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Background <p>This risk assessment study applies failure mode effects analysis to mitigate delays in a high-volume biochemistry laboratory, focusing on failures related to insufficient data storage capacity and to estimate the effect of increasing data storage capacity on test processing time.</p> Method <p>Observational study conducted with the Cobas Infinity server by observing 1516 samples processed to identify failure nodes, and risk priority numbers (RPNs) were calculated.</p> Results <p>Expanding the storage from 1.5 to 2.0&#xa0;TB (terabyte)-processing times were compared using paired t-tests and Wilcoxon Signed-Rank tests. The failure node identified was analytical delay due to inadequate data backup capacity. The RPN score reduced from 20 (high risk) to 5 (low risk) post-intervention, showing a 75% risk reduction. Mean processing time per test decreased significantly from 91.6 to 79.9&#xa0;min (<i>p</i> &lt; 0.001). Sixteen of the 22 tests showed improved processing time after the intervention.</p> Conclusion <p>The study highlights the value of extending FMEA applications into digital infrastructure risks. Proactive mitigation of server storage issues was associated with measurable improvements in test processing times and overall laboratory efficiency. These findings underscore the importance of including Information Technology infrastructure in routine quality and risk audits in modern laboratories.</p>

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A systematic approach to minimizing quality failures in clinical biochemistry reporting using failure mode and effects analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月7日

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  • Sravanthi Poosa,
  • Vijetha Shenoy Belle,
  • Nihaal Maripini,
  • Naveen Kumar Pera

摘要

Background

This risk assessment study applies failure mode effects analysis to mitigate delays in a high-volume biochemistry laboratory, focusing on failures related to insufficient data storage capacity and to estimate the effect of increasing data storage capacity on test processing time.

Method

Observational study conducted with the Cobas Infinity server by observing 1516 samples processed to identify failure nodes, and risk priority numbers (RPNs) were calculated.

Results

Expanding the storage from 1.5 to 2.0 TB (terabyte)-processing times were compared using paired t-tests and Wilcoxon Signed-Rank tests. The failure node identified was analytical delay due to inadequate data backup capacity. The RPN score reduced from 20 (high risk) to 5 (low risk) post-intervention, showing a 75% risk reduction. Mean processing time per test decreased significantly from 91.6 to 79.9 min (p < 0.001). Sixteen of the 22 tests showed improved processing time after the intervention.

Conclusion

The study highlights the value of extending FMEA applications into digital infrastructure risks. Proactive mitigation of server storage issues was associated with measurable improvements in test processing times and overall laboratory efficiency. These findings underscore the importance of including Information Technology infrastructure in routine quality and risk audits in modern laboratories.