ArticleRisk management and healthcare policy2026
Six Sigma and Statistical Process Control in Clinical Pathway Management: An Evaluation Using Coefficient of Variation, Control Charts, and Process Performance Index.
Article in Risk management and healthcare policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence and Enhanced Recovery After Surgery as Patient Safety Strategies for Perioperative Care in Resource-Limited Settings.Risk management and healthcare policy · 2026Article
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4 authors.
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Abstract
Background: With the increasing emphasis on cost control and quality improvement under the Diagnosis-Related Groups (DRG) payment system, clinical pathway management has become a crucial strategy for hospitals. However, systematic, quantitative tools to assess its implementation and impact remain limited. This study explores the application value of Six Sigma-related metrics in clinical pathway management using the example of cholecystectomy for gallbladder polyps. Methods: Hospitalization costs and length of stay were used as evaluation indicators. Six Sigma-related parameters, including the coefficient of variation, Statistical Process Control (SPC) control charts, and process performance indices, were employed to assess the differences before and after the implementation of the clinical pathway. Results: After implementation of the clinical pathway, the average total cost significantly decreased from ¥10,509±1457 to ¥9998.4 ±1370.7 (P < 0.001), and the mean LOS reduced from 4.64±1.47 to 3.90 ± 1.08 days (P < 0.001). Process stability improved markedly, with the CV for cost and LOS dropping from 0.139 to 0.137 and 0.317 to 0.278, respectively. The Ppk for total cost rose from 0.46 to 0.67, corresponding to a reduction in the expected defective rate from 82,101.71 to 21,779.86 Parts Per Million (PPM). SPC control charts demonstrated narrowed control limits and enhanced process precision, indicating that clinical variability was effectively suppressed. Conclusion: Integrating Six Sigma into clinical pathway management provides a robust approach to enhance healthcare delivery under Diagnosis-Related Group(DRG) constraints. Hospital administrators can leverage Statistical Process Control charts for real-time monitoring and train staff in Six Sigma to improve care consistency, while policymakers can establish healthcare-specific process performance benchmarks to advance quality and payment reforms. This method bridges industrial quality control with healthcare, offering scalable benefits for system efficiency and equity.
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