Selecting the right proficiency testing scheme for my laboratory

Significance of the topic

Proficiency testing (PT) is a cornerstone of laboratory quality assurance and external validation of competence. Appropriate selection of PT schemes reduces wasted resources, ensures meaningful performance assessment, supports accreditation and regulatory compliance, and increases confidence in routine test results. Choosing unsuitable PT schemes can lead to misleading conclusions about a laboratory's capabilities, unnecessary corrective actions, or missed nonconformances.

Objectives and scope of the guidance

This guidance aims to help laboratories identify and select PT schemes that are best matched to their routine work. It highlights the key questions and decision criteria to evaluate PT schemes, including how matrices, analytes, concentrations, statistical design, participant composition and performance criteria influence the relevance and usability of PT results for a given laboratory.

Methodology and key selection criteria

When evaluating PT schemes, laboratories should systematically consider a set of technical and practical factors:

• Relevance of test items: Are the matrices, analytes and concentration ranges representative of routine samples? For example, contaminant levels expected in drinking water differ substantially from those in industrial waste, so schemes should match the laboratory’s sample types.
• Form of test material: For molecular analyses, determine whether the PT supplies whole tissues, extracted DNA, or sequence data — each option assesses different parts of the analytical process (extraction versus sequencing).
• Statistical design and reporting: Review the provider’s statistical approach, the method used to derive assigned values, and whether the design supports comparison across methods or groups of participants.
• Number of test items and replicates: Check whether the scheme provides sufficient items and replicates to produce reliable performance assessments for the laboratory’s scope.
• Participant composition: Consider the number and geographic origin of participants and how many use the same measurement procedure; this affects the relevance of peer-group statistics.
• Data collection logistics: Assess submission routes (web portals, e-mail, other), turnaround times, and report formats to ensure they meet laboratory and accreditation needs.
• Compatibility with regulatory and customer requirements: Confirm whether customers, accreditation bodies or regulators expect specific statistical designs, reporting elements, or performance thresholds.

Evaluation of performance and measurement uncertainty

Understand how the provider evaluates performance (for example, z-scores) and whether the approach allows or incorporates participant measurement uncertainty. Key considerations include:

• Transparency of formulas and decision rules used for performance evaluation.
• Whether and how participant-reported measurement uncertainty is used in scoring.
• Suitability of criteria for the laboratory’s role — confirmatory laboratories may require stricter criteria than screening laboratories.

Laboratories should ensure they can interpret scheme results in the context of their own uncertainty budgets and regulatory obligations.

Trust in the PT provider and communication

Select providers on the basis of competence, experience and service quality:

• Accreditation to ISO/IEC 17043 is a strong indicator of competence and appropriate technical procedures.
• Review provider experience in the specific field and the reliability of their assigned values (e.g., use of reference methods, consensus values, or certified reference materials).
• Assess practical support: clarity and timeliness of reports, language and accessibility of information, frequency of communications, and responsiveness to customer requirements.

Good provider–participant communication is essential for resolving technical queries and for tailoring schemes to laboratory needs.

Main findings and discussion

The principal message is that PT scheme selection must be deliberate and evidence-based. Laboratories should not select schemes solely on cost or availability; instead they should match scheme characteristics to their analytical scope, methods and regulatory context. Important implications include:

• Method-specific effects: When multiple operationally defined methods exist (e.g., fat determination by Röse-Gottlieb versus hydrolysis-based methods), the PT design must allow meaningful comparisons or provide method-specific statistics.
• Commutability and matrix effects: Non-commutable or unrepresentative materials can mask method biases and lead to inappropriate conclusions about competence.
• Performance scoring transparency: Lack of clarity about scoring methods or exclusion of reported measurement uncertainty can limit the usefulness of PT reports.

Practical examples in the guidance illustrate choices such as whether to participate in a scheme that uses matrices outside routine practice or whether to select a scheme offering DNA extracts versus tissue samples, depending on which analytical step the laboratory intends to validate.

Benefits and practical applications of selecting appropriate PT schemes

Appropriate PT selection delivers tangible benefits:

• Robust external verification of measurement quality across the laboratory’s actual scope of work.
• Support for accreditation and regulatory demonstration of competence.
• Benchmarking against relevant peer groups and targeted identification of method-related issues.
• Optimised use of resources by avoiding irrelevant or low-value proficiency exercises.
• Improved confidence for clients and stakeholders through transparent and appropriate external assessments.

Future trends and potential applications

Emerging directions that will influence PT scheme selection and design include:

• Greater emphasis on commutable and matrix-representative materials to improve relevance of results.
• Development of method-specific and tiered PT schemes that group participants by analytical procedure to provide fairer comparisons.
• Integration of digital reporting platforms, real-time dashboards and automated data validation to improve timeliness and usability of results.
• Increased use of synthetic or in-silico PT materials for molecular or sequencing proficiency, accompanied by tailored assessment metrics for bioinformatics pipelines.
• Stronger focus on incorporating participant measurement uncertainty and on metrological traceability in assigned values.
• Application of data analytics and machine learning to detect anomalous patterns across interlaboratory datasets and to improve assigned-value estimation.

Conclusion

Choosing the right PT scheme requires matching scheme design and materials to the laboratory’s routine matrices, methods and regulatory obligations; verifying the provider’s competence and reporting practices; and understanding performance evaluation procedures including handling of measurement uncertainty. A structured evaluation saves time and cost, strengthens quality assurance, and ensures that PT outcomes are meaningful for continual improvement and compliance.

References

• Brookman B, Mann I (eds). Eurachem Guide: Selection, Use and Interpretation of Proficiency Testing (PT) Schemes. 3rd ed. 2021.