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<p>The Immunohematology(IH) laboratory plays a vital role in ensuring safe transfusion practices but is currently facing operational inefficiencies that impact workflow and turnaround time(TAT). To enhance these processes amidst increasing workloads, the Lean Six Sigma(LSS) methodology was employed to identify inefficiencies, implement corrective and preventive actions(CAPA), and evaluate post-implementation outcomes.A prospective interventional study with retrospective analysis was conducted between January and March 2025 to evaluate laboratory workflow and identify non-value-added(NVA) activities. The study revealed key issues such as extended TAT, high sample rejection rates, frequent telephone interruptions, and disorganized staff movements. Following which, CAPA were implemented and evaluated simultaneously from April to June 2025, targeting improvements in workflow design, inventory management, staff communication, and the establishment of standardized procedures. The pre- and post-interventional outcomes were analyzed using SPSS software (version-29). Post-intervention analysis revealed significant TAT improvements, including a 66.7% reduction for blood grouping, a 100% compliance for Coombs tests and crossmatching, and a 70% decrease in phone call frequency and lost time. Additionally, sample rejection rates decreased from 78 to 11 per month, while reducing inventory disruptions, and staff movement by 60%, ultimately enhancing overall productivity. McNemar’s test and t test have revealed statistically significant difference following intervention with large Cohen’s-d effect. The implementation of LSS in the IH lab has led to significant improvements in workflow efficiency, reduction in errors, and enhanced communication. This study highlights the effectiveness of data-driven and human-centered strategies in healthcare, providing a replicable model for other resource-constrained institutions after generalizability and validation of results.</p>

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“Waste Minimization and Workflow Optimization in Immunohematology Laboratory: A Prospective Interventional Study Using Lean Six Sigma Methodology in a Tertiary Care Blood centre”

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Vijit Joon,
  • Rowena D.L Robins,
  • Suresh Kumar,
  • Sriraman Punniakoti,
  • Hari Haran,
  • Sahayaraj James

摘要

The Immunohematology(IH) laboratory plays a vital role in ensuring safe transfusion practices but is currently facing operational inefficiencies that impact workflow and turnaround time(TAT). To enhance these processes amidst increasing workloads, the Lean Six Sigma(LSS) methodology was employed to identify inefficiencies, implement corrective and preventive actions(CAPA), and evaluate post-implementation outcomes.A prospective interventional study with retrospective analysis was conducted between January and March 2025 to evaluate laboratory workflow and identify non-value-added(NVA) activities. The study revealed key issues such as extended TAT, high sample rejection rates, frequent telephone interruptions, and disorganized staff movements. Following which, CAPA were implemented and evaluated simultaneously from April to June 2025, targeting improvements in workflow design, inventory management, staff communication, and the establishment of standardized procedures. The pre- and post-interventional outcomes were analyzed using SPSS software (version-29). Post-intervention analysis revealed significant TAT improvements, including a 66.7% reduction for blood grouping, a 100% compliance for Coombs tests and crossmatching, and a 70% decrease in phone call frequency and lost time. Additionally, sample rejection rates decreased from 78 to 11 per month, while reducing inventory disruptions, and staff movement by 60%, ultimately enhancing overall productivity. McNemar’s test and t test have revealed statistically significant difference following intervention with large Cohen’s-d effect. The implementation of LSS in the IH lab has led to significant improvements in workflow efficiency, reduction in errors, and enhanced communication. This study highlights the effectiveness of data-driven and human-centered strategies in healthcare, providing a replicable model for other resource-constrained institutions after generalizability and validation of results.