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<p>Nearly 70% medical decisions are related to diagnosis, and their treatment revolves around laboratory results, emphasizing the need for reliable performance evaluation. Measurement Uncertainty (MU) reflects variations due to imprecision within the analytical phase, whereas Total Error (TE) estimate combines imprecision and bias, thus directly impacts clinical interpretation. Study was conducted in the Biochemistry Laboratory, Neurochemistry Department, IHBAS, Delhi from September 2024 to February 2025) to assess and compare performance of biochemical tests by calculating MU and TE over six months and evaluate their clinical utility and relative applicability in laboratory performance. Internal Quality Controls (IQC; two levels, daily) were used to calculate imprecision and External Quality Assessment (EQA, CMC Vellore) provided bias estimates. MU was calculated from six-monthly IQC data’s SD and CV. TE was calculated from bias and SD and was compared against Total allowable error (TEa) limits. This was observed that Six-monthly MU for most analytes viz. Glucose- 6.44%/6.13%, Creatinine- 8.31%/6.40%, Sodium- 3.60%,/3.80% and Cholesterol- 8.64%/6.98% for Level-1/Level-2 controls respectively, were in acceptable limits. However, TE values exceeded TEa for several analytes despite acceptable MU suggesting that MU alone underestimated these clinically relevant errors, while six-monthly TE captured both bias and imprecision more comprehensively. Our findings propose that MU supports longitudinal monitoring, where consistency is more critical than absolute agreement with a peer group mean. Though MU reflects analytical stability; TE identifies analytes at risk of exceeding permissible error thresholds. Thus both together offers, critical quality tools for quality assurance and clinician confidence in reported results.</p>

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Debate on Measurement Uncertainty Verses Total Error: A Take by Tertiary Care Hospital Laboratory

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Aastha Bansal,
  • Rachna Agarwal

摘要

Nearly 70% medical decisions are related to diagnosis, and their treatment revolves around laboratory results, emphasizing the need for reliable performance evaluation. Measurement Uncertainty (MU) reflects variations due to imprecision within the analytical phase, whereas Total Error (TE) estimate combines imprecision and bias, thus directly impacts clinical interpretation. Study was conducted in the Biochemistry Laboratory, Neurochemistry Department, IHBAS, Delhi from September 2024 to February 2025) to assess and compare performance of biochemical tests by calculating MU and TE over six months and evaluate their clinical utility and relative applicability in laboratory performance. Internal Quality Controls (IQC; two levels, daily) were used to calculate imprecision and External Quality Assessment (EQA, CMC Vellore) provided bias estimates. MU was calculated from six-monthly IQC data’s SD and CV. TE was calculated from bias and SD and was compared against Total allowable error (TEa) limits. This was observed that Six-monthly MU for most analytes viz. Glucose- 6.44%/6.13%, Creatinine- 8.31%/6.40%, Sodium- 3.60%,/3.80% and Cholesterol- 8.64%/6.98% for Level-1/Level-2 controls respectively, were in acceptable limits. However, TE values exceeded TEa for several analytes despite acceptable MU suggesting that MU alone underestimated these clinically relevant errors, while six-monthly TE captured both bias and imprecision more comprehensively. Our findings propose that MU supports longitudinal monitoring, where consistency is more critical than absolute agreement with a peer group mean. Though MU reflects analytical stability; TE identifies analytes at risk of exceeding permissible error thresholds. Thus both together offers, critical quality tools for quality assurance and clinician confidence in reported results.