ArticleBMJ open quality2026
Reducing medical equipment downtime using Lean Six Sigma: a quality improvement project at Suhul Hospital, Shire.
Article in BMJ open quality, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Medical equipment downtime is a major challenge in healthcare facilities, often driven by mechanical and electrical failures, delayed fault reporting and the absence of structured preventive maintenance. At Suhul Hospital, Shire, these issues were compounded by human error, outdated maintenance practices, limited standards and shortages of spare parts, leading to prolonged equipment unavailability and disrupted patient care.This project applied a Lean Six Sigma (LSS) approach to address these inefficiencies. Key tools included 5S practices, detailed process mapping, corrective maintenance standardisation and a structured preventive maintenance plan. A mixed-methods design was used, combining document review with semi-structured interviews involving 18 purposively selected professionals with at least a diploma and 4 years of relevant experience. Quantitative measures focused on downtime, defects and process capability, while qualitative data explored root causes and waste within the maintenance workflow.Implementation of Lean Six Sigma produced substantial improvements in medical equipment maintenance performance. Total equipment downtime decreased from 89,735 minutes to 292 minutes, while recorded defects decreased from 842 to 43. Overall, downtime decreased by 99.67%, defects by 94.89% and the Sigma level improved from 2.74 to 3.62, indicating a shift from low to moderate process reliability.These findings demonstrate that structured quality improvement methods can significantly enhance the efficiency and reliability of medical equipment maintenance in resource-constrained settings. While the intervention was successful, the project was limited to a single hospital, and further multi-site studies are recommended to strengthen generalisability.
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