Assessment of risk factors to Green, Lean, Six Sigma adoption in construction sector: Integrated ISM-MICMAC approach.

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.

Use of the DMAIC Lean Six Sigma quality improvement framework to improve beta-lactam antibiotic adequacy in the critically ill.

BACKGROUND: 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 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 DMAIC Lean Six Sigma quality improvement framework. METHODS: A multidisciplinary steering committee was formed and 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 which 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. RESULTS: The education and TDM intervention were deployed through a Plan, Do, Study, Act (PDSA) cycle. In the three months after 'go-live,' 54 episodes of beta-lactam TDM occurred in 41 unique ICU patients. The primary quality metric of beta-lactam adequacy was achieved in 94% of individuals after the intervention. 94% 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=0.73). CONCLUSIONS: Application of the DMAIC 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.

The value of Sigma-metrics in laboratory medicine.

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.

A case study of lean digital transformation through robotic process automation in healthcare.

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.

[Six Sigma driven QC management in hepatitis C serology]. / La méthodologie six sigma dans la gestion des contrôles de qualité en sérologie de l'hépatite C.

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.

Enhancing Hospital Pharmacy Operations Through Lean and Six Sigma Strategies: A Systematic Review.

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.

Contextual factors and mechanisms in the implementation of Value Stream Mapping in breast cancer centres - A multiple case study.

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.

Anticoagulation Stewardship to Bridge the Implementation Gap in Perioperative Anticoagulation Management.

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.

Six SIGMA evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data.

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.

Revolutionising Quality Management in the Oral Pathology Laboratory: A Deep Dive Into the Six Sigma Methodology.

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.

Optimizing workflow of urgent stroke endovascular intervention: A focused lean six sigma project.

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.

Reduction in Average Length-of-Stay in Emergency Department of a Low-Income Country's Cancer Hospital.

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.

Development of a Data-Centric Design Thinking Process for Innovative Care Delivery.

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.

Quality improvement initiative to improve communication domains of patient satisfaction in a regional community hospital with Six Sigma methodology.

BACKGROUND: Communication gaps, whether incomplete or fragmented communication, have been the cause of many disasters in human civilisation. Coordination of healthcare is directly related to proper communication and handoffs among multidisciplinary teams throughout multiple shifts during a patient's hospitalisation. LOCAL PROBLEM: Patient surveys and direct patient feedback at Mayo Clinic Health System in Mankato, Minnesota, indicated that patient communication with physicians and nurses had declined in 2017 and 2018. Viewing this as an opportunity for improvement, our leadership initiated several changes to increase physician and nurse communication with patients, which resulted in no notable improvements. METHODS: A systematic quality improvement approach was implemented by using Six Sigma methodology. Stakeholders from multidisciplinary teams were assembled as the project team. The five steps of Six Sigma methodology (Define, Measure, Analyse, Improve and Control) were followed to create a quality improvement intervention. INTERVENTION: We developed a standardised and easy-to-use bedside team rounding tool to improve patient communication with physicians and nurses. RESULTS: Postintervention patient satisfaction top-box scores exceeded target improvements for both physician (from 78.5% to 82.0%, p<0.01) and nurse (from 80.5% to 83.1%, p=0.04) communication domains. Physicians had a 33-point increase in percentile rank (from 41st to 74th percentile rank), and nurses had a 25-point increase in percentile rank (from 59th to 84th percentile rank). This increase in communication ranked our institution at the top of national benchmark organisations. CONCLUSIONS: Overwhelmingly positive patient feedback was achieved, and postintervention employee satisfaction was primarily positive when compared with preintervention satisfaction.

A total productive maintenance & reliability framework for an active pharmaceutical ingredient plant utilising design for Lean Six Sigma.

This research investigates the requirement for and relationship and implementation of a total productive maintenance (TPM) and Reliability Centred Maintenance (RCM) strategy within an Active Pharmaceutical Ingredient (API) Plant. This research aimed to study the tools and techniques of TPM and Reliability Engineering and then deploy a designed model to an API plant. A case study involving Design for Lean Six Sigma phases of Define, Measure, Analyse, Design, and Verify was utilised to build an API site TPM pilot program. Data was collected using interviews across Company 'X's local and Global Engineering teams. Process runtime, downtime and plant availability metrics were compiled and a new design for Total Productive Maintenance and Reliability was proposed and verified. A maintenance framework was designed to optimally incorporate Total Productive Maintenance, Reliability and Operational Excellence with an emphasis on Overall Equipment Efficiency (OEE) realizing a 33 % reduction in planned maintenance activities, a 70 % reduction in Corrective Maintenance, Cleaning for Maintenance was reduced by 50 %, the pilot maintenance area of the centrifuge has its OEE increased by 20 % and plant availability increased by two hundred and 6 h. This research highlights the importance of Total Productive Maintenance as a key component of an effective maintenance strategy and its potential to transform maintenance practices. Based on this research and results, TPM is recommended to be applied to any API manufacturing organization. A limitation of the study is that it is a single-site case study. The novelty of this research is based upon the emphasis on Reliability Engineering to remove non-value add Maintenance time from the manufacturing schedule. The Total Productive Maintenance & Reliability model designed and implemented in this research is unique in the literature and can be leveraged by engineering professionals and academics to understand the benefits of TPM.

Investigating the quality of hemovigilance process using the first two steps of Six Sigma model: a cross-sectional study.

BACKGROUND AND PURPOSE: Hemovigilance is a set of monitoring methods that covers the blood transfusion chain, from collecting blood and blood products to monitoring the blood recipients. To this end, any error in this process can have serious and irreparable consequences for patients. The present study aimed to investigate the quality of hemovigilance process in Iran, using the first two steps of Six Sigma model. METHODS: This was a quantitative cross-sectional study that was conducted over 6 months (from August 20, 2021, to February 20, 2022) at Afzalipour Hospital in Iran, using the first two steps of Six Sigma model. The study population comprised of all inpatients who needed blood or blood product transfusion in various departments of Afzalipour Hospital, among whom 477 patients were selected via stratified sampling in three shifts (morning, evening, and night). The datasheet was used to record errors in the three shifts. This research was conducted, using the DMAIC cycle's "define" and "measure" steps. RESULTS: In the define step, the hemovigilance process at Afzalipour Hospital was divided into two categories of normal process and emergency process. Each of these processes consists of several sub-processes, including "phlebotomy," "requesting blood and blood products from the department," "preparation of application by the blood bank," " sending a request from the blood bank to the blood transfusion center," "transfusing blood and blood products," and "returning the blood and blood products to the blood bank and waste disposal." In the measure step, the quality of hemovigilance process was evaluated based on sub-processes and labels at morning, evening and night shifts. The sub-process of sending a request from the blood bank to the blood transfusion center had the highest error rate with a sigma level of 1.5. Also, the evening and night shifts had a sigma level of 1.875, and the clinical and registration labels had a sigma level of 1.875. The overall sigma level of hemovigilance process was calculated to be 2. CONCLUSION: The results of this study showed that the quality of hemovigilance process at Afzalipour Hospital was poor. By employing the first two steps of Six Sigma method, we identified the existing errors in the hemovigilance process of Afzalipour hospital in order to assist hospital managers to take the necessary measures to improve this process.

Using Lean Six Sigma in a Private Hospital Setting to Reduce Trauma Orthopedic Patient Waiting Times and Associated Administrative and Consultant Caseload.

In Ireland, the extent of outpatient orthopedic waiting lists results in long waiting times for patients, delays in processing referrals, and variation in the consultant caseload. At the study site, the Define, Measure, Analyze, Improve, and Control (DMAIC) Lean Six Sigma framework was applied to evaluate sources of Non-Value-Added (NVA) activity in the process of registering and triaging patients referred to the trauma orthopedic service from the Emergency Department. A pre- (October-December 2021)/post- (April-August 2022) intervention design was employed, utilizing Gemba, Process Mapping, and the TIMWOODS tool. Embracing a person-centered approach, stakeholder Voice of Customer feedback was sought at each stage of the improvement process. Following data collection and analysis, a co-designed pilot intervention (March 2022) was implemented, consisting of a new triage template, dedicated trauma clinic slots, a consultant triage roster, and a new option to refer directly to physiotherapy services. This resulted in the total wait time of patients for review being reduced by 34%, a 51% reduction in the process steps required for registering, and an increase in orthopedic consultant clinic capacity of 22%. The reduction in NVA activities in the process and the increase in management options for triaging consultants have delivered a more efficient trauma and orthopedic pathway.

Facile synthesis and eco-friendly analytical methods for concurrent estimation of selected pharmaceutical drugs in their solutions: application to quality by design, lean six sigma, and stability studies.

Economical, highly robust, selective, precise, and eco-friendly RP-UPLC and spectrophotometric methods were developed and validated for the concurrent estimation of selected pharmaceutical drugs represented in ceftazidime (CFZ) and pyridine (PYD) in their solutions using Agilent Zorbax SB-C18 RRHD (50 × 2.1 mm, 1.8 µm) column at flow rate 0.3 mL/min with wavelength 254 nm. Box-Behnken design (BBD) established Response surface methodology (RSM) to achieve the optimum chromatographic condition with minimal trials conducted. Three independent variables specifically acetonitrile ratio 60-70%, pH 3-7, and temperature 25-35 °C were implemented to evaluate the influences of these variables on the responses as resolution and retention time. Desirability and overlay plots were carried out to adjust the optimal condition that achieved the shortest retention time of less than 2 min and desired resolution of more than 1.5 using a mobile phase consisting of acetonitrile: purified water (70:30, v/v) at pH 5.0 adjusted by 0.1% orthophosphoric acid with the column oven temperature 30 °C and column void volume 0.46 mL. Mean centering of ratio spectra (MCR) and ratio subtraction (RS) methods were effectively applied to resolve drugs' spectral superposition at 220 nm, 255.4 nm, 260.3 nm, and 254.6 nm for CFZ and PYD, respectively. Linearity range was accomplished for UPLC, MCR, and RS methods over the concentration range of 2-100, 1-50,3-30 and 5-30 µg/mL for CFZ and PYD, respectively with correlation coefficient > 0.999 and good recovery results within 98-102%. Six Sigma methodology was achieved using the process capability index (Cpk) to compare the suggested and USP methods showing that both are highly capable with Cpk > 1.33. The proposed method was successfully validated depending on ICH guidelines and ANOVA results and applied for the accelerated stability study.

A comparative analysis of Sigma metrics using conventional and alternative formulas.

BACKGROUND AND AIM: The Six Sigma approach, employing Sigma Metrics (SM), is commonly used to evaluate analytical performance in clinical laboratories. However, there is ongoing debate regarding the suitability of the conventional SM formula, which incorporates total allowable error (TEa) and bias. To address this, an alternative formula based on within-subject biological variation (CVI) as the tolerance range (TR) has been proposed. The study aimed to calculate and compare SM values using both formulas. MATERIAL AND METHODS: Twenty clinical chemistry parameters were evaluated, and SM values were calculated using conventional formula with two TEa goals and the alternative formula. Intermediate precision (CVA%) values were obtained from internal quality control data, while bias values were derived from external quality assessment reports. RESULTS: The results showed that using the conventional formula, 11 SM values based on CLIA TEa goals and 21 SM values based on BV TEa goals were deemed unacceptable (SM < 3). Additionally, 22 SM values calculated using the alternative formula were below 3. CONCLUSION: The choice of TR had a substantial impact on the assessed analytical performance. Laboratories should carefully consider the appropriateness of each approach based on their specific quality objectives, analyte characteristics, and laboratory operations.