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Background and purpose: Radiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity. Materials and methods: Lean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013-2021). Outlier complexities were defined as >95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR <90 %, were identified. The tool's impact was assessed after nine months of clinical use. Results: Among 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p < 0.05) due to plan re-optimization. Conclusions: ML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.

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The efficacious treatment of muscle and joint pain relies heavily on etofenamate (ETO) and benzyl nicotinate (BN), which possess robust anti-inflammatory and pain-relieving properties when paired with methylparaben (MP) or benzyl alcohol (BA). In this study, we have established and validated innovative RP-UPLC methods for assessing ETO and BN in the presence of MP or BA in their dosage forms, employing eight green tools to evaluate their eco-friendliness and effectiveness. Reversed phase-ultra-performance liquid chromatography (RP-UPLC) technique employs a flow rate of 0.3 mL/min on Waters Acquity UPLC BEH Column (C18, 1.7 µm, 100 mm × 2.1 mm), detection at 254 nm using a photo diode array (PDA) detector and mobile phase of 0.05 M KH2PO4 buffer, acetonitrile, and methanol (50:15:35, v/v/v) adjusted pH 6.0 with 0.2% triethylamine. For ETO, BN, MP, and BA, the calibration curves were linear and ranged from 0.005 to 1.0, from 0.001 to 0.2, from 0.002 to 0.08, and from 0.0001 to 0.1 mg/mL, respectively. The correlation value was 0.9999, and the accuracy findings ranged from 98.81% to 100.56%. Consequently, the methodology has been successfully implemented in assay testing for the pharmaceuticals in the presence of the MP or BA, demonstrating the high selectivity of these approaches. The present study presents the Blue Applicability Grade Index (BAGI), an innovative approach that complements green metrics in practical white analytical chemistry. According to the International Council for Harmonisation (ICH) criteria, the procedures were effectively validated.

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Background and aim: This study applied Six Sigma metrics to facilitate the quality control (QC) review for hospital glucose meters. Materials and methods: QC data from a period of six months on all hospital glucose meters were extracted from the data management system. Sigma values for each meter at two QC levels were calculated and evaluated each month by combining the imprecision, the absolute bias between the meter mean and all-meter mean, and the standards from ISO 15179:2013. The effectiveness of using Sigma values in identifying meters with possible quality problems for further Levey-Jennings QC chart review was assessed. Results: More than 80 % of the meter's Sigma values within the six months were greater than 4 at either QC level. At the high QC level, twice as many Sigma values were below 4 than the low QC level. Including Sigma values 4, 3.5 or 3 in the criteria for the QC review reduced the number of chart review to 32.8 %, 11.2 % or 3.5 %, respectively. Conclusions: The majority of the glucose meters examined in this study demonstrated optimal Sigma values. The Sigma metrics-based approach could be a valuable tool to guide an effective QC review of glucose meters for quality improvement.

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Sigma metric analysis was conducted across two New Zealand Blood Services (NZBS) laboratories (Auckland and Christchurch) to optimize quality control (QC) procedures. We evaluated five assays (anti-HCV, HIV Ag/Ab combo, HTLV-I/II, HBsAg, and Syphilis) using internal quality control (IQC) and third-party daily QC data extracted from four Architect i2000SR instruments during Jan 2 -31st, 2023. Mean, standard deviation (SD), and coefficient of variation (CV%) were calculated, assuming zero bias. Sigma metrics were determined using the Total Allowable Error (TEa %) based on difference between positive control mean and signal-to-cutoff (s/co) cut-off. Most assays exhibited CV% values ≤10 % except for HBsAg IQC (18.5 %) and anti-HCV third-party QC (13.4 %) at Christchurch. TEa % ranged from 38 % to 90 %. Overall, the assays demonstrated Six Sigma performance (σ > 6), except for HBsAg IQC (3.97) and anti-HCV third-party QC (5.46) at Christchurch. These high-quality serology assays can benefit from simplified QC design without compromising blood safety.

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The construction industry consumes significant resources, emits considerable pollutants, and generates substantial waste. Green, Lean, Six Sigma (GLS) is an emerging paradigm to control waste, carbon footprint, resource conservation, non-value-added activities, and cost. However, limited focus has been given to the risks involved in GLS construction projects (GLSCPs). This research explored risk factors (RFs) to GLSCPs based on literature review and expert judgments. Brainstorming sessions were conducted to validate the RFs and establish mutual interactions among them through experts' opinions. A 4-level structural model was extracted through Interpretive structural modeling (ISM). The Matriced Impacts Croise's Multiplication Appliqée a UN Classement (MICMAC) was integrated to assess the 'driving' and 'dependence' power of the RFs. The results show that all RFs are crucial and impact GLSCPs, but the most critical are 'unstable inflation,' 'fluctuations in interest rate,' and 'fluctuations in exchange rate.' This study enhances managers' and policymakers' understanding of RFs associated with GLSCPs and supports effective risk management for successful GLS implementation in construction projects.

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Beta-lactam antibiotics are widely used in the intensive care unit due to their favorable effectiveness and safety profiles. Beta-lactams given to patients with sepsis must be delivered as soon as possible after infection recognition (early), treat the suspected organism (appropriate), and be administered at a dose that eradicates the infection (adequate). Early and appropriate antibiotic delivery occurs in >90% of patients, but less than half of patients with sepsis achieve adequate antibiotic exposure. This project aimed to address this quality gap and improve beta-lactam adequacy using the Define, Measure, Analyze, Improve, and Control Lean Six Sigma quality improvement framework. A multidisciplinary steering committee was formed, which completed a stakeholder analysis to define the gap in practice. An Ishikawa cause and effect (Fishbone) diagram was used to identify the root causes and an impact/effort grid facilitated prioritization of interventions. An intervention that included bundled education with the use of therapeutic drug monitoring (TDM; i.e. drug-level testing) was projected to have the highest impact relative to the amount of effort and selected to address beta-lactam inadequacy in the critically ill. The education and TDM intervention were deployed through a Plan, Do, Study, Act cycle. In the 3 months after "go-live," 54 episodes of beta-lactam TDM occurred in 41 unique intensive care unit patients. The primary quality metric of beta-lactam adequacy was achieved in 94% of individuals after the intervention. Ninety-four percent of clinicians gauged the education provided as sufficient. The primary counterbalance of antimicrobial days of therapy, a core antimicrobial stewardship metric, was unchanged over time (favorable result; P = .73). Application of the Define, Measure, Analyze, Improve, and Control Lean Six Sigma quality improvement framework effectively improved beta-lactam adequacy in critically ill patients. The approach taken in this quality improvement project is widely generalizable to other drugs, drug classes, or settings to increase the adequacy of drug exposure.

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PURPOSE: We aimed to show the framework of the six-sigma methodology (SSM) that can be used to determine the limits of QC tests for the linear accelerator (Linac). Limits for QC tests are individually determined using the SSM. METHODS AND MATERIALS: The SSM is based on the define-measure-analyze-improve-control (DMAIC) stages to improve the process. In the "define" stage, the limits of QC tests were determined. In the "measure" stage, a retrospective collection of daily QC data using a Machine Performance Check platform was performed from January 2020 to December 2022. In the "analyze" stage, the process of determining the limits was proposed using statistical analyses and process capability indices. In the "improve" stage, the capability index was used to calculate the action limits. The tolerance limit was established using the larger one of the control limits in the individual control chart (I-chart). In the "control" stage, daily QC data were collected prospectively from January 2023 to May 2023 to monitor the effect of action limits and tolerance limits. RESULTS: A total of 798 sets of QC data including beam, isocenter, collimation, couch, and gantry tests were collected and analyzed. The Collimation Rotation offset test had the min-Cp, min-Cpk, min-Pp, and min-Ppk at 2.53, 1.99, 1.59, and 1.25, respectively. The Couch Rtn test had the max-Cp, max-Cpk, max-Pp, and max-Ppk at 31.5, 29.9, 23.4, and 22.2, respectively. There are three QC tests with higher action limits than the original tolerance. Some data on the I-chart of the beam output change, isocenter KV offset, and jaw X1 exceeded the lower tolerance and action limit, which indicated that a system deviation occurred and reminded the physicist to take action to improve the process. CONCLUSIONS: The SSM is an excellent framework to use in determining the limits of QC tests. The process capability index is an important parameter that provides quantitative information on determining the limits of QC tests.

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BACKGROUND: Routine clinical biochemistry tests are crucial for clinical diagnostics and play a key role in enhancing outpatient turnover efficiency and patient satisfaction. This study aimed to implement Lean Six Sigma in the biochemistry laboratory of a hospital in China to improve efficiency and quality by reducing turnaround time. METHODS: The study was conducted from January to December 2023, using the DMAIC (Define, Measure, Analyze, Improve, Control) framework, and employed tools such as the voice of the customer, Value Stream Mapping, '5 whys' technique, Nominal Group Technique, and Pareto chart. RESULTS: The turnaround time for outpatient routine clinical biochemistry tests was reduced from 139 min to 58 min (p < 0.05), effectively increasing both patient and physician satisfaction. CONCLUSIONS: Lean Six Sigma aimed to reduce the turnaround time for biochemical tests have significant advantages. This study confirms the effectiveness of Lean Six Sigma in a Chinese clinical laboratory setting and provides guidance for optimizing efficiency in global clinical laboratories with limited implementation experience, constrained technical and equipment resources, and high demand for medical diagnostics.

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Under Taiwan's National Health Insurance (NHI) system, it's crucial for all healthcare providers to accurately submit medical expense claims to the National Health Insurance Administration (NHIA) to avoid incorrect deductions. With changes in healthcare policies and adjustments in hospital management strategies, the complexity of claiming rules has resulted in hospitals expending significant manpower and time on the medical expense claims process. Therefore, this study utilizes the Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) management approach to identify wasteful and non-value-added steps in the process. Simultaneously, it introduces Robotic Process Automation (RPA) tools to replace manual operations. After implementation, the study effectively reduces the process time by 380 min and enhances Process Cycle Efficiency (PCE) from 69.07 to 95.54%. This research validates a real-world case of Lean digital transformation in healthcare institutions. It enables human resources to be allocated to more valuable and creative tasks while assisting hospitals in providing more comprehensive and patient-centric services.

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While Six Sigma is used in different disciplines to improve quality, Tony Badric and Elvar Theodorsson in a recent paper in CCLM have questioned Six Sigma application in medical laboratory concluding Six Sigma has provided no value to medical laboratory. In addition, the authors have expanded their criticism to Total Analytical Error (TAE) model and statistical quality control. To address their arguments, we have explained the basics of TAE model and Six Sigma and have shown the value of Six Sigma to medical laboratory.

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The Westgard quality control (QC) rules are often applied in infectious diseases serology to validate the quality of results, but this requires a reasonable tradeoff between maximum sensitivity to errors and minimum false rejections. This article, in addition to illustrate the six sigma methodology in the QC management of the (anti-HCV Architect®) test, it discusses the main influencing factors on sigma value. Data from low positive and in-kit control materials spreading over 6 months and using four reagent kits, were used to calculate the precision of the test. The difference between the control material reactivity and the cut-off defined the error budget. Sigma values were > 6, which indicates that the method produces four erroneous results per million tests. The application of the six sigma concept made it possible to argue the choice of the new QC strategy (use of 13S rule with one positive control) and to relax the existing QC rules. This work provides a framework for infectious diseases serology laboratories to evaluate tests performances against a quality requirement and design an optimal QC strategy.

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Hospital pharmacies are integral to the healthcare system, and evaluating the factors influencing their efficiency and service standards is imperative. This analysis offers global insights to assist in developing strategies for future enhancements. The objective is to identify the optimal Lean Six Sigma methodologies to improve workflow and quality of hospital pharmacy services. A strategic search, aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassed an extensive range of academic databases, including Scopus, PubMed/Medline, Web of Science, and other sources for relevant studies published from 2009 to 2023. The focus was on management tactics and those examining outcomes, prioritizing publications reflecting pharmacy operations management's state. The quality of the selected articles was assessed, and the results were combined and analyzed. The search yielded 1,447 studies, of which 73 met the inclusion criteria. The systematic review found a low to moderate overall risk of bias. The number of publications rose during the coronavirus disease (COVID-19) outbreak. Among studies, research output in the United States of America represented 26% of the total. Other countries such as Indonesia, Spain, Canada, China, Saudi Arabia, the United Arab Emirates, and the United Kingdom also made significant contributions. Each country accounted for 12%, 8%, 7%, 5%, 5%, 5%, and 5%, respectively. The pharmacy journals led with 26 publications, and healthcare/medical with 14. The quality category came next with 12 articles, while seven journals represented engineering. Studies used empirical and observational methods, focusing on practice quality enhancement. The process control plan had 26 instances, and the define, measure, analyze, improve, and control (DMAIC) was identified 13 times. The sort, set in order, shine, standardize, and sustain (5S) ranked third, totaling seven occurrences. Failure mode and effects analysis (FMEA) and root cause analysis were moderately utilized, with six and four instances, respectively. Poka-Yoke (mistake-proofing measures) and value stream mapping were each counted three times. Quality improvement and workflow optimization dominated managerial strategies in 22 (30.14%) studies each, followed by technology integration in 15 (20.55%). Cost, patient care, and staffing each featured in three (4.11%) studies, while two (2.74%) focused on inventory management. One (1.37%) study each highlighted continuing education, collaboration, and policy changes. Analysis of the 73 studies on Lean and Six Sigma in hospital pharmacy operations showed significant impacts, with 26% of studies reporting decreased medication turnaround time, 15% showing process efficiency improvements, and 11% each for enhanced inventory management and bottleneck/failure mode reduction. Additionally, 9% of studies observed decreased medication errors, 8% noted increased satisfaction and cost savings, 6% identified enhancements in clinical activities, 3% improved prescription accuracy, 2% reduced workflow interruptions, and 1% reported increased knowledge. Also, this study has identified key strategies for service delivery improvement and the importance of quality practices and lean leadership. To the best of the author's knowledge, this research is believed to be the first in-depth analysis of Lean and Six Sigma in the hospital pharmacy domain, spanning 15 years from 2009 to 2023.

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INTRODUCTION: Lean, especially Value Stream Mapping is increasingly used in hospitals to optimize processes. This method, which originated in the automotive industry, enables all staff involved in the process to make it more customer-friendly. Despite the widely reported success of Lean projects, they have failed in some cases. This study investigated the contextual factors and mechanisms that contribute to a successful implementation of Value Stream Mapping. METHODS: Value Stream Mapping was applied to the discharge process in four breast cancer centers. A mixed-method approach was used in two steps. First, to verify the successful implementation, defined as time optimization, time measurement was conducted at three points in time and analyzed using an ANOVA. Second, an analysis of contextual factors was combined with a qualitative content analysis of mechanisms based on normalization process theory, using routine data, meeting protocols, field notes, and interview transcripts as data source. RESULTS: At one of the four breast cancer centers, lead- and waiting time were significantly reduced; at the others, these reductions did not occur. Failure/success cannot be explained by the size of the hospital, the number of cases or staffing levels. The variable project team composition is evident, especially leadership involvement. DISCUSSION: A comparative analysis was conducted to identify the factors that led to success. These factors were: participation of all leaders relevant to the process, in the case of the discharge process including medical and nursing leaders; dissemination of the changes from the project team to colleagues including its sense and possibility to discuss it; joint reflection of the implementation process in regular work team meetings. CONCLUSIONS: These results confirm the important role of leadership in implementation projects. Leadership support enabled the mechanisms found. The used combination of theoretical approaches from management research and implementation science determined the interpretation and should be applied more often in implementation science.

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Lack of alignment of care protocols among providers in health care is a driver of increased costs and suboptimal patient outcomes. Perioperative anticoagulation management is a good example of a complex area where protocol creation is a clinical challenge that demands input from multiple experts. Questions regarding the need for anticoagulation interruptions are frequent. Yet, due to layers of complexity involving analysis of anticoagulation indication, surgical risk, and anesthesia-associated bleeding risk as well as institutional practices, there is heterogeneity in how these interruptions are approached. The recent perioperative anticoagulation guidelines from the American College of Chest Physicians summarize extensive evidence for the management of anticoagulant and antiplatelet medications in patients who undergo elective interventions. However, implementation of these guidelines by individual clinicians is highly varied and often does not follow the best available clinical evidence. Against this background, anticoagulation stewardship units, which exist to improve safety and quality monitoring for the anticoagulated patient, are of growing interest. These units provide a bridge for the implementation of value-based, high-quality guidelines for patients who need perioperative anticoagulation interruption. We use a case to pragmatically illustrate the problem and tactics for change management and implementation science that may facilitate the adoption of perioperative anticoagulation guidelines.

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Background: Six Sigma is a popular quality management system that enables continuous monitoring and improvement of analytical performance in the clinical laboratory. We aimed to calculate sigma metrics and quality goal index (QGI) for 17 biochemical analytes and compare the use of bias from internal quality control (IQC) and external quality assurance (EQA) data in the calculation of sigma metrics. Methods: This retrospective study was conducted in Marmara University Pendik E&R Hospital Biochemistry Laboratory. Sigma metrics calculation was performed as (TEa-bias)/CV). CV was calculated from IQC data from June 2018 - February 2019. EQA bias was calculated as the mean of % deviation from the peer group means in the last seven surveys, and IQC bias was calculated as (laboratory control result mean-manufacturer control mean)/ manufacturer control mean) x100. In parameters where sigma metrics were <5; QGI=bias/1.5 CV) score of <0.8 indicated imprecision, >1.2 pointed inaccuracy, and 0.8-1.2 showed both imprecision and inaccuracy. Results: Creatine kinase (both levels), iron and magnesium (pathologic levels) showed an ideal performance with ≥6 sigma level for both bias determinations. Eight of the 17 parameters had different sigma levels when we compared sigma values calculated from EQA and IQC derived bias% while the rest were grouped at the same levels. Conclusions: Sigma metrics is a good quality tool to assess a laboratory's analytical performance and facilitate the comparison of the assay performances in the same manner across multiple systems. However, we might need to design a tight internal quality control protocol for analytes showing poor assay performance.

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Six Sigma Foundations is a statistical standard that indicates an exceptionally high level of quality, along with a customer satisfaction management approach that intends to lower error rates and boost process efficiency. The Define, Measure, Analyse, Improve, and Control (DMAIC) approach is a fundamental component of Six Sigma and provides an organised framework for process improvement. In contrast to conventional techniques that are more manual-based, Six Sigma emphasises and focuses on making decisions based on facts and evidence. The key to the success of Six Sigma is its reliance on statistical methods. Advanced tools like Pareto charts, histograms, regression analysis, and fishbone diagrams are used ardently for the benefit of customers and to reduce the overall error rate. To support clinical decision-making, a clinical laboratory's primary responsibility is to generate test results that are accurate, repeatable, fast, and appropriately interpreted. Ensuring desired clinical outcomes must be the ultimate objective. To accomplish this goal, laboratories must prioritise cost-effectiveness while establishing and maintaining quality in all laboratory procedures. The concept of the Lean Six Sigma (LSS) methodology, which mainly centres on efficiency by discerning and eradicating actions or operations that do not provide any benefit to the organisation, is combined with the proposition of Six Sigma, which emphasises data-driven analyses and optimization. The integration of these powerful concepts aids in the overall improvement of the organisations adopting these techniques. This review provides a brief overview of the benefits of the LSS methodology and its implementation in the oral pathology laboratory.

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INTRODUCTION: Urgent endovascular intervention is currently accepted as the primary and critical therapeutic approach to patients whose acute ischemic stroke results from a large arterial occlusion (LAO). In this context, one of the quality metrics most widely applied to the assessment of emergency systems performance is the "door-to-puncture" (D-P) time. We undertook a project to identify the subinterval of the D-P metric causing the most impact on workflow delays and created a narrowly focused project on improving such subinterval. METHODS: Using the DMAIC (i.e., define, measure, analyze, improve and control) approach, we retrospectively reviewed our quality stroke data for calendar year (CY) 2021 (i.e., baseline population), completed a statistical process control assessment, defined the various subintervals of the D-P interval, and completed a Pareto analysis of their duration and their proportional contribution to the D-P interval. We retooled our workflow based on these analyses and analyzed the data resulting from its implementation between May and December 2022 (i.e., outcome population). RESULTS: The baseline population included 87 patients (44 men; mean age = 67.2 years). Their D-P process was uncontrolled, and times varied between 35-235 minutes (Mean = 97; SD = 38.40). Their door to angiography arrival (D-AA) subinterval was significantly slower than their arrival to puncture (AA-P) (73.4 v. 23.5 minutes; p < 0.01), accounted for 73% of the average length of the D-P interval. The group page activation to angiography arrival (GP-AA) subinterval accounted for 41.5% of the entire D-AA duration, making it the target of our project. The outcome population originally consisted of 38 patients (15 men; mean age = 70.3 years). Their D-P process was controlled, its times varying between 43-177 minutes (Mean = 85.8; SD = 34.46), but not significantly difference than the baseline population (p = 0.127). Their target subinterval GP-AA varied between 0-37 minutes and was significantly improved from the baseline population (Mean = 13.21 v. 29.68; p < 0.001). CONCLUSIONS: It seems feasible and reasonable to analyze the subinterval components of complex quality metrics such as the D-P time and carry out more focused quality improvement projects. Care must be exercised when interpreting the impact on overall system performance, due to unexpected variations within interdependent subprocesses. The application of a robust and comprehensive LSS continuous quality improvement process in any CSC will have to include individualized focused projects that simultaneously control the different components of overall system performance.

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Introduction: Prolonged length of stay (LOS) in emergency departments (ED) is a widespread problem in every hospital around the globe. Multiple factors cause it and can have a negative impact on the quality of care provided to the patients and the patient satisfaction rates. This project aimed to ensure that the average LOS of patients in a tertiary care cancer hospital stays below 3 hours. Materials and Methods: The Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) approach was followed. Results: The average LOS was 166 minutes before implementing interventions. The two primary reasons for the increased length of stay were delays secondary to physician assessment and diagnostic lab reports. Strategies were defined to control these factors, which helped reduce the average length of stay to 142 minutes, a 30% reduction. Conclusion: A process improvement model similar to this project is recommended to enhance the quality of hospital services. It will provide valuable insights into the process flow and assist in gathering precise data on the various steps involved. The data collected can then be analyzed to identify potential causes and make informed decisions that can significantly improve hospital processes.

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OBJECTIVES: This work aims to improve the quality of care provided to patients by equipping caregivers with comprehensive set of problem-solving tools and competencies. This is achieved through the development of a customized health design process that incorporates both human-centric and data-centric tools. BACKGROUND: To meet the growing complexity of today's clinical practice, caregivers need to be empowered with the tools and competencies necessary to address the multifaceted challenges they encounter. This has emphasized the need to broaden the traditional role of caregivers as evidence-based practitioners to include being healthcare problem-solvers and innovators who utilize their creative and critical thinking skills. METHOD: While design thinking (DT) is a popular methodology that fosters caregivers' empathy and creativity, it does not provide tools for evaluating the quality of obtained solutions. To address this gap, a problem-solving process that combines DT and data-centric tools of the Lean Six Sigma method was developed in this work. RESULTS: The evaluation of this customized design process was based on targeted competencies derived from the six aims of healthcare. The potential benefits are then highlighted through mapping the possible outputs of every phase with the targeted set of caregivers' skills. Additionally, an implementation plan was outlined for a local hospital, showcasing the potential impact this process can have in empowering caregivers with the necessary competencies to create effective and innovative solutions for care delivery. CONCLUSION: Overall, This unique approach has the potential to contribute to the ongoing effort to transform healthcare into an efficient system that meets the needs of both patients and caregivers.