Lean Six Sigma Approach for Reducing Length of Hospital Stay for Patients with Femur Fracture in a University Hospital

The classification algorithms to support the management of the patient with femur fracture

Effectiveness in health care is a specific characteristic of each intervention and outcome evaluated. Especially with regard to surgical interventions, organization, structure and processes play a key role in determining this parameter. In addition, health care services by definition operate in a context of limited resources, so rationalization of service organization becomes the primary goal for health care management. This aspect becomes even more relevant for those surgical services for which there are high volumes. Therefore, in order to support and optimize the management of patients undergoing surgical procedures, the data analysis could play a significant role. To this end, in this study used different classification algorithms for characterizing the process of patients undergoing surgery for a femoral neck fracture. The models showed significant accuracy with values of 81%, and parameters such as Anaemia and Gender proved to be determined risk factors for the patient's length of stay. The predictive power of the implemented model is assessed and discussed in view of its capability to support the management and optimisation of the hospitalisation process for femoral neck fracture, and is compared with different model in order to identify the most promising algorithms. In the end, the support of artificial intelligence algorithms laying the basis for building more accurate decision-support tools for healthcare practitioners.

Lean Six Sigma Approach to Improve the Management of Patients Undergoing Laparoscopic Cholecystectomy

Laparoscopic cholecystectomy (LC) is the gold standard technique for gallbladder diseases in both emergency and elective surgery. The incidence of the disease related to an increasingly elderly population coupled with the efficacy and safety of LC treatment resulted in an increase in the frequency of interventions without an increase in surgical mortality. For these reasons, managers implement strategies by which to standardize the process of patients undergoing LC. Specifically, the goal is to ensure, in accordance with the guidelines of the Italian Ministry of Health, a reduction in post-operative length of stay (LOS). In this study, a Lean Six Sigma (LSS) methodological approach was implemented to identify and subsequently investigate, through statistical analysis, the effect that corrective actions have had on the post-operative hospitalization for LC interventions performed in a University Hospital. The analysis of the process, which involved a sample of 478 patients, with an approach guided by the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle, made it possible to reduce the post-operative LOS from an average of 6.67 to 4.44 days. The most significant reduction was obtained for the 60-69 age group, for whom the probability of using LC is higher than for younger people. The LSS offers a methodological rigor that has allowed us, as already known, to make significant improvements to the process, standardizing the result by limiting the variability and obtaining a total reduction of post-operative LOS of 67%.

A cost analysis of treating postoperative periprosthetic femoral fractures following hip replacement surgery in a UK tertiary referral centre

AIM

This study aims to evaluate costs associated with periprosthetic femoral fracture (PFF) treatment at a UK tertiary referral centre.

METHODS

This study included 128 consecutive PFFs admitted from 02/04/2014-19/05/2020. Financial data were provided by Patient Level Information and Costing Systems. Primary outcomes were median cost and margin. Secondary outcomes were length of stay, blood transfusion, critical care, 30-day readmission, 2-year local complication, 2-year systemic complication, 2-year reoperation and 30-day mortality rates. Statistical comparisons were made between treatment type. Statistical significance was set at p<0.05.

RESULTS

Across the cohort, median cost was £15,644.00 (IQR £11,031.00-£22,255.00) and median loss was £3757.50 (£599.20-£8296.20). The highest costs were ward stay (£3994.00, IQR £1,765.00-£7,013.00), theatre utilisation (£2962.00, IQR £0.00-£4,286.00) and overheads (£1705.10, IQR £896.70-£2432.20). Cost (£17,455.00 [IQR, £13,194.00-£23,308.00] versus £7697.00 [IQR £3871.00-£10,847.00], p<0.001) and loss (£4890.00 [IQR £1308.00-£10,009.00] versus £1882.00 [IQR £313.00-£3851.00], p = 0.02) were greater in the operative versus the nonoperative group. There was no difference in cost (£17,634.00 [IQR £12,965.00-£22,958.00] versus £17,399.00 [IQR £13,394.00-£23,404.00], p = 0.98) or loss (£5374.00 [IQR £1950.00-£10,143.00] versus £3860.00 [IQR -£95.50-£7601.00], p = 0.21) between the open reduction and internal fixation (ORIF) and revision groups. More patients required blood transfusion in the operative versus the nonoperative group (17 [17.9%] versus 0 [0.0%], p = 0.009). There was no difference in any clinical outcome between the ORIF and revision groups (p>0.05).

CONCLUSION

PFF treatment costs are high with inadequate reimbursement from NHS tariff. Work is needed to address this disparity and reduce hospital costs. Cost should not be used to decide between ORIF and revision surgery.

Lean Six Sigma to reduce the acute myocardial infarction mortality rate: a single center study

Purpose

Cardiovascular diseases are the leading cause of death worldwide. In Italy, acute myocardial infarction (AMI) is a major cause of hospitalization and healthcare costs. AMI is a myocardial necrosis event caused by an unstable ischemic syndrome. The Italian government has defined an indicator called “AMI: 30-day mortality” to assess the quality of the overall care pathway of the heart attacked patient. In order to guarantee high standards, all hospitals had to implement techniques to increase the quality of care pathway. The aim of the paper is to identify the root cause and understand the mortality rate for AMI and redesign the patient management process in order to improve it.

Design/methodology/approach

A Lean Six Sigma (LSS) approach was used in this study to analyze the patient flow in order to reduce 30-days mortality rate from AMI registered by Complex Operative Unit (COU) of Cardiology of an Italian hospital. Value stream mapping (VSM) and Ishikawa diagrams were implemented as tools of analysis.

Findings

Process improvement using LSS methodology made it possible to reduce the overall times from 115 minutes to 75 minutes, with a reduction of 35%. In addition, the corrective actions such as the activation of a post-discharge outpatient clinic and telephone contacts allowed the 30-day mortality rate to be lowered from 16% before the project to 8% after the project. In this way, the limit value set by the Italian government was reached.

Research limitations/implications

The limitation of the study is that it is single-centered and was applied to a facility with a limited number of cases.

Practical implications

The LSS approach has brought significant benefits to the process of managing patients with AMI. Corrective actions such as the activation of an effective shared protocol or telephone interview with checklist can become the gold standard in reducing mortality. The limitation of the study is that it is single-centered and was applied to a facility with a limited number of cases.

Originality/value

LSS, applied for the first time to the management of cardiovascular diseases in Italy, is a methodology which has proved to be strategic for the improvement of healthcare process. The simple solutions implemented could serve as a guide for other hospitals to pursue the national AMI mortality target.

Predictive model for prolonged length of hospital stay in patients with osteoporotic femoral neck fracture: A 5-year retrospective study

Prolonged length of stay (LOS) in osteoporotic femoral neck fracture patients increased the hospital care cost and demonstrated in-hospital complications. This study aimed to develop an ease-of use predictive model of prolonged LOS in osteoporotic femoral neck fracture patients. In this 5-year retrospective study, the medical charts of 255 patients admitted to hospital with an osteoporotic femoral neck fracture resulting from a simple fall from January 2014 to December 2018 were reviewed. Multivariable fractional polynomials (MFP) algorithms was applied to develop the predictive model from candidate predictors of prolonged LOS. The discrimination performance of predictive model was evaluated using the receiver operating characteristic curve (ROC). Internal validity was assessed using bootstrapping. From 289 patients who were hospitalized with an osteoporotic fracture of femoral neck throughout this study, 255 (88%) fulfilled the inclusion criteria. There was 54.90% (140 of 255 patients) of patients who had prolonged LOS. The predictors of the predictive model were age, BMI, ASA score class 3 or 4, arthroplasty and time from injury to surgery. The area under ROC curve of the model was 0.83 (95% confidence interval 0.77-0.88). Internal validation with bootstrap re-sampling revealed an optimism of -0.002 (range -0.300-0.296) with an estimated shrinkage factor of 0.907 for the predictive model. The current predictive model developed from preoperative predictors which had a good discriminative ability to differentiate between length of hospitalization less than 14 days and prolonged LOS in osteoporotic femoral neck patients. This model can be applied as ease-of use calculator application to help patients, their families and clinicians make appropriate decisions in terms of treatment planning, postoperative care program, and cost-effectiveness before patients receiving the definitive treatments.

Implementation of Lean Management Tools Using an Example of Analysis of Prolonged Stays of Patients in a Multi-Specialist Hospital in Poland

Healthcare institutions in Poland constantly encounter challenges related both to the quality of provided services and to the pressures associated with treatment effectiveness and economic efficiency. The implemented solutions have a goal of improving the service quality of lowering the continuously increasing operational costs. The aim of this paper is to present the application of Lean Management (LM) tools in a Polish hospital, which allowed for the identification of prolonged stays as one of the main issues affecting the service costs and the deteriorating financial results of the hospital. The study was conducted in the neurology department and involved an analysis of data for the whole of 2019 and the first half of 2022. In addition, surveys were conducted among the medical staff to help identify the main causes of prolonged stays. Methods of data analysis and feasible solutions were developed in order to improve the economic efficiency of the unit. The analysis shows that the application of LM tools may contribute to improvement in the functioning of hospitals and that further studies should focus on the development of the method to evaluate efficiency of the implemented solutions intended at shortening the hospital stays of the patients.

FACTORS ASSOCIATED WITH THE OUTCOMES OF OLDER PATIENTS OPERATED DUE TO HIP FRACTURES

Objective

Evaluating clinical factors associated with mortality in older patients who underwent surgical correction of hip fractures.

Methods

This observational and retrospective study analyzed the medical records of 67 patients (aged older than 60 years), both men and women, who underwent surgical correction of hip fractures from 2019 to 2020 at Hospital São Paulo. The following variables were analyzed: age, sex, presence of comorbidities, affected hip region, and trauma mechanism. Statistical analyses were performed using the SPSS software.

Results

The mean age of patients was 78.12 ± 9.80 years and 80.6% of the sample were women. The prevalence of hip fractures on the right side (52.2%), in the transtrochanteric region (53.7%), and due to fall on the same level (88.1%) was higher. Systemic arterial hypertension (77.6%), diabetes mellitus (37.3%), and dementia (16.4%) were frequent comorbidities. The prevalence of death after fracture was 17.9% and it was associated with longer hospital stay after surgery (p = 0.028).

Conclusion

The prevalence of mortality of patients with hip fractures who underwent surgery was 17.9%. A longer hospital stay due to pre-existing comorbidities was the main factor related to this outcome. Level of Evidence III, Retrospective Study.

Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi

The length of stay (LOS) in hospital is one of the main parameters for evaluating the management of a health facility, of its departments in relation to the different specializations. Healthcare costs are in fact closely linked to this parameter as well as the profit margin. In the orthopedic field, the provision of this parameter is increasingly complex and of fundamental importance in order to be able to evaluate the planning of resources, the waiting times for any scheduled interventions and the management of the department and related surgical interventions. The purpose of this work is to predict and evaluate the LOS value using machine learning methods and applying multiple linear regression, starting from clinical data of patients hospitalized with lower limb fractures. The data were collected at the "San Giovanni di Dio e Ruggi d'Aragona" hospital in Salerno (Italy).

The management of healthcare-related infections through lean methodology: systematic review and meta-analysis of observational studies

INTRODUCTION

Lean is largely applied to the health sector and on the healthcare-associated infections (HAI). However, a few results on the improvement of the outcome have been reported in literature. The purpose of this study is to analyze if the lean application can reduce the HAI rate.

METHODS

A comprehensive search was performed on PubMed/Medline, Scopus, CINAHL, Cochrane, Embase, and Google Scholar databases using various combinations of the following keywords: "lean" and "infection". Inclusion criteria were: 1) research articles with quantitative data and relevant information on lean methodology and its impact on healthcare infections; 2) prospective studies. The risk of bias and the study quality was independently assessed by two researchers using the "The National Institutes of Health (NIH) quality assessment tool for before-after (Pre-Post) study with no control group". The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines has been used. 22 studies were included in the present meta-analysis.

RESULTS

Lean application demonstrated a significant protective role on healthcare-associated infections rate (RR 0.50; 95% C.I.: 0.38-0.66) with significant impact on central line-associated bloodstream infections (CLABSIs) (RR 0.47; 95% C.I.: 0.28-0.82).

CONCLUSIONS

Lean has a positive impact on the decreasing of HAIs and on the improvement of compliance and satisfaction of the staff.

Application of six sigma and the system thinking approach in COVID-19 operation management: a case study of the victorian aged care response centre (VACRC) in Australia

COVID-19 has posed many unique and critical challenges in various contexts and circumstances. This often led the stakeholders and decision-makers to depart from traditional thinking and the business-as-usual processes and to come up with innovative approaches to tackle various mission-critical situations within a short time frame. In this paper, a real-life case study of COVID-19 operation management following a multi-disciplinary, multi-stakeholder novel integrated approach in aged care facilities in Victoria, Australia, is presented which yielded significant and positive outcomes. The purpose of the intervention was to develop an integrated system performance approach through the application of various quality management tools and techniques to achieve organizational excellence at the aged care centers. The case involved the use of mathematical models along with statistical tools and techniques to address the specific problem scenario. A system-wide management plan was proposed, involving various agencies across several residential aged care facilities during the pandemic. A three-step methodological framework was developed, where Six Sigma, a system thinking approach, and a holistic metric were proposed to manage the value chain of the pandemic management system. The experimental result analyses showed significant improvement in the management process, suggesting the validity and potential of this holistic approach to stabilize the situation and subsequently set the conditions for operations excellence within the sectors. The model offers new insight into the existing body of knowledge and offers an efficient approach to achieving operational excellence in any organization or business regardless of its type, shape and complexity, which can help practitioners in managing complex, mission-critical situations like a pandemic.

Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

Background

The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value.

Methods

In this study, a predictive model was built for the total LOS of patients undergoing laparoscopic appendectomy, one of the most common emergency procedures. Demographic and clinical data of the 357 patients admitted at “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno (Italy) had used as independent variable of the multiple linear regression model.

Results

The obtained model had an R² value of 0.570 and, among the independent variables, the significant variables that most influence the total LOS were Age, Pre-operative LOS, Presence of Complication and Complicated diagnosis.

Conclusion

This work designed an effective and automated strategy for improving the prediction of LOS, that can be useful for enhancing the preoperative pathways. In this way it is possible to characterize the demand and to be able to estimate a priori the occupation of the beds and other related hospital resources.

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

The proximal fracture of the femur and hip is the most common reason for hospitalization in orthopedic departments. In Italy, 115,989 hip-replacement surgeries were performed in 2019, showing the economic relevance of studying this type of procedure. This study analyzed the data relating to patients who underwent hip-replacement surgery in the years 2010-2020 at the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno. The multiple linear regression (MLR) model and regression and classification algorithms were implemented in order to predict the total length of stay (LOS). Lastly, using a statistical analysis, the impact of COVID-19 was evaluated. The results obtained from the regression analysis showed that the best model was MLR, with an R² value of 0.616, compared with XGBoost, Gradient-Boosted Tree, and Random Forest, with R² values of 0.552, 0.543, and 0.448, respectively. The t-test showed that the variables that most influenced the LOS, with the exception of pre-operative LOS, were gender, age, anemia, fracture/dislocation, and urinary disorders. Among the classification algorithms, the best result was obtained with Random Forest, with a sensitivity of the longest LOS of over 89%. In terms of the overall accuracy, Random Forest and Gradient-Boosted Tree achieved a value of 71.76% and an error of 28.24%, followed by Decision Tree, with an accuracy of 71.13% and an error of 28.87%, and, finally, Support Vector Machine, with an accuracy of 65.06% and an error of 34.94%. A significant difference in cardiovascular disease, fracture/dislocation, and post-operative LOS variables was shown by the chi-squared test and Mann-Whitney test in the comparison between 2019 (before COVID-19) and 2020 (in full pandemic emergency conditions).

Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study

Background: In health, it is important to promote the effectiveness, efficiency and adequacy of the services provided; these concepts become even more important in the era of the COVID-19 pandemic, where efforts to manage the disease have absorbed all hospital resources. The COVID-19 emergency led to a profound restructuring—in a very short time—of the Italian hospital system. Some factors that impose higher costs on hospitals are inappropriate hospitalization and length of stay (LOS). The length of stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. Methods: This study analyzed how COVID-19 changed the activity of the Complex Operative Unit (COU) of the Neurology and Stroke Unit of the San Giovanni di Dio e Ruggi d’Aragona University Hospital of Salerno (Italy). The methodology used in this study was Lean Six Sigma. Problem solving in Lean Six Sigma is the DMAIC roadmap, characterized by five operational phases. To add even more value to the processing, a single clinical case, represented by stroke patients, was investigated to verify the specific impact of the pandemic. Results: The results obtained show a reduction in LOS for stroke patients and an increase in the value of the diagnosis related group relative weight. Conclusions: This work has shown how, thanks to the implementation of protocols for the management of the COU of the Neurology and Stroke Unit, the work of doctors has improved, and this is evident from the values of the parameters taken into consideration.

Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture

Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R²) was achieved by the support vector machine (R² = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare.

Regression Models to Study the Total LOS Related to Valvuloplasty

Background: Valvular heart diseases are diseases that affect the valves by altering the normal circulation of blood within the heart. In recent years, the use of valvuloplasty has become recurrent due to the increase in calcific valve disease, which usually occurs in the elderly, and mitral valve regurgitation. For this reason, it is critical to be able to best manage the patient undergoing this surgery. To accomplish this, the length of stay (LOS) is used as a quality indicator. Methods: A multiple linear regression model and four other regression algorithms were used to study the total LOS function of a set of independent variables related to the clinical and demographic characteristics of patients. The study was conducted at the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno (Italy) in the years 2010–2020. Results: Overall, the MLR model proved to be the best, with an R² value of 0.720. Among the independent variables, age, pre-operative LOS, congestive heart failure, and peripheral vascular disease were those that mainly influenced the output value. Conclusions: LOS proves, once again, to be a strategic indicator for hospital resource management, and simple linear regression models have shown excellent results to analyze it.

Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study

Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy).

The Use of Lean Six Sigma Methodology in the Reduction of Patient Length of Stay Following Anterior Cruciate Ligament Reconstruction Surgery

Background: The purpose of this study was to reduce the length of stay of anterior cruciate ligament reconstruction patients within a private hospital in Ireland, reducing any non-value-added activity in the patient pathway, with the goal of increasing patient flow, bed capacity, and revenue generation within the hospital system, while maintaining patient satisfaction. Methods: We used a pre-/post-intervention design and Lean Six Sigma methods and tools to assess and improve the current process. Results: A reduction in inpatient length of stay by 57%, and a reduction in identified non-value-added activity by 88%, resulted in a new day-case surgery pathway for anterior cruciate ligament reconstruction patients. The pathway evidenced no re-admissions and demonstrated patient satisfaction. Conclusion: Six months post-project commencement, we had successfully achieved our goals of reducing our anterior cruciate ligament reconstruction patient's length of stay. This study contributes to the growing body of published evidence which shows that adopting a Lean Six Sigma approach can be successfully employed to optimise care and surgical pathways in healthcare.

Operation Note Transformation: The Application of Lean Six Sigma to Improve the Process of Documenting the Operation Note in a Private Hospital Setting

Clinical documentation is a key safety and quality risk, particularly at transitions of care where there is a higher risk of information being miscommunicated or lost. A surgical operation note (ON) is an essential medicolegal document to ensure continuity of patient care between the surgical operating team and other colleagues, which should be completed immediately following surgery. Incomplete operating surgeon documentation of the ON, in a legible and timely manner, impacts the quality of information available to nurses to deliver post-operative care. In the project site, a private hospital in Dublin, Ireland, the accuracy of completion of the ON across all surgical specialties was 20%. This project sought to improve the accuracy, legibility, and completeness of the ON in the Operating Room. A multidisciplinary team of staff utilised the Lean Six Sigma (LSS) methodology, specifically the Define/Measure/Analyse/Design/Verify (DMADV) framework, to design a new digital process application for documenting the ON. Post-introduction of the new design, 100% of the ONs were completed digitally with a corresponding cost saving of EUR 10,000 annually. The time to complete the ON was reduced by 30% due to the designed digital platform and mandatory fields, ensuring 100% of the document is legible. As a result, this project significantly improved the quality and timely production of the ON within a digital solution. The success of the newly designed ON process demonstrates the effectiveness of the DMADV in establishing a co-designed, value-adding process for post-operative surgical notes.

Implementation of lean Six-Sigma project in enhancing health care service quality during COVID-19 pandemic

The recent outbreak of coronavirus (COVID-19) pandemic has exposed the weakness of the existing healthcare facilities in developing countries, and Pakistan has no exception. The increasing amount of patients has made this condition more vulnerable to failure. It became difficult for health care management to handle the surge of patients. This case study is based on the XYZ hospital system of Pakistan. The hospital initiates passive immunization as a savior in the absence of a vaccine. The process initiates numerous challenges as the same facility was using for passive immunization and routine operations of the hospital. DMAIC lean sig-sigma problem-solving methodology has been adopted to Define, Measure, Analyze, Implement and Control the improvement process for smooth special and routine activities. The staff and patients were interviewed, their issues were listed, and a comprehensive solution was suggested to deal with operational uncertainties. The results identified various factors through VOC and SIPOC processes, prioritized using fishbone diagram, analyzed through Kano model, and finally proposed process improvement by incorporating Kaizen process improvement methodology. Other industries could use this set of tools to evaluate and optimize routine problems, which ultimately enhances the quality and reduces cost.

Enhancing palliative care occupancy and efficiency: a quality improvement project that uses a healthcare pathway for service integration and policy development

This article described our experience in implementing a quality improvement project to overcome the bed overcapacity problem at a comprehensive cancer centre in a tertiary care centre. We formed a multidisciplinary team including a representative from patient and family support (six members), hospice care and home care services (four members), multidisciplinary team development (four members) and the national lead. The primary responsibility of the formulated team was implementing measures to optimise and manage patient flow. We used the plan-do-study-act cycle to engage all stakeholders from all service layers, test some interventions in simplified pilots and develop a more detailed plan and business case for further implementation and roll-out, which was used as a problem-solving approach in our project for refining a process or implementing changes. As a result, we observed a significant reduction in bed capacity from 35% in 2017 to 13.8% in 2018. While the original length of stay (LOS) was 28 days, the average LOS was 19 days in 2017 (including the time before and after the intervention), 10.8 days in 2018 (after the intervention was implemented), 10.1 days in 2019 and 16 days in 2020. The increase in 2020 parameters was caused by the COVID-19 pandemic, since many patients did not enrol in our new care model. Using a systematic care delivery approach by a multidisciplinary team improves significantly reduced bed occupancy and reduces LOS for palliative care patients.

Efficiency measures of emergency departments: an Italian systematic literature review

Life expectancy globally increased in the last decades: the number of people aged 65 or older is consequently projected to grow, and healthcare demand will increase as well. In the recent years, the number of patients visiting the hospital emergency departments (EDs) rocked in almost all countries of the world. These departments are crucial in all healthcare systems and play a critical role in providing an efficient assistance to all patients. A systematic literature review covering PubMed, Scopus and the Cochrane Library was performed from 2009 to 2019. Of the 718 references found in the literature research, more than 25 studies were included in the current review. Different predictors were associated with the quality of EDs care, which may help to define and implement preventive strategies in the near future. There is no harmonisation in efficiency measurements reflecting the performance in the ED setting. The identification of consistent measures of efficiency is crucial to build an evidence base for future initiatives. The aim of this study is to review the literature on the problems encountered in the efficiency of EDs around the world in order to identify an organisational model or guidelines that can be implemented in EDs to fill inefficiencies and ensure access optimal treatment both in terms of resources and timing. This review will support policy makers to improve the quality of health facilities, and, consequently of the entire healthcare systems.

Application of DMAIC Cycle and Modeling as Tools for Health Technology Assessment in a University Hospital

Background

The Health Technology Assessment (HTA) is used to evaluate health services, manage healthcare processes more efficiently, and compare medical technologies. The aim of this paper is to carry out an HTA study that compares two pharmacological therapies and provides the clinicians with two models to predict the length of hospital stay (LOS) of patients undergoing oral cavity cancer surgery on the bone tissue.

Methods

The six Sigma method was used as a tool of HTA; it is a technique of quality management and process improvement that combines the use of statistics with a five-step procedure: “Define, Measure, Analyze, Improve, Control” referred to in the acronym DMAIC. Subsequently, multiple linear regression has been used to create two models. Two groups of patients were analyzed: 45 were treated with ceftriaxone while 48 were treated with the combination of cefazolin and clindamycin.

Results

A reduction of the overall mean LOS of patients undergoing oral cavity cancer surgery on bone was observed of 40.9% in the group treated with ceftriaxone. Its reduction was observed in all the variables of the ceftriaxone group. The best results are obtained in younger patients (−54.1%) and in patients with low oral hygiene (−52.4%) treated. The regression results showed that the best LOS predictors for cefazolin/clindamycin are ASA score and flap while for ceftriaxone, in addition to these two, oral hygiene and lymphadenectomy are the best predictors. In addition, the adjusted R squared showed that the variables considered explain most of the variance of LOS.

Conclusion

SS methodology, used as an HTA tool, allowed us to understand the performance of the antibiotics and provided variables that mostly influence postoperative LOS. The obtained models can improve the outcome of patients, reducing the postoperative LOS and the relative costs, consequently increasing patient safety, and improving the quality of care provided.

Six Sigma in Health Literature, What Matters?

Six Sigma has been widely used in the health field for process or quality improvement, constituting a quite profusely investigated topic. This paper aims at exploring why some studies have more academic and societal impact, attracting more attention from academics and health professionals. Academic and societal impact was addressed using traditional academic metrics and alternative metrics, often known as altmetrics. We conducted a systematic search following the PRISMA statement through three well-known databases, and identified 212 papers published during 1998–2019. We conducted zero-inflated negative binomial regressions to explore the influence of bibliometric and content determinants on traditional academic and alternative metrics. We observe that the factors influencing alternative metrics are more varied and difficult to apprehend than those explaining traditional impact metrics. We also conclude that, independently of how the impact is measured, the paper’s content, rather than bibliometric characteristics, better explains its impact. In the specific case of research on Six Sigma applied to health, the papers with more impact address process improvement focusing on time and waste reduction. This study sheds light on the aspects that better explain publications’ impact in the field of Six Sigma application in health, either from an academic or a societal point of view.

Improving performance of the hospitalization process by applying the principles of Lean Thinking

Purpose

The goal was to improve the quality of the hospitalization process and the management of patients, allowing the reduction of costs and the minimization of the preoperative Length of Hospital Stay (LOS).

Design/methodology/approach

The methodology used to improve the quality of the hospitalization process and patient management was Lean Thinking. Therefore, the Lean tools (Value stream map and Ishikawa diagram) were used to identify waste and inefficiencies, improving the process with the implementation of corrective actions. The data was collected through personal observations, patient interviews, brainstorming and from printed medical records of 151 patients undergoing oral cancer surgery in the period from 2006 to 2018.

Findings

The authors identified, through Value Stream Map, waste and inefficiencies during preoperative activities, consequently influencing preoperative LOS, considered the best performance indicator. The main causes were identified through the Ishikawa diagram, allowing reflection on possible solutions. The main corrective action was the introduction of the pre-hospitalization service. A comparative statistical analysis showed the significance of the solutions implemented. The average preoperative LOS decreased from 4.90 to 3.80 days (−22.40%) with a p-value of 0.001.

Originality/value

The methodology allowed to highlight the improvement of the patient hospitalization process with the introduction of the pre-hospitalization service. Therefore, by adopting the culture of continuous improvement, the flow of hospitalization was redrawn. The benefits of the solutions implemented are addressed to the patient in terms of lower LOS and greater service satisfaction and to the hospital for lower patient management costs and improved process quality. This article will be useful for those who need examples on how to apply Lean tools in healthcare.

Health technology assessment through the six sigma approach in abdominoplasty: Scalpel vs electrosurgery

Abdominoplasty is a surgical procedure conducted to reduce excess abdominal skin and fat and improve body contouring. Despite being commonly performed, it is associated with a risk of complications such as infection, seroma, haematoma and wound dehiscence. To reduce the incidence of complications, different methods are used to create the abdominal flap, i.e., incision with a scalpel or electrosurgery. In this study, health technology assessment (HTA) using the Six Sigma methodology was conducted to compare these incision techniques in patients undergoing abdominoplasty. Two consecutively enroled groups of patients (33 in the scalpel group and 35 in the electrosurgery group) who underwent surgery at a single institution, the University of Campania "Luigi Vanvitelli", were analysed using the drain output as the main outcome for comparison of the incision techniques. While no difference was found regarding haematoma or seroma formation (no cases in either group), the main results also indicate a greater drain output (p-value<0.001) and a greater incidence of dehiscence (p-value=0.056) in patients whose incisions were made through electrosurgery. The combination of HTA and the Six Sigma methodology was useful to prove the possible advantages of creating skin incisions with a scalpel in full abdominoplasty, particularly a significant reduction in the total drain output and a reduction in wound healing problems, namely, wound dehiscence, when compared with electrosurgery, despite considering two limited and heterogeneous groups.

Big Data-Enabled Analysis of Factors Affecting Patient Waiting Time in the Nephrology Department of a Large Tertiary Hospital

The length of waiting time has become an important indicator of the efficiency of medical services and the quality of medical care. Lengthy waiting times for patients will inevitably affect their mood and reduce satisfaction. For patients who are in urgent need of hospitalization, delayed admission often leads to exacerbation of the patient's condition and may threaten the patient's life. We gathered patients' information about outpatient visits and hospital admissions in the Nephrology Department of a large tertiary hospital in western China from January 1st, 2014, to December 31st, 2016, and we used big data-enabled analysis methods, including univariate analysis and multivariate linear regression models, to explore the factors affecting waiting time. We found that gender (P=0.048), the day of issuing the admission card (Saturday, P=0.028), the applied period for admission (P < 0.001), and the registration interval (P < 0.001) were positive influencing factors of patients' waiting time. Disease type (after kidney transplantation, P < 0.001), number of diagnoses (P=0.037), and the day of issuing the admission card (Sunday, P=0.001) were negative factors. A linear regression model built using these data performed well in the identification of factors affecting the waiting time of patients in the Nephrology Department. These results can be extended to other departments and could be valuable for improving patient satisfaction and hospital service quality by identifying the factors affecting waiting time.

A Six Sigma DMAIC methodology as a support tool for Health Technology Assessment of two antibiotics

Health Technology Assessment (HTA) and Six Sigma (SS) have largely proved their reliability in the healthcare context. The former focuses on the assessment of health technologies to be introduced in a healthcare system. The latter deals with the improvement of the quality of services, reducing errors and variability in the healthcare processes. Both the approaches demand a detailed analysis, evidence-based decisions, and efficient control plans. In this paper, the SS is applied as a support tool for HTA of two antibiotics with the final aim of assessing their clinical and organizational impact in terms of postoperative Length Of Stay (LOS) for patients undergoing tongue cancer surgery. More specifically, the SS has been implemented through its main tool, namely the DMAIC (Define, Measure, Analyse, Improve, Control) cycle. Moreover, within the DMAIC cycle, a modelling approach based on a multiple linear regression analysis technique is introduced, in the Control phase, to add complementary information and confirm the results obtained by the statistical analysis performed within the other phases of the SS DMAIC. The obtained results show that the proposed methodology is effective to determine the clinical and organizational impact of each of the examined antibiotics, when LOS is taken as a measure of performance, and guide the decision-making process. Furthermore, our study provides a systematic procedure which, properly combining different and well-assessed tools available in the literature, demonstrated to be a useful guidance for choosing the right treatment based on the available data in the specific circumstance.