Practical quality indicators for diagnostic laboratories

Quality indicators show whether an important laboratory process is working as intended. Useful indicators do more than produce a percentage: they identify variation, support investigation and show whether improvement has worked.

There is no universal scorecard for every laboratory. Test mix, workload, collection model, equipment and risk differ. A practical system selects manageable indicators, defines them precisely and connects results to review and action.

The operating principle is simple: measure what matters, define how it is counted, interpret the trend and act on what the evidence shows.

1. Purpose of a quality indicator

A quality indicator is a defined measure of an activity, process or outcome. It should answer a management question: Are specimen-identification errors increasing? Are urgent reports meeting agreed turnaround time? Are corrected reports recurring in one section?

An indicator is not an audit finding, single incident or internal QC result. These may supply data, but the indicator observes a defined period and population.

Good indicators help the laboratory:

• detect deterioration or variation;
• compare performance with its baseline;
• focus investigation on higher-risk processes;
• assess whether corrective or improvement action was effective; and
• support decisions about processes, training or resources.

The indicator should lead to a decision. If nobody knows what a change means or who should respond, collecting it adds work without improving the process.

2. Choosing indicators by risk

Start with the laboratory's processes and risks, not a long external list. Consider where failure could affect patient care, incidents recur, work crosses organisational boundaries or performance is difficult to observe.

A candidate indicator is stronger when it is:

• relevant: connected to a quality objective or risk;
• measurable: supported by consistently captured data;
• actionable: owned by someone who can investigate and improve it;
• interpretable: stable definitions allow comparison over time; and
• proportionate: its value justifies the collection effort.

Begin with a small, balanced group across all three phases. More indicators do not guarantee better oversight. Add, revise or retire measures as risks and services change.

3. Pre-analytical examples

Pre-analytical indicators examine what happens before testing. Useful candidates may include:

• incorrectly or incompletely identified specimens;
• specimens rejected, classified by the documented reason;
• recollections attributable to collection or handling problems;
• unsuitable volume, container, transport or storage conditions;
• haemolysed specimens where a defined assessment method is used; and
• samples exceeding the laboratory's specified transport or stability conditions.

The denominator matters. A specimen-rejection rate might use all specimens received, while a blood-culture contamination indicator would require a relevant blood-culture denominator. Combining unlike sample types can hide a problem or create a misleading comparison.

For collection centres, hospitals or referral partners, stratification may locate variation. Use it to improve the process, not assign blame without considering workload and collection context.

4. Analytical examples

Analytical indicators monitor examination processes and evidence supporting result validity. Depending on the discipline and risk, examples may include:

• unacceptable internal QC events by method or analyser;
• calibration failures or repeated calibration events requiring investigation;
• EQA or proficiency-testing results outside approved acceptance criteria;
• examination repeats caused by an identified analytical problem;
• equipment downtime that affects agreed service requirements; and
• nonconforming work attributable to an analytical process.

These indicators need careful interpretation. A repeat may be appropriate, not an error. Counting every QC warning as a failure can also distort performance when the procedure distinguishes warning and rejection rules.

Manufacturer instructions, method limitations and the laboratory's approved QC and equipment procedures remain controlling. An indicator can reveal a pattern, but it does not determine the technical cause or prescribe corrective action.

5. Post-analytical examples

Post-analytical indicators examine review, reporting and communication. Candidates may include:

• reports exceeding a laboratory-defined turnaround-time requirement;
• completion and documentation of critical-result communication under the approved procedure;
• corrected or amended reports, grouped by reason;
• delayed reports for which required communication was not completed; and
• complaints related to report clarity, access or communication.

Turnaround time is meaningful only with defined start and end points. Collection-to-report, receipt-to-report and analysis-to-authorisation measure different processes. Examinations, priorities and exclusions must also be defined.

Do not create universal critical thresholds or notification times from a blog. Those belong in the laboratory's authorised procedure and must reflect clinical, accreditation and jurisdictional requirements.

6. Definitions and denominators

Before monitoring begins, create a controlled definition for each indicator. At minimum, record:

Definition element	What the laboratory should specify
Purpose	The process, objective or risk being monitored
Numerator	The events counted as meeting the indicator definition
Denominator	The eligible population or opportunities during the period
Inclusions and exclusions	Locations, tests, priorities, sample types and exceptional cases
Time and frequency	Start and end points, reporting period and review frequency
Data source	System fields, registers or validated extraction method
Responsibility	Data owner, reviewer and person authorised to initiate action
Interpretation and action	Baseline, limits, escalation route and action plan

A percentage is commonly calculated as:

Indicator rate = eligible events meeting the definition ÷ total eligible opportunities × 100

Keep the raw counts. A rate of 5% from one event in 20 opportunities is less stable than 5% from 500 in 10,000. Missing data, duplicates and definition changes must remain visible.

7. Targets, trends and investigation

Establish a baseline using sufficiently consistent local data. The laboratory can then set a target or review limit appropriate to the process and risk. External benchmarks can be informative only when definitions, populations and calculation methods are genuinely comparable.

Review both the current value and the pattern over time. A run chart can show sustained change, recurring peaks or gradual deterioration that a monthly total may conceal. Stratification by location, shift, specimen type, analyser or reason may help identify where investigation should begin.

An indicator crossing a limit is a signal for review, not proof of a cause. Confirm the data and denominator, examine related incidents and workflow changes, determine the likely cause, implement proportionate action and monitor whether performance improves. Avoid changing the target merely because it is difficult to meet.

8. Management review and improvement

Quality indicators should enter management review as evidence, not decoration. Present the definition, period, numerator, denominator, trend, interpretation, investigation, action owner and due date. Where action has been completed, show the evidence used to judge effectiveness.

Management should also ask whether each indicator remains appropriate. A stable, low-risk measure may be monitored less often or replaced, while a new service, complaint pattern or repeated nonconformity may justify a new indicator. Changes to definitions should be controlled so that historical comparisons are not presented as if nothing changed.

ISO 15189:2022 is the current international standard specifying quality and competence requirements for medical laboratories. NATA's public ISO 15189:2022 assessment worksheet reflects the need for indicators across pre-examination, examination and post-examination processes and for planned objectives, methodology, interpretation, limits, action and review. The WHO Laboratory Quality Management System handbook provides broader quality-management foundations. Indian laboratories should verify the current applicable NABL documents and their approved procedures before publication or implementation.

How a LIMS can support quality indicators

A LIMS can support consistent data capture by connecting timestamps, specimen status, rejection reasons, analyser or method identifiers, report versions, review actions and authorised users. With verified configuration, it may reduce manual compilation and make trends, exceptions and follow-up easier to trace.

The usefulness of the output still depends on definitions and data quality. Software cannot decide which indicator matters, explain a trend or determine an appropriate action without laboratory review.

Frequently asked questions

What is the simplest definition of a practical quality indicator?

It is a clearly defined measure that helps a laboratory monitor an important process, recognise change and decide whether investigation or improvement is needed.

Which part of the laboratory workflow is responsible?

Responsibility follows the process being measured. Collection teams, technical sections, quality personnel and reporting staff may own different indicators, while laboratory management approves objectives and reviews performance.

What records should remain available for review?

Keep the approved definition, numerator and denominator data, exclusions, results, trend, review notes, investigations, actions, responsibilities, approvals and evidence of effectiveness.

Where can a LIMS help, and where is professional judgement still required?

A LIMS can preserve source data, calculate consistently defined measures and support traceability. Laboratory professionals must select indicators, validate definitions, interpret signals, investigate causes and authorise action.