indicators are among the most practical tools for converting a quality management system into measurable performance.
Their greatest value lies not in creating dashboards but in identifying processes that need improvement.
A well-designed system monitors the full testing pathway: patient identification, test requesting, specimen collection, transportation, sample integrity, analytical performance, turnaround time, result reporting, critical-value communication, amended reports, and other processes with potential impact on patient care.
The most effective laboratories use quality indicators as part of a continuous cycle:
Measure → Analyze → Investigate → Improve → Re-measure
When this cycle is consistently applied, quality indicators become much more than accreditation evidence. They become an operational patient-safety tool.
26. References and Further Reading
Source policy: The links below point to official organizations, PubMed records, or official IFCC publications. No fabricated DOI or placeholder reference links are included.
1. International Organization for Standardization (ISO). ISO 15189:2022 — Medical laboratories — Requirements for quality and competence. https://www.iso.org/standard/76677.html
2. World Health Organization. Laboratory Quality Management System: Handbook. https://www.who.int/publications/i/item/9789241548274
3. World Health Organization. Laboratory Quality Management. https://www.who.int/activities/laboratory-quality-management
4. International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). Working Group on Laboratory Errors and Patient Safety (WG-LEPS). https://ifcc.org/ifcc-education-division/working-groups-special-projects/wg-leps/
5. IFCC WG-LEPS. Quality Indicators in Laboratory Medicine — Quality Indicators Project. https://ifcc.org/ifcc-education-division/working-groups-special-projects/wg-leps/quality-indicators-project/
6. IFCC WG-LEPS. Publications on laboratory errors, patient safety, and quality indicators. https://ifcc.org/ifcc-education-division/working-groups-special-projects/wg-leps/publications/
7. Sciacovelli L, et al. Pre-analytical quality indicators in laboratory medicine: Performance of laboratories participating in the IFCC Working Group "Laboratory Errors and Patient Safety" project. Clinica Chimica Acta. 2019;497:35–40. PubMed: 31295446
8. Plebani M. Quality indicators to detect pre-analytical errors in laboratory testing. Clinical Biochemistry Reviews. 2012;33(3):85–88. PubMed: 22930602
9. Plebani M, et al. Harmonization of pre-analytical quality indicators. PubMed: 24627719
10. Plebani M. Quality indicators in laboratory medicine: a fundamental tool for quality and patient safety. PubMed: 23219744
11. Ricós C, et al. Quality indicators and specifications for the extra-analytical phases in clinical laboratory management. PubMed: 15259371
12. Zorbozan N, et al. Evaluation of preanalytical and postanalytical phases in clinical laboratory practice using quality indicators. PubMed: 35966260
Prepared by Dr. Omar Adwan
Medical Laboratory Technologist. This educational article was prepared for MedLab Academy with a focus on practical laboratory quality management, patient safety, and continuous improvement.
Medical Disclaimer
This article is intended for professional education and general laboratory quality-management information. It does not replace applicable accreditation standards, regulatory requirements, manufacturer instructions, institutional procedures, or clinical judgment. Each laboratory should establish quality indicators, acceptance criteria, targets, and corrective-action procedures appropriate to its own methods, patient population, regulatory environment, and risk assessment.
Last updated: August 2026