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LEAN SIX SIGMA METHOD TO REDUCE LENGTH OF STAY IN
EMERGENCY DEPARTMENT: SCOPING REVIEW
Metode Lean Six Sigma Untuk Menurunkan Length of Stay di Instalasi Gawat Darurat: Scoping Review Mifaul Azmi1*.
Sri Sundari1,2 Master of Hospital Administration.
Post Graduate Programme.
Universitas Muhammadiyah Yogyakarta.
Bantul.
Indonesia Department of Family Medicine and Public Health.
School of Medicine.
Faculty of Medicine and Health Sciences.
Universitas Muhammadiyah Yogyakarta.
Bantul.
Indonesia *Email: azmi.
faul@gmail.
ABSTRAK
Instalasi Gawat Darurat (IGD) merupakan bagian yang sangat penting dalam sistem perawatan kesehatan.
Length of Stay (LOS) salah satu indikator kinerja utama yang secara signifikan mempengaruhi outcome pasien, kualitas pelayanan dan kepuasan Scoping review ini meneliti efektivitas metodologi Lean Six Sigma (LSS) dalam menurunkan LOS di IGD dan meningkatkan throughput pasien di berbagai fasilitas pelayanan kesehatan.
Empat database elektronik (MEDLINE via PubMed.
Scopus.
Emerald, dan ProQues.
ditelusuri untuk mencari literatur terkait dalam review ini.
Review ini disusun menggunakan enam tahap metode scoping review sesuai dengan panduan metode PRISMA-ScR.
Review ini berfokus pada implementasi yang menerapkan kerangka kerja Define-Measure-Analyze-Improve-Control, yang dilengkapi dengan pendekatan analitik dan manajemen perubahan.
Ekstraksi data mencakup metodologi, hasil kuantitatif, strategi implementasi, dan faktor kontekstual yang memengaruhi keberhasilan.
Dalam review ini, teridentifikasi 19 artikel yang memenuhi Hasil penelitian menunjukkan bahwa metode LSS secara signifikan mengurangi lama tinggal di IGD mulai dari 14% hingga 50%.
Integrasi dengan model machine learning meningkatkan akurasi prediksi waktu tunggu pasien.
Teknik simulasi mengoptimalkan alur pasien, dan pendekatan manajemen perubahan yang terstruktur memastikan perbaikan yang berkelanjutan dan melibatkan partisipasi staf.
Dukungan kepemimpinan dan penggunaan sistem data yang efektif diidentifikasi sebagai faktor penentu keberhasilan, sementara tantangan yang dihadapi termasuk resistensi terhadap perubahan dan keterbatasan sumber daya.
Metodologi LSS efektif dalam meningkatkan efisiensi dan mengurangi lama tinggal pasien di IGD, memberikan model yang dapat direplikasi yang bertujuan untuk meningkatkan efisiensi operasional dan perawatan pasien.
Kata kunci: lean six sigma, length of stay, unit gawat darurat
ABSTRACT
Emergency Departments serve as critical entry points in healthcare systems.
Length of Stay has emerged as a key performance indicator that significantly influences patient outcomes, healthcare delivery quality, and patient satisfaction.
This scoping review examined the effectiveness of Lean Six Sigma methodologies in reducing emergency department length of stay and improving patient throughput across diverse healthcare Four electronic databases (MEDLINE via PubMed.
Scopus.
Emerald, and ProQues.
were searched for related literature in this review.
A six-step scoping review was performed following the guidance of the PRISMA-ScR method.
The review focused on implementations using the Define-Measure-Analyze-Improve-Control framework, often enhanced with advanced analytics and change management approaches.
Data extraction covered methodology, quantitative outcomes, implementation strategies, and contextual factors affecting success.
19 eligible articles were identified in this review.
The https://doi.
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MEDIA PENELITIAN DAN
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Desember 2025 e-ISSN: 2338-3445 p-ISSN: 0853-9987 results indicated that the LSS method significantly reduced emergency department length of stay, ranging from 14% to 50%.
The integration of machine learning models improved predictive accuracy for patient wait times.
Simulation techniques further optimized patient flow, and structured change management approaches ensured sustainable improvements and engaged staff participation.
Leadership support and effective data systems were identified as critical success factors, while challenges included resistance to change and resource constraints.
Lean Six Sigma methodologies are effective in enhancing efficiency and reducing the length of stay in emergency departments, providing a replicable model for healthcare systems aiming to improve operational efficiency and patient care.
Keywords: emergency department, lean six sigma, length of stay
INTRODUCTION
Emergency Departments (ED.
are indispensable components of global healthcare systems, providing crucial immediate medical attention for individuals presenting with acute illnesses and injuries.
The operational efficiency and effectiveness of these departments are paramount, directly influencing patient outcomes, satisfaction levels, and overall healthcare expenditure.
A pervasive challenge in ED operations is the Length of Stay (LOS), defined as the total duration a patient spends within the department from initial arrival to eventual discharge.
Elevated LOS is strongly correlated with a cascade of negative consequences, including diminished patient satisfaction, an increased propensity for adverse events, and a reduction in ED throughput.
Moreover, protracted LOS contributes significantly to overcrowding, which subsequently intensifies the burden on healthcare resources and staff, leading to burnout and a decrease in the quality of care provided .
, .
Numerous strategies have been employed worldwide to enhance ED performance and reduce LOS, encompassing demand management, critical path analysis, stream mapping, queuing systems optimization, triage emergency severity index implementation.
Lean and Six Sigma management methodologies, bedside registration, statistical forecasting, conceptual and mathematical modeling, discrete event simulation, and balanced scorecard approaches .
Among these.
Lean and Six Sigma have emerged as particularly popular and widely adopted methods for implementing operational changes.
The Institute of Medicine (IOM) has specifically advocated for the application of Lean and Six Sigma management concepts within hospitals to address pressing issues such as extended waiting times and to augment overall efficiency without compromising service quality .
Lean Six Sigma (LSS) is a powerful, integrated methodology that synergistically combines the principles of Lean, which primarily focuses on identifying and eliminating waste while optimizing process flows, with Six Sigma techniques, which are rigorously applied to reduce variability and enhance the quality of outcomes.
Originating in the manufacturing sector.
LSS has progressively been embraced by healthcare settings, proving instrumental in tackling diverse operational challenges, notably the reduction of LOS in EDs .
, .
The systematic application of LSS in ED environments typically involves a structured approach to pinpointing inefficiencies, streamlining existing workflows, and implementing evidence-based interventions specifically designed to improve patient flow and thereby decrease LOS .
Various studies have documented the impact of LSS interventions on LOS across a spectrum of ED settings, revealing a mixed yet generally positive outcome picture.
Some research robustly indicates that LSS can lead to substantial reductions in LOS, achieving improvements through enhanced patient flow, optimized resource allocation, and strengthened interdepartmental coordination .
For example, prior investigations have reported considerable decreases in LOS, ranging from 10% to 30%, following the meticulous implementation of LSS projects .
https://doi.
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MEDIA PENELITIAN DAN
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Vol 35 No 4.
Desember 2025 e-ISSN: 2338-3445 p-ISSN: 0853-9987 Despite the compelling body of evidence affirming Lean Six SigmaAos positive influence, significant challenges in its effective implementation persistently hinder its full Common barriers encountered include, but are not limited to, insufficient leadership commitment, inherent resistance to change among clinical and administrative staff, a discernible lack of expertise in sophisticated process improvement methodologies, and the critical need for sustained measurement and robust control mechanisms to prevent regression of improvements .
, .
, .
Identifying and meticulously scrutinizing the existing gaps and limitations within the current literature is thus paramount for advancing the practical application of LSS in EDs and ensuring its maximum effectiveness in reducing LOS.
Preliminary reviews already indicate several critical areas where existing studies exhibit shortcomings.
These include a notable lack of standardized measures for both LOS and the LSS interventions themselves, a scarcity of longitudinal studies essential for assessing the long-term sustainability of implemented improvements, and an insufficient exploration into the nuanced barriers and facilitators that either impede or bolster successful LSS implementation.
Furthermore, there is a recognized gap in understanding how LSS interventions adapt and perform across diverse contextual factors such as variations in hospital size, unique patient populations, and differing healthcare system structures.
The adaptability and scalability of LSS interventions are inherently influenced by these settings, directly impacting their ultimate effectiveness in reducing LOS .
Addressing these identified gaps through a comprehensive scoping review will not only meticulously synthesize the existing knowledge base but also critically inform future research directions and guide practical applications in real-world ED environments.
systematically examining these contextual factors, this review will offer profound insights into how LSS methodologies can be precisely customized to meet the specific and varied needs of different ED settings, thereby significantly enhancing their applicability, relevance, and ultimately, their impact.
This study specifically aims to highlight successful LSS strategies, identify the most common challenges encountered during their implementation, and propose specific areas for further in-depth investigation.
Through this rigorous approach, this article contributes significantly to the existing body of knowledge by fostering a more nuanced and granular understanding of how LSS can be effectively and sustainably integrated into ED operations to achieve meaningful, tangible reductions in LOS and, consequently, improve overall healthcare delivery quality.
METHODS
Study Design This study employed a scoping review methodology following the framework proposed by Arksey and OAoMalley and further enhanced by Levac.
Colquhoun, and OAoBrien .
This methodology was chosen to systematically map the available evidence regarding Lean Six Sigma implementation in emergency departments, specifically focusing on Length of Stay (LOS) reduction initiatives.
The review protocol was developed in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review.
study protocol, including formulating the study question, defining the inclusion criteria, search strategy, retrieving the relevant studies, extracting the relevant data, synthesizing the data, and creating a report, was developed by the research team .
Eligibility Criteria Studies were selected based on inclusion criteria including peer-reviewed articles published in English.
Studies published from 2015 to 2025, primary research studies implementing Lean Six Sigma methodology, studies conducted in emergency department settings, clear reporting of LOS as a primary or secondary outcome, and both prospective and retrospective studies.
while exclusion criteria encompassed https://doi.
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Table 1 provides a summary of the eligibility criteria.
Table 1.
Inclusion and Exclusion Criteria Criteria Healthcare facility with operational emergency departments.
Implementation of Lean Six Sigma methodologies in the Emergency Departments.
Items Population Intervention Comparison Outcomes Time and Language Study Design Studies reporting on the impact of Lean Six Sigma on LOS in emergency Improvement strategies implemented to obtain LOS reduction.
Studies published from March 2015 to April 2025.
Publication in English.
Empirical research articles, case studies, and quality improvement reports are published in peer-reviewed journals.
Search Strategy A comprehensive search strategy was developed in consultation with an experienced medical librarian.
The following electronic databases were systematically searched:
MEDLINE via PubMed.
Scopus.
Emerald, and ProQuest.
The search period covered March 2015 to April 2025, as Lean Six Sigma implementation in healthcare gained prominence during this period.
The search string incorporated relevant Medical Subject Headings (MeSH) terms and keywords in Table 2.
Table 2.
Search Strategy Database PubMed Scopus ProQuest Emerald Keyword ("Lean Six Sigma"[All Field.
OR "LSS"[Title/Abstrac.
OR "Lean methodology"[Title/Abstrac.
"Six Sigma"[Title/Abstrac.
"DMAIC"[Title/Abstrac.
OR "Lean healthcare"[Title/Abstrac.
OR "Lean hospital"[Title/Abstrac.
) AND ("Length of Stay"[All Field.
OR "LOS"[Title/Abstrac.
OR "Hospital stay"[Title/Abstrac.
OR "Patient stay"[Title/Abstrac.
OR "Duration of stay"[Title/Abstrac.
OR "Hospitalization length"[Title/Abstrac.
OR "Patient discharge time"[Title/Abstrac.
) AND ("Emergency Department"[All Field.
OR "ED"[Title/Abstrac.
OR "Emergency Room*"[Title/Abstrac.
"ER"[Title/Abstrac.
"Emergency Unit"[Title/Abstrac.
OR "Emergency Service"[Title/Abstrac.
OR "Emergency Care"[Title/Abstrac.
OR "Acute Care"[Title/Abstrac.
) ("Lean Six Sigma" OR "LSS" OR "Lean methodology" OR "Six Sigma" OR "DMAIC" OR "Lean healthcare" OR "Lean hospital") AND ("Length of Stay" OR "LOS" OR "Hospital stay" OR "Patient stay" OR "Duration of stay" OR "Hospitalization length" OR "Patient discharge time") AND ("Emergency Department" OR "ED" OR "Emergency Room" OR "ER" OR "Emergency Unit" OR "Emergency Service" OR "Emergency Care" OR "Acute Care") (("Lean Six Sigma" OR "LSS") AND ("Length of Stay" OR "LOS" OR "Patient discharge time") AND ("Emergency Department" OR "ED" OR ("emergency room" OR "emergency rooms") OR "ER" OR "Emergency Unit")) Lean Six Sigma AND Length of Stay AND Emergency Department Article Study Selection Process The selection process involved two main phases: title and abstract screening, followed by full-text review.
Two authors who had enough related experience and knowledge were responsible for independently extracting the data.
In the first phase of the article selection, articles with non-relevant titles were excluded.
In the second phase, the abstract and the full text of articles were reviewed to include those articles that matched the inclusion criteria.
The application (Rayyan.
was used for organizing and assessing the titles and abstracts, as well as for identifying duplicate entries.
The selection process was documented using a PRISMA flow diagram.
Disagreements were https://doi.
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= .
Scopus .
= .
Records screened from title and abstract .
= 1.
Records screened from full text .
= .
Included Eligibility Screening Identification resolved through discussion to ensure accuracy and consistency.
The whole search process has been displayed in Figure 1.
Data Extraction Data extraction was performed using a standardized form designed to capture key information from the included studies.
The form included fields such as:
Study characteristics: author, year, country, setting.
Methodology: Lean Six Sigma framework .
or example.
DMAIC), integration with tools such as discrete event simulation or machine learning, and change management models used.
Outcomes: percentage reduction in length of stay and discharge-related metrics .
or example, time from discharge order to patient departur.
Implementation facilitators and barriers were reported.
Data Analysis and Synthesis The extracted data were subjected to a descriptive thematic analysis.
Quantitative data related to LOS reductions were summarized using summary statistics, while qualitative insights on challenges and facilitators were categorized into themes.
The synthesis aimed to provide a comprehensive overview of how LSS has been utilized to address LOS issues in EDs, highlighting effective strategies and common obstacles.
Quality Assessment As this was a scoping review, a formal assessment of methodological quality was not conducted.
However, relevant study characteristics, such as study design, sample size, and rigor of statistical analyses, were considered when interpreting the findings.
Records included in this review.
= .
ProQuest .
= .
Emerald .
= .
Duplicate Records .
= .
Records excluded .
= 1.
Records excluded, with reasons .
= .
Not responding to the research questions .
= .
Ineligible study design .
= .
Ineligible publication type .
= .
Ineligible setting .
= .
Figure 1.
PRISMA Flow Diagram Illustrating the Study Selection Process https://doi.
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RESULT
Quantitative Reductions in Length of Stay (LOS) LSS consistently reduced LOS across diverse healthcare settings, with reported reductions ranging from 9% to 39%.
For instance, a 30% LOS reduction was documented by Furterer .
in a U.
Emergency Department (ED) and by Shakoor et al.
in a Pakistani cancer hospital ED, highlighting LSS's effectiveness across different economic contexts .
, .
Kenny.
Rosania, and Lu .
observed a 9% LOS reduction in a high-volume urban U.
ED, demonstrating significant operational improvements even with single-digit percentage reductions in complex environments, while Blouin-Delisle et al.
achieved a 39% LOS reduction in geriatric care units, suggesting profound results in settings with intricate care pathways .
, .
Additionally.
Hussein et al.
reported an LOS decrease from 118 to 112 minutes in an Egyptian ED, and Shang et al.
noted a 4-hour median LOS reduction in a Chinese healthcare facility, affirming LSS's role in streamlining clinical workflows across various continents .
, .
This also shows that the effectiveness of LSS varied across medical specialties and geographic locations.
Table 3 provides a comprehensive overview of these LOS reductions, meticulously detailing the LSS methodology employed and the observed quantitative changes for each reviewed study.
LSS implementation significantly improved patient throughput and operational efficiency by identifying and eliminating process inefficiencies.
Table 3.
Characteristics and Outcomes of Included Studies Titled and Author A Systems Approach to Front-End Redesign With Rapid Triage Implementation .
LSS Methodology DMAIC Donabedian model An integrated approach for designing intime and economically sustainable emergency care networks: A case study in the public sector .
Applying health-six-sigma principles helps reducing the variability of length of stay in the emergency department .
LSS DES LSS Tailored H-6S Applying Lean Six Sigma methods to reduce length of stay in a hospital's emergency department .
DMAIC Sample and Instruments Pre-intervention: 57.
091 total visits.
Postintervention: 56.
561 total visits.
Emergency Severity Index (ESI) for triage, data collection through electronic health records (EHR), and performance work teams.
Data from two hospitals and eight POCs (Points of Car.
Use of input data analysis, simulation model testing, and FMEA for risk Q2 2017: 9928 patients.
Q2 2018: 9484
Q2 2019: 7647 patients.
Comparison of LOS data from different periods with varying team experience continuous data collection.
Observation of processes, data collection through patient Key outcome Discharge time Results Average discharge time reduced 1 minutes to 242.
Waiting time Average ED waiting time reduced 6 minutes to 103.
Length of Stay ED LOS decreased to 2.
3 A 1.
Length of Stay Reduce patientsAo length of stay by https://doi.
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Desember 2025 Titled and Author Exploring the Effect of At-Risk Case Management Compensation on Hospital Pay-for-Performance Outcomes: Tools for Change .
Implementing Lean Six Sigma in a Multispecialty Hospital through a Change Management Approach .
Improving Hospital Discharge Time: A Successful Implementation of Six Sigma Methodology .
LSS Methodology DMAIC Change Sample and Instruments Data collected using monthly dashboards.
Implementation of an at-risk compensation Key outcome Length of Stay Results Length of stay reduced by 4.
DMAIC ADKAR
Initial time study data for ED patients.
Use of DMAIC and ADKAR models.
Length of Stay
DMAIC
Length of Stay Improving Interprofessional Approach Using a Collaborative Lean Methodology in Two Geriatric Care Units for a Better Patient Flow .
Lean intervention improves patient discharge times, improves emergency department throughput, and reduces congestion .
Lean Six Sigma for Health Care:
Multiple Case Studies in Latin America Lean thinking by integrating with discrete event simulation and design of experiments: An emergency department expansion .
Lean-Based Approach to Improve Emergency Department Throughput .
DMAIC Collaborative Lean Hospital data analysis with 8494 pre- and 8560 post-intervention for inpatients.
pre- and 3169 post-intervention for ED Convenience sampling from medical records.
Pilot project conducted with multiple interprofessional teams.
Analysis based on admission and discharge records.
The average Length of Stay (LOS) reduced from 267 to 158 ED mean LOS significantly lower post-intervention, 6.
9 vs 5.
Pre-intervention: 1,800 discharges/month.
Post-intervention: 1,800 discharges/month.
Electronic Medical Record (EMR) data for discharge times.
Case 2 (ED): Process mapping of ED.
Selection of hospitals based on interest in Lean Six Sigma implementation.
Simulation model used for system analysis.
Use of Discrete Event Simulation (DES) and Design of Experiments (DoE) for simulation purposes.
161 patients .
re-interventio.
, 200 patients .
ost-interventio.
Implied consecutive sampling.
Data collected from a specific Egyptian ED.
simulation model based on real patient Data obtained from ED records.
ED median boarding time Mitigating Overcrowding in Emergency Departments Using Six Sigma and Simulation: A Case Study in Egypt .
LSS Focused Lean
LSS DMAIC
Lean DES DOE
LSS Fast Track
LSS DES
Length of Stay The average Length of Stay (LOS) at the Emergency room reduced from 84.
5 to 52.
Median ED boarding time decreased by 176-127 .
Waiting time 72% reduction in waiting time Length of Stay Average LOS decreased to 461.
min, around 79.
0% reduction.
Length of Stay The overall length of stay decreased by 9% Length of Stay Average LOS decreased from 118 men to 112 men https://doi.
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LSS Methodology LSS Standardized discharge processes and roles Redesigning an Inpatient Pediatric Service Using Lean to Improve Throughput Efficiency .
DMAIC
Reducing the Length of Stay for Patients Stranded in the Emergency Department .
Reduction in Average Length-of-Stay in Emergency Department of a LowIncome CountryAos Cancer Hospital .
The Use of Lean Six Sigma for Improving Availability of and Access to Emergency Department Data to Facilitate Patient Flow .
Machine Learning-Based Lean Service Quality Improvement by Reducing Waiting Time in the Healthcare Sector
DMAIC
Sample and Instruments 19 acute care units.
Daily huddles, visual management boards, and electronic medical record (EMR) data for discharge Key outcome Discharge time Results Patients were discharged 56 minutes earlier.
Discharge time Improved ED throughput and reduced boarding times, with the median patient discharge time decreased by 93 minutes Median LOS for admitted patients decreased from 19.
64 to 15.
Length of stay reduced from 166 to 142 minutes .
%) Study with concurrent controls.
1,552
Controlled observational.
DMAIC
DMAIC DMAIC
data-process DMAIC Machine Learning 10,230 patients .
re-interventio.
8,997
ost-interventio.
Implied census data for specific periods.
200 patients .
re-interventio.
ost-interventio.
Convenience sampling over specific time periods.
Focused on data availability and access.
Observational study with a multidisciplinary Length of Stay 924 patients.
Random sampling.
Machine learning algorithms .
Random Forest.
XGBoos.
and data analytics tools.
Wait time Length of Stay Length of Stay - Median LOS O 6 h despite increasing volume .
h 57 m Ie 4 h 25 .
- % LOS > 6 hours .
% Ie 13%) Support Vector Machine (SVM) achieved superior waiting time prediction accuracy with RA = 992 and Mean Absolute Error (MAE) = 1.
73 min.
DeMaio et al.
documented a 16.
7% increase in pre-11 a.
discharges, optimizing discharge processes and improving bed turnover, and Daly et al.
reported a decrease in ED data access time from 9 minutes to near-zero, enabling faster patient assessments and treatment initiation .
, .
These improvements demonstrate LSS's capacity to reduce patient time in the system and increase patient volume, enhancing healthcare facility productivity.
LSS implementation substantially reduced patient waiting times, leading to increased patient satisfaction and improved care quality.
Buestan.
Perez, and Rodryguez-Zurita .
achieved a 72% reduction in patient waiting times in a Latin American ED through comprehensive process reengineering .
Furthermore.
Ortyz-Barrios and Alfaro-Sayz .
observed a decrease in waiting times from 9.
08 to 6.
71 minutes, demonstrating LSS's ability to meticulously minimize delays .
These findings collectively emphasize that LSS interventions directly benefit patients by significantly reducing waiting times for care.
The variations in the magnitude of LOS reduction across these studies are visually represented in Figure 2.
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Shakoor et al.
Shang et al.
Beck and Gosik .
DeMaio et al.
Hussein et al.
Kenny.
Rosania and Lu .
Gabriel et al.
Buestan.
Perez and Rodryguez-Zurita,A Beck et al.
Blouin-Delisle et al.
El-Eid et al.
Samanta et al.
Granata and Hamilton .
4,7% Furterer .
Kobo-Greenhut et al.
Ortyz-Barrios and Alfaro-Sayz .
Chmielewski.
Tomkin and Edelstein .
1,3% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% LOS Discharge time Waiting Time Figure 2.
The Percentage LOS & Discharge Related Metric Reduction by Individual Study LOS Reduction Range Implementation Strategies and Outcomes DMAIC Only DMAIC Machine DMAIC Discrete Hybrid Approaches Learning (ML) Event Simulation (DES) Strategy Type Figure 3.
Comparing LOS Reduction Ranges by Implementation Strategy The efficacy of LSS is closely linked to specific implementation strategies, ranging from traditional to sophisticated hybrid approaches (Figure .
The DMAIC methodology was the most common standalone approach .
, consistently yielding substantial LOS reductions of 20% to 40%.
When combined with Machine Learning (ML), as in one study by Garedew et al.
, predictive capabilities led to LOS reductions of 25% to 35%, while two studies Hussein et al.
and Gabriel et al.
integrating DMAIC with Discrete Event Simulation (DES) achieved pronounced outcomes, with LOS reductions of 30% to 79%, by enabling virtual testing of process changes .
, .
, .
Additionally, seven studies adopted hybrid approaches, integrating LSS with change management frameworks like the ADKAR model, emphasizing cultural transformation and achieving LOS reductions of 15% to 30%.
Samanta et al.
reported a 40% ED LOS reduction using DMAIC with ADKAR .
Geographically, developed nations often leveraged advanced technologies like DES and ML, while developing nations achieved considerable gains through process https://doi.
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Implementation Challenges and Critical Success Factors Despite successes.
LSS implementation faces several challenges, including resistance to cultural change, the integration of Lean practices without disrupting existing workflows, and resource limitations, particularly in low-resource settings.
Table 4 delineates several prominent challenges encountered during LSS implementation.
Table 4.
Key Implementation Challenges and Contextual Variations in LSS Implementation Context Key Challenge Specific Manifestation / Impact General Resistance .
Cultural Difficulty in staff buy-in and adoption of new mindsets Workflow Integration .
Disruption potential Need for careful planning to avoid impacting ongoing patient care Low-resource Setting .
Resource limitation Requires process-focused solutions, less reliance on technology Urban Environment .
Variation in effect by Impact size influenced by existing infrastructure and patient volume However, critical success factors were consistently identified across studies:
leadership support, cited in all 19 reviewed studies, provides essential resources and staff training, identified in 18 studies, is vital for equipping staff with the knowledge and skills for LSS implementation.
robust data systems, noted in 15 studies, are crucial for accurate measurement and informed decision-making.
and change management frameworks, documented in 12 studies, enhance the acceptance and long-term sustainability of LSS initiatives.
Figure 4 summarizes these factors and their observed Frequency In Study Data System Change Management Leadership Support Staff Training Succes Factor Figure 4.
Frequency of Critical Success Factors This review demonstrates that Lean Six Sigma (LSS) methodologies significantly improve critical operational metrics in healthcare systems globally.
LSS interventions consistently reduced Length of Stay (LOS), enhanced patient throughput, and decreased waiting times.
The effectiveness varied based on the specific healthcare setting, intervention scope.
LSS tools used, and contextual factors.
DISCUSSION
Lean Six Sigma (LSS) methodologies are a potent and adaptable framework for driving significant operational improvements and enhancing patient and staff outcomes in diverse healthcare settings.
Its effectiveness is significantly modulated by contextual factors, specific implementation strategies, and the crucial integration of robust change management and leadership support, underscoring its capacity for universally streamlined clinical workflows.
LSS consistently achieves substantial reductions in Length of Stay (LOS) .
, 939%) and patient waiting times .
, up to 95%), alongside significant increases in patient throughput .
p to sixfol.
across various healthcare specialties and geographies .
, .
, .
LSS improves efficiency by systematically eliminating waste and reducing https://doi.
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Desember 2025 e-ISSN: 2338-3445 p-ISSN: 0853-9987 process variation, utilizing principles like value stream mapping and the DMAIC cycle.
From a systems perspective, it optimizes interconnected processes, while efficiency models explain how reducing bottlenecks enhances flow and reduces waits .
The extent of improvement varies, influenced by specific LSS tools and departmental Emergency departments, with standardized workflows, show more dramatic gains than complex areas like oncology.
Hybrid approaches combining DMAIC with tools like Discrete Event Simulation often yield superior results.
Diverse study designs and metrics complicate direct comparisons, limiting generalizability.
However, consistent improvements across varied geographical and economic contexts affirm LSS's fundamental robustness.
LSS is a powerful tool, but successful implementation requires a nuanced approach, tailoring tools to specific needs and considering process maturity.
Strategic investment in process mapping and waste identification in high-volume, standardized areas is crucial .
These findings empirically support efficiency theories, demonstrating how LSS systematically reduces waste and variation, while also highlighting the mediating role of contextual factors.
Robust leadership support .
ited as critical in all 19 studie.
, comprehensive staff training .
/19 studie.
, and formal change management frameworks .
studies, e.
ADKAR with DMAIC) are crucial for LSS success.
Cultural resistance is a common The necessity stems from change theories (Lewin's "unfreeze, change, refreeze," Kotter's 8-Step Proces.
and leadership theories .
ransformational leadershi.
, which explain how to navigate organizational inertia and foster buy-in for new practices .
Change management approaches vary, and their effectiveness is influenced by organizational culture.
Cultural resistance, particularly among healthcare professionals perceiving standardization as a threat to autonomy, necessitates context-specific strategies addressing psychological aspects of change.
Proactive leadership, fostering psychological safety, and tailored staff training are vital.
Implementing LSS without robust change management or visible leadership endorsement is likely to encounter significant LSS can lead to sustained improvements, with the "Control" phase of DMAIC and change management frameworks supporting long-term benefits.
However, extensive long-term follow-up data across current research remains a significant limitation.
LSS
fosters organizational learning and continuous improvement by embedding a problemsolving, data-driven mindset, allowing organizations to adapt and sustain improvements beyond initial interventions .
Future research must prioritize longitudinal studies to definitively assess the durability of LSS benefits and cultural transformation.
Standardizing reporting metrics is crucial for robust meta-analyses.
Exploring LSS integration with emerging technologies like AI for predictive analytics and expanding research into diverse contexts .
, rural, underresourced setting.
will be critical.
More robust economic evaluations and deeper qualitative research into patient/staff experience are also needed.
Sustained LSS initiatives contribute to theories of organizational learning, demonstrating how structured process improvement fosters continuous adaptation.
This reinforces continuous improvement theories by highlighting the impact of sustained effort and effective CONCLUSION LSS significantly enhances operational efficiency .
LOS and waiting time reduction.
across diverse healthcare settings.
Its success is critically dependent on robust change management and visible leadership, though comprehensive financial analyses and long-term follow-up data remain limited in current literature.
For hospital management.
LSS is a powerful strategic tool requiring context-specific implementation, proactive leadership, and comprehensive staff training.
A holistic approach that https://doi.
org/10.
34011/jmp2k.
MEDIA PENELITIAN DAN
PENGEMBANGAN KESEHATAN
Vol 35 No 4.
Desember 2025 e-ISSN: 2338-3445 p-ISSN: 0853-9987 integrates all components of the "quadruple aim" into data collection and evaluation is essential for sustained success and economic justification.
This review empirically validates the applicability of systems theory and efficiency models in healthcare, reinforcing the crucial roles of change management and transformational leadership.
It also contributes to organizational learning theories by demonstrating LSS's capacity to foster continuous adaptation and knowledge creation.
Limitations include the heterogeneity of primary studies, which complicated direct comparisons, and a scope limited by available published literature.
The scarcity of comprehensive financial analyses and extensive long-term follow-up data in the source material also posed constraints.
Future research should prioritize .
Longitudinal Studies, .
Standardized Reporting, .
Comprehensive Economic Evaluations, .
Technology Integration, and .
Contextual Adaptations.
REFERENCES