RISK MANAGEMENT
Aspect Purpose
Summary
Detect analytical drift, calibration bias, or pre-analytical change via patient result distribution monitoring.
Methods Patient means/medians, trend analysis, alignment of reference intervals with decision limits. Risks
Population mix changes, misaligned reference vs decision limits, overtesting, terminology confusion.
Best practice
Define patient selection; interpret trends in context; present both reference interval & decision limits; use approved terminology.
Integration Link with other external signals (EQA, delta checks, critical result frequency) for comprehensive risk-based monitoring.
Table 3. Key aspects of reference range/interval use for clinical risk.
lot or calibrator changes, verification of calibration traceability, and, where appropriate, comparison with results from other methods or sites. Input from clinical leads may also be needed to assess whether the reference range or decision limits remain appropriate in light of any observed trends. The impact of reference range definition and application is not purely technical.11
Because reference intervals
are often set to include 95% of a healthy population, performing multiple tests in asymptomatic individuals inevitably yields some ‘abnormal’ results by chance alone. This overtesting can trigger cascades of further investigations and even interventions with no clinical benefit, particularly when testing is initiated through non-evidence-based screening programmes. Such practices heighten the risk of patient harm, especially when abnormal results fall close to the limits of the reference interval, where biological and analytical variation play a greater role. Misalignment between reference intervals and clinical decision limits can also contribute to inappropriate clinical action. Giovanella et al.12
illustrate this
with the example of thyroid-stimulating hormone (TSH) testing in elderly patients, where mild TSH elevations are often physiological and not associated with adverse outcomes. If the laboratory reference range upper limit is set too low relative to the threshold for intervention, more patients are classified as ‘abnormal’, potentially leading to overtreatment. Symonds et al.,13 by Giovanella et al.,12
as cited demonstrated that
even a small planned decrease in the TSH upper reference limit nearly tripled the proportion of abnormal results, increased retesting, and drove up new thyroxine prescriptions. Aligning laboratory reports to clearly present both the reference range and the relevant clinical decision limits can mitigate such risks (Table 3). The clarity of terminology also underpins safe use of reference data. Haeckel et al.14
highlight the persistent confusion between ‘reference interval’ 32
and ‘reference range’, recommending the IFCC-approved usage where the interval denotes the central proportion of reference values (eg 135–145 mmol/L for serum sodium), and the range refers to the numerical difference between these limits.14
Using ‘normal range’ is
discouraged, as ‘normality’ is difficult to define in medicine and can mislead clinicians and patients. Precision in language helps prevent misinterpretation and ensures that decision-making is based on scientifically valid and clinically relevant thresholds. Reference range monitoring is not limited to detecting analytical error; it can also identify clinically driven changes, such as new disease prevalence patterns or evolving testing guidelines. These shifts may require adjustments to interpretation criteria or reporting practices. By linking reference range monitoring with other external signals – such as EQA results, delta checks, and critical result frequencies – laboratories can better distinguish between analytical, pre-analytical, and clinical causes of change.
When implemented as part of a risk- based quality system, reference range monitoring helps fulfil the ISO 15189 requirement for ongoing verification of examination performance and supports the ISO 22367 emphasis on early detection and mitigation of risks to patient safety. The combined insights from Vogeser,11 et al.14
Giovanella12
underline that the definition, application, and communication of reference data must be as robustly managed as any other element of laboratory quality assurance to protect patients from avoidable harm.
Monitoring frequency of critical results Monitoring the frequency of critical results provides another external analytical signal that can indicate emerging risk to patient safety. Tracking the number of results that meet critical value criteria, and compliance with the reporting
process, can reveal both analytical and systemic issues. A sudden increase in the proportion of critical results for a particular analyte may suggest analytical drift or a calibration change that is shifting results toward or beyond a decision threshold. Conversely, an unexplained decrease in critical results could indicate bias in the opposite direction, potentially masking clinically important findings. In either case, a change in the frequency of critical results should prompt a review of recent reagent lot changes, calibration events, and IQC or EQA data to determine whether an analytical cause is present. Changes in patient population or clinical service patterns can also influence critical result frequency. For example, during the COVID-19 pandemic, many laboratories observed a shift in test result distributions due to changes in disease prevalence and patient demographics. Similarly, contractual changes, laboratory network reorganisations, or the introduction of new testing sites can alter the case mix and frequency of abnormal results. Pre-analytical changes, such as modifications to sample collection protocols or transport conditions, can also affect the rate at which critical results are generated.
Monitoring compliance with critical
value reporting procedures is equally important. Even if analytical performance is stable, delays or omissions in communicating critical results can have serious consequences for patient care. Regularly reviewing both the percentage of critical results successfully communicated within target timeframes and the absolute number of reportable critical values provides a dual perspective on risk: the effectiveness of the reporting process and the stability of analytical performance.
and Haeckel
By embedding critical result frequency monitoring within the laboratory’s QI framework, and linking it to other performance indicators such as lot verification outcomes, delta check alerts, and EQA performance, laboratories can identify problems more quickly and apply targeted corrective actions. This approach is fully consistent with the ISO 22367 principle of using quality indicators as active tools for risk detection rather than passive measures of compliance.
Integration of external signals into risk-based quality systems Each of the external analytical signals discussed – lot-to-lot verification, delta checks, monitoring of critical result frequency, and reference range monitoring – provides valuable but partial information about analytical performance. When reviewed in
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