Reducing medical equipment downtime using Lean Six Sigma: a quality improvement project at Suhul Hospital, Shire

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 At Suhul Hospital, Shire, these issues were compounded by human error, outdated maintenance practices, limited standards and shortages of spare parts, leading to prolonged equipment unavailability and disrupted patient care.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.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Medical equipment downtime is a persistent challenge in many low-resource healthcare settings and is often associated with reactive maintenance systems and limited preventive maintenance practices.

WHAT THIS STUDY ADDS

• This quality improvement project demonstrates that Lean Six Sigma implementation was associated with substantial reductions in medical equipment downtime and maintenance defects, together with improved process reliability in a resource-limited hospital setting.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• The findings suggest that structured quality improvement approaches can strengthen biomedical maintenance systems and improve equipment availability in similar healthcare settings.

Problem

Biomedical equipment plays a vital role in delivering safe and effective healthcare, yet hospitals in developing countries frequently experience prolonged equipment downtime due to weak maintenance systems. Evidence shows that ineffective maintenance practices, delayed fault reporting and the absence of standardised procedures contribute to repeated service interruptions in these settings.^{1 2} Despite this, there is limited research on the application of structured quality improvement methodologies particularly Lean Six Sigma (LSS) to enhance equipment reliability in developing country health systems.

Suhul Hospital, a public general hospital located in Shire, Tigray, serves a large catchment population with a wide range of clinical services. The biomedical engineering unit comprises a small team of technicians responsible for managing a diverse set of diagnostic, therapeutic and laboratory devices. Internal reports showed that the hospital had 720 potential productive equipment service hours per month, yet approximately 249.26 hours were routinely lost due to maintenance inefficiencies, equivalent to a 35% downtime rate. This level of downtime disrupted clinical workflows, delayed services and increased the risk of compromised patient care.

The problem became particularly urgent following repeated service interruptions in critical equipment such as laboratory analysers, imaging machines and patient monitors. Staff identified challenges including limited spare parts, inconsistent maintenance routines, lack of preventive maintenance and absence of data-driven decision making. These gaps underscored the need for a structured improvement approach rather than ad-hoc repairs.

The SMART aim of this project was to reduce medical equipment downtime at Suhul Hospital from 35% to less than 10% within 6 months by applying the Lean Six Sigma tools.

This project was initiated after hospital leadership and the biomedical engineering unit recognised persistent inefficiencies and their impact on patient flow, service delivery and operational costs. The goal was not only to improve equipment functionality but also to develop a sustainable, standardised maintenance system that could support long-term reliability and reduce avoidable disruptions in care.

Background

Reliable medical equipment is essential for accurate diagnosis, effective treatment and patient safety. However, in many low- and middle-income countries (LMICs), equipment downtime continues to hinder the delivery of high-quality healthcare services. The WHO estimates that 50%–70% of medical equipment in these settings is either non-functional or under-used, primarily due to inadequate maintenance systems, limited spare-parts availability and insufficient user training.^{3 4} These weaknesses create significant operational burdens and contribute to avoidable delays in service delivery.

Evidence from various countries shows that structured maintenance systems and quality improvement (QI) approaches can significantly reduce medical equipment downtime. In Egypt, the adoption of Lean Six Sigma (LSS) improved equipment availability by strengthening maintenance processes.⁵ A hospital in India applying the DMAIC approach achieved a dramatic reduction in breakdown time of nearly 99.9% by introducing 5S practices, standardised procedures and more effective maintenance routines.⁶ Similarly, Jordanian hospitals implementing Six Sigma DMAIC reported a 35% decrease in equipment downtime after establishing clearer procedures and performance monitoring mechanisms.⁷ Comparable benefits were observed in Burundi, where locally tailored maintenance strategies increased equipment functionality to 80%, compared with the national average of 64%.⁸ Together, these studies highlight how systematic maintenance frameworks can improve reliability even in resource constrained settings.

Despite these positive experiences, persistent challenges continue to undermine equipment performance in many low-resource health systems. In Ghana, recurrent equipment failures were closely linked to limited technical capacity and the absence of planned preventive maintenance, leaving many devices vulnerable to repeated breakdowns.⁹ In India, human-factor issues also played a major role: about 40% of equipment failures were attributed to user errors, largely driven by inadequate training and the lack of standard operating procedures.¹⁰ These findings demonstrate that effective equipment management depends not only on technical solutions but also on strengthening organisational processes and user competencies.

In Ethiopia, existing research mainly focuses on reporting the current status of medical equipment, such as functionality rates and utilisation levels, rather than examining the underlying reasons for equipment downtime. Across several studies, a recurring pattern emerges: equipment frequently remains non-functional for extended periods because preventive maintenance is irregular or entirely absent. In addition, many biomedical engineering units lack the essential systems required for effective equipment management, including standardised workflows, structured fault-reporting mechanisms and quality improvement tools.^{2 11–13} As a result, the literature highlights gaps not only in technical maintenance practices but also in the organisational systems that support reliable equipment performance.

Measurement

A combination of quantitative and qualitative measures was used to assess baseline performance and evaluate the impact of the Lean Six Sigma (LSS) intervention. The primary outcome measure was medical equipment downtime (minutes per month). This measure was selected because it directly reflects equipment availability and patient service continuity, and it is consistently documented in routine maintenance logs. Secondary outcome measures included:

1. Number of defects per month,

2. Number of defect opportunities,

3. Yield rate,

4. Defects Per Million Opportunities (DPMO) and

5. Sigma level.

These measures are widely used in Six Sigma programmes to assess process stability, reliability and variation.

Operational definitions were standardised prior to data collection:

• Case: A single recorded medical equipment requirement maintenance service event requiring inspection, corrective maintenance, preventive maintenance, adjustment, repair or related technical intervention during study period.

• Defect: A recorded medical equipment failure or malfunction requiring a maintenance intervention.

• Defect opportunities: For the purposes of Six Sigma measurement, defect opportunities were operationally defined using the major recurrent categories of maintenance-related failure identified through Pareto analysis. This pragmatic approach was adopted to support process measurement within a resource-limited hospital setting. As the number of counted defect opportunity categories changed between baseline, implementation and follow-up phases, interpretation of DPO, DPMO and sigma-level improvement should be made cautiously.

• Downtime: The total duration during which medical equipment remained unavailable for clinical use due to malfunction, maintenance or repair activities.

Data sources and collection procedure

Maintenance logbooks and breakdown reports

Quantitative data were extracted from:

• Routine biomedical maintenance log books

• Breakdown reports

• Workshop records

Baseline data were collected retrospectively for January–June 2023 (online supplemental appendix 3), ensuring complete monthly records of downtime, defect counts, defect opportunities and maintenance response timelines. Implementation-phase data were collected prospectively from July–December 2023 (online supplemental appendix 12), during which Lean Six Sigma interventions were introduced and applied. Follow-up data used to assess the sustainability of the intervention were collected from January–March 2024 (online supplemental appendix 14). The same hospital maintenance records, reporting systems and measurement procedures were used throughout all study phases to maintain consistency and comparability of the data.

To strengthen validity and reliability, data were cross-checked against:

• Ward-level incident reports

• Technician feedback

• Weekly maintenance summaries

Missing entries were verified through direct communication with biomedical technicians.

Interview

Semi-structured interviews were conducted with biomedical engineers, technicians, nurses and equipment operators. A purposive sampling method was used to select 18 participants with at least a diploma and 4 years of relevant experience, and direct involvement in equipment operation, troubleshooting or maintenance. Interviews explored perceived causes of downtime, workflow gaps, documentation issues and human-factor contributions to equipment failure. These qualitative insights informed the development of operational definitions, validation of logbook data and identification of hidden waste not captured in documents.

Document review

Additional qualitative data were obtained from:

• WHO Work Instruction Procedures

• Hospital Standard Operating Procedures

• 5S checklists

• Preventive maintenance guidelines

These documents provided reference standards for comparing current practice and for assessing the accuracy of routine data.

Use of baseline measurement

Baseline analysis revealed:

• Total downtime: 89 735 min

• Defect opportunities: 7 categories

• Sigma level: 2.74, indicating substantial process instability

These baseline results guided prioritisation of high-impact equipment, identification of major failure modes (electrical, mechanical and installation-related) and selection of LSS tools for improvement.

Attribution of change

The same measures were tracked monthly during and after implementation. Run charts and I-MR control charts were used to differentiate random variation from special-cause signals. Consistent operational definitions, standardised documentation and prospective data collection helped ensure that any observed improvements were attributable to the interventions rather than measurement bias.

SQUIRE 2.0 reporting standard

This manuscript was prepared in accordance with the SQUIRE 2.0 reporting guidelines for healthcare quality improvement studies.¹⁴

Design

The intervention was designed using the Lean Six Sigma DMAIC framework, informed by quantitative performance data and qualitative findings from interviews and document reviews. The goal was to improve equipment reliability by addressing workflow inefficiencies, variation in maintenance practices, and absence of preventive maintenance structures.

Intervention components

Workflow redesign and new process map

Interviews revealed inconsistent reporting pathways and delays in communicating faults. A simplified process map was co-designed with biomedical staff, nurses and equipment operators to clarify roles, reduce delays and streamline escalation steps. This redesign was expected to improve flow by removing redundant loops, a core Lean principle.

5S workplace organisation

5S was implemented in the maintenance workshop and store to improve tool accessibility, reduce time spent searching for parts and standardise work areas. Interview feedback repeatedly identified disorganisation and missing tools as contributors to prolonged downtime.

Standardised corrective maintenance procure

A unified corrective maintenance form and troubleshooting sequence were introduced, addressing wide variability in technician practices. Standardisation improves consistency, reduces diagnostic errors, and facilitates root-cause analysis.

Preventive maintenance programme

A monthly and quarterly preventive maintenance schedule was developed for high-use and high-risk equipment identified through Pareto analysis. The assumption was that a structured inspection plan would prevent avoidable breakdowns and stabilise process performance over time.

Team engagement and governance

The project team included biomedical engineers, technicians, nurses, equipment operators, store staff and two clinical supervisors. Weekly meetings were held to validate findings, agree on workflow changes and ensure feasibility. The participatory approach fostered ownership, reduced resistance, and ensured that interventions matched actual clinical realities.

Anticipated changes and mitigations

Predicted challenges included staff non-compliance, limited spare parts, inconsistent documentation and time constraints. These risks were mitigated by:

• Short practical orientations

• Integrating checklists into existing routines

• Using locally available organisational materials

• Assigning clear responsibilities for preventive maintenance tasks

Ensuring sustainability

To embed the changes, a PDCA monitoring system was instituted. Monthly review meetings, I-MR chart tracking and periodic 5S audits were integrated into the hospital’s management workflow. Responsibility for ongoing monitoring was assigned to the biomedical unit, ensuring continuity beyond the project period.

Strategy

The improvement strategy followed a structured Lean Six Sigma (LSS) approach anchored in the DMAIC framework and executed through iterative Plan–Do–Study–Act (PDSA) cycles. The goal was to progressively refine maintenance processes, test small-scale changes before full implementation and ensure continuous learning throughout the project. Data were collected continuously through maintenance logbooks, 5S checklists, defect reports and weekly review meetings, enabling real-time monitoring of outcomes and rapid course correction.

PDSA cycle 1: Clarifying the maintenance workflow (Process mapping redesign)

Aim

To understand the existing workflow and identify sources of delay, waste, and repeated failures (online supplemental appendix 5).

Hypothesis

If the team visualised the current process through detailed mapping, hidden bottlenecks would surface, allowing targeted improvement in subsequent cycles.

Strategy and implementation

• The project team conducted observation sessions and interviews with technicians, nurses, equipment operators and store staff.

• A high-level SIPOC (online supplemental appendix 1) and an old process map were reconstructed from real workflows.

Data collected

Cycle times, waiting times between fault identification and reporting, number of handoffs, and documentation errors.

Learning

The existing process was highly fragmented, with inconsistent fault reporting and unclear responsibilities. This learning confirmed that subsequent cycles needed to address reporting delays and communication barriers.

Outcome

Provided baseline clarity but no immediate reduction in downtime; however, it established the foundation for the next interventions.

PDSA cycle 2: Improving fault reporting and corrective maintenance

Aim

To reduce delays between equipment failures, reporting, and repair.

Hypothesis

Standardising the reporting workflow and assigning clear responsibilities would decrease response time and reduce avoidable downtime.

Strategy and implementation

• Introduced a standardised fault-reporting form.

• Created a direct communication channel between departments and the biomedical unit.

• Established a prioritisation system based on criticality (from the Pareto distribution and failure data).

Data collected

Time from failure to report submission, number of incomplete reports, and frequency of technician response within 24 hours.

Learning

Departments quickly adapted to the reporting form, but initial delays persisted due to unavailability of spare parts. This insight led to integrating stores personnel into weekly planning meetings.

Outcome

The number of incomplete reports reduced significantly, and repair initiation times improved, contributing to early reductions in downtime.

PDSA cycle 3: Implementing 5S to improve workspace efficiency

Aim

To reduce waste related to motion, searching and disorganisation in the biomedical workshop.

Hypothesis

Applying 5S principles would improve technician efficiency, reduce search time for tools and spare parts, and indirectly reduce repair time.

Strategy and implementation

• The biomedical workshop was reorganised using Sort, Set in Order, Shine, Standardise, and Sustain.

• Visual labels and storage shelves were introduced.

• Weekly 5S audits were initiated with the project team.

Data collected

5S audit scores, average repair duration and frequency of missing tools or materials.

Learning

5S improved the working environment almost immediately, reducing clutter and search time. However, sustaining the ‘Standardise’ and ‘Sustain’ phases required repeated coaching.

Outcome

Repair time decreased measurably, supporting the downward trend in overall downtime.

PDSA cycle 4: Developing and testing the preventive maintenance (PM) plan

Aim

To reduce repeat failures and avoidable breakdowns by establishing a routine preventive maintenance schedule.

Hypothesis

Systematic PM would reduce the number of defects and slow the progression of wear-related failures.

Strategy and implementation

• Using Pareto analysis, the top failure-prone equipment were prioritised (online supplemental appendix 6).

• PM tasks were developed using WHO guidelines and manufacturer instructions.

• A monthly PM schedule was piloted for three high-risk devices.

Data collected

PM completion rates, repeat failures, downtime associated with the prioritised equipment.

Learning

PM effectiveness improved when operators were involved in basic care tasks (eg, proper shutdown procedures). Lack of replacement parts remained a barrier but was mitigated through planned procurement.

Outcome

Repeat failures decreased sharply, and prioritised equipment showed the earliest improvements in downtime and defect frequency.

PDSA cycle 5: Establishing control and sustainability mechanisms

Aim

To stabilise improvements and prevent regression to old practices.

Hypothesis

Continuous monitoring with I-MR charts and PDCA cycles (online supplemental appendix 13) would maintain process stability and support long-term improvement.

Strategy and implementation

• Weekly review meetings evaluated downtime, defects and PM performance.

• PDCA cycles were incorporated into routine departmental activities.

• A maintenance monitoring checklist was introduced for daily use.

Data collected

I-MR control chart data, monthly downtime levels, defect count and PM adherence.

Learning

After 3 months post-implementation, the process remained in statistical control, confirming sustainability.

Outcome

Downtime remained low (26–40 min per month), defect opportunities reduced from 7 to 5 and the process demonstrated stable performance across subsequent months.

Overall strategic learning

The improvement cycles demonstrated that small, structured changes when guided by data and implemented iteratively can lead to dramatic operational gains even in resource-limited hospitals. Not all interventions worked immediately: spare-part constraints and inconsistent staff engagement required repeated adjustments. However, the cumulative learning from each cycle shaped the next, contributing to a sustained reduction in downtime and an improvement in Sigma level.

Results

Baseline performance (before intervention)

The pre-intervention assessment (January–June 2023) revealed substantial inefficiencies in Suhul Hospital’s biomedical maintenance system. Across the 6 month period, total medical equipment downtime reached 89 735 min, with 842 recorded defects and 12 maintenance-related failure categories were identified (online supplemental appendix 3). Mechanical failures, electrical problems, installation, electronic, accessory, alignment-related errors and adjustment issues were the dominant contributors, as reflected in the Pareto analysis (online supplemental appendix 2).

The baseline run chart (figure 1) demonstrated high variability and recurrent spikes in downtime, indicating an unstable and reactive maintenance environment (online supplemental appendix 4). The absence of a preventive maintenance plan, inconsistent corrective procedures and limited communication between clinical and maintenance teams were major factors contributing to prolonged equipment unavailability (online supplemental appendix 7).

Figure 1. Downtime and sigma level during baseline period.

The initial sigma level was 2.74 (online supplemental appendix 3), corresponding to high process variation and low reliability, typical in low-resource maintenance settings without structured quality systems.

Lean Six Sigma implementation and post-intervention performance

Following the implementation of standardised corrective procedures (online supplemental appendix 10), a preventive maintenance plan (online supplemental appendix 11), 5S workspace reorganisation (online supplemental appendix 9) and improved reporting workflows (online supplemental appendix 8), significant improvements were observed during the implementation phase (July – December 2023) (online supplemental appendix 12), and these improvements were sustained during follow-up/control phase (January – March 2024) (online supplemental appendix 14).

Total equipment downtime decreased dramatically from 89,735 minutes to 292 minutes, representing a 99.67% reduction. Recorded defects decreased from 842 to 43, corresponding to a 94.89% reduction. In addition, the number of identified maintenance-related defect opportunities decreased from seven during the baseline phase to five during the implementation phase (online supplemental appendix 13).

For Six Sigma measurement, defect opportunities were defined using the major recurrent defect categories identified through Pareto analysis. During the baseline phase, seven high-frequency defect opportunity categories accounting for most maintenance-related failures were included in DPO, DPMO and Sigma calculations. Following implementation, several low-frequency categories became operationally negligible, resulting in five counted defect opportunities during the implementation and follow-up phases. As changes in counted defect opportunities may influence DPO, DPMO and Sigma calculations, Sigma comparisons between study phases should be interpreted cautiously.

The number of recorded maintenance cases differed between study phases because implementation of Lean Six Sigma introduced preventive maintenance scheduling, improved reporting workflows and earlier identification of equipment issues, which reduced repeated breakdown-related maintenance events during implementation phase (online supplemental appendix 12) and follow-up period (online supplemental appendix 14).

The combined bar graph (figure 2) visually highlights the substantial reduction in medical equipment downtime and the corresponding improvement in Sigma level following Lean Six Sigma implementation. These improvements were supported by better adherence to maintenance protocols, availability of updated checklists (online supplemental appendix 15) and enhanced coordination between end-users and biomedical staff.

Figure 2. Downtime and Sigma level during the implementation phase.

The Sigma level increased from 2.74 to 3.62 during the implementation phase, indicating a shift toward moderate process reliability and reduced variation in maintenance outcomes.

Comparative improvements

Table-based calculations confirmed substantial gains across all performance metrics:

• Downtime reduction: 99.67%

• Defect reduction: 94.89%

• Reduction in defect opportunities: 58%

• Sigma level improvement: from 2.74 to 3.62

• Reporting time improved through streamlined communication channels

• Maintenance tasks standardised through updated protocols and workflows

Operationally, equipment became more consistently available for patient care, and maintenance teams experienced fewer repeated failures and rework cycles.

Process stability and control

To assess sustainability and process stability, an I-MR control chart (figure 3) was recalculated using implementation phase and follow-up data (July 2023–March 2024). The chart demonstrated improved process stability following Lean Six Sigma implementation. Although one special-cause observation was identified during the initial implementation month, likely reflecting the transition period associated with intervention deployment, subsequent observations remained within control limits, indicating a stable and predictable maintenance system over time (online supplemental appendix 14).

Figure 3. I-MR control chart for implementation and follow-up periods.

The consistency observed during the later implementation-phase and follow-up periods suggests that the improvements were sustained through continuous monitoring, improved documentation practices and adherence to preventive maintenance routines.

Achievement of SMART aim

The primary outcome was also assessed using downtime rate relative to productive equipment service hours, consistent with the project SMART aim. Before intervention, the hospital lost approximately 249.26 productive service hours per month out of 720 potential service hours, corresponding to a downtime rate of approximately 35%. Following Lean Six Sigma implementation, average monthly downtime decreased to less than one productive service hour per month, representing a downtime rate of less than 1%. This exceeded the project target of reducing downtime to below 10% within 6 months.

Interpretation of findings

Although the observed improvements were substantial, the findings should be interpreted cautiously. Regression to the mean, improvements in documentation practices and possible inconsistencies in retrospective baseline data capture may have partially contributed to the magnitude of observed reductions. However, the intervention was implemented within a resource-limited hospital setting with previously limited preventive maintenance systems, where large operational inefficiencies existed at baseline. Similar substantial improvements have also been reported in comparable quality-improvement interventions conducted in low- and middle-income healthcare settings. These findings support the plausibility of meaningful performance gains following structured Lean Six Sigma implementation in biomedical maintenance systems.

Summary of key outcomes

1. Lean Six Sigma interventions resulted in substantial, quantifiable, and sustained improvements in medical equipment maintenance.

2. Downtime, defects and defect opportunities all declined sharply after structured process redesign.

3. System stability improved, as evidenced by the control chart during the follow-up period.

4. Suhul Hospital’s biomedical maintenance workflow shifted from reactive to proactive, leading to enhanced equipment availability and reliability.

Before and after photographic evidence

Laboratory room and equipment (online supplemental appendix 16), X-ray room and equipment (online supplemental appendix 17), gas cylinders (online supplemental appendix 18), electrical bed (online supplemental appendix 19), hospital bed (online supplemental appendix 20) and sustainability / continuous improvement board (online supplemental appendix 21).

Lessons and limitations

This project generated several important lessons regarding the application of Lean Six Sigma (LSS) in a resource-limited hospital setting. One key lesson was the value of engaging multidisciplinary staff from the outset. Biomedical technicians, nurses, operators and supply staff provided essential insights into workflow constraints, communication gaps and equipment handling practices that were not visible in formal protocols. Their involvement ensured that the redesigned process map, preventive maintenance plan and 5S improvements were practical and aligned with real workplace needs.

Another central lesson was the importance of structured improvement cycles. The iterative nature of the DMAIC approach enabled the project team to refine interventions as learning emerged. For example, early 5S activities revealed opportunities to further organise tools and spare parts, which were incorporated into subsequent cycles. Adjusting preventive maintenance scheduling based on staff feedback also demonstrated how flexibility and continuous learning strengthen the success of quality-improvement efforts. These experiences confirmed that LSS creates a culture of improvement when teams are empowered to reflect and adapt.

Overall, the strengths of this project lie in its structured methodology, strong team engagement and clear measurement strategy. Efforts were made throughout implementation to ensure continuous improvement and to maintain the reliability of measurement processes. The positive outcomes demonstrate that Lean Six Sigma can be effectively adapted to strengthen biomedical maintenance systems in resource-limited environments, while the lessons learnt provide a foundation for wider application and future research.

If this project were to be undertaken again, several refinements would strengthen the design. In particular, long-term sustainability beyond the 9 month follow-up period remains uncertain. Continued monitoring over several years would be necessary to determine whether the gains are maintained over time.

Despite these strengths, several limitations should be acknowledged. First, the project relied on data from a single hospital, which limits generalisability to other institutions with different equipment profiles, staffing structures or maintenance resources. While the findings are contextually strong, external applicability may be restricted until replicated across multiple facilities.

Second, the sample size for qualitative interviews (18 participants) was appropriate for a focused LSS project but may not have captured all operational behaviours influencing equipment downtime. Staff turnover during the study period also meant that some perspectives were lost, which could introduce a degree of selection bias.

Efforts were made throughout the project to minimise limitations. Data verification was performed by comparing logbooks, breakdown reports and technician notes; staff were retrained on documentation procedures and the preventive maintenance plan was piloted before full implementation to check feasibility.

Another limitation relates to the absence of formal balancing measures. Although the project demonstrated substantial reductions in downtime and defects, the study did not systematically evaluate whether the interventions created unintended consequences elsewhere in the hospital system, such as increased staff workload, delays in other maintenance activities or shifts in resource allocation. Future quality-improvement work should incorporate balancing indicators to assess potential trade-offs associated with process redesign.

In addition, a full economic evaluation was not conducted. While reductions in downtime likely improved equipment availability and operational efficiency, the study did not formally quantify cost savings, return on investment, labour costs or the financial implications of preventive maintenance activities. Future studies should include structured cost-effectiveness or cost-benefit analyses to better assess the economic impact of Lean Six Sigma implementation in resource-limited healthcare settings.

Finally, interpretation of the observed Sigma level improvement should be approached cautiously because the number of cases and counted defect opportunities changed between baseline, implementation and follow-up phases. These variations may have influenced DPO, DPMO and Sigma calculations, limiting direct comparability across study periods.

Future work should explore applying the model across several hospitals, integrating digital maintenance tracking systems and evaluating the cost-benefit implications of LSS in biomedical engineering departments. Additional research could also examine how structured training and capacity building influence technician performance and long-term equipment reliability.

Conclusion

This improvement project set out to address a well-documented challenge in healthcare systems: persistent equipment downtime due to inefficient maintenance processes. Previous literature highlights that ineffective workflows and lack of structured maintenance systems are major contributors to reduced equipment availability. Building on this understanding, our project applied Lean Six Sigma to a low-resource hospital setting, demonstrating that structured improvement methodologies can be both feasible and effective in strengthening biomedical maintenance performance.

The project aimed to reduce equipment downtime and improve workflow efficiency in the biomedical unit at Suhul Hospital through targeted, data-driven interventions, and this aim was achieved. Downtime decreased substantially, turnaround time improved and the frequency of recurring defects declined. These outcomes suggest that the measures selected including productive hours lost, number of failures and response time were appropriate, sensitive to change and aligned with the operational nature of the problem. While formal balancing measures were not used, informal monitoring showed no negative effects on staff workload or service flow.

The improvements also created indirect cost benefits by reducing repeated failures, preventing long repair delays and avoiding reliance on external maintenance support. Although a full economic evaluation was beyond the project scope, the reduction in breakdown frequency and the more efficient allocation of technician time indicate a favourable financial effect for the hospital.

Measures to ensure sustainability included a standardised preventive maintenance plan, 5S routines, clearer corrective maintenance procedures and a PDCA-based monitoring mechanism. Post-implementation control charts demonstrated steady performance, suggesting that the new processes are likely to hold if documentation and regular reviews continue. The model requires minimal resources and therefore can be replicated in similar public hospitals with comparable constraints.

This work demonstrates that a structured, data-driven improvement approach can significantly strengthen equipment maintenance in a resource-limited environment. It provides practical evidence for hospital leaders and policymakers seeking efficient, low-cost strategies to reduce downtime and improve service reliability. Future work may include integrating digital documentation tools, expanding the approach to other departments and testing the model across multiple facilities to further validate and refine the findings.

Data availability statement

Data are available upon reasonable request.