Lean Six Sigma approach to reduce LOS through a diagnostic-therapeutic-assistance path at A.O.R.N. A. Cardarelli

Integrated System for the Proactive Analysis on Infection Risk at a University Health Care Establishment Servicing a Large Area in the South of Italy

OBJECTIVES

Our study proposes the use of a proactive system to manage risk combining the new Risk Identification Framework by the World Health Organization, the Lean method, and the hospital's Procedure Analysis.The system was tested for the prevention of surgical site infections in the University Hospital of Naples "Federico II" on the surgical paths, where they were usually applied individually.

METHODS

We conducted a retrospective observational study from March 18, 2019, to June 30, 2019, at the University Hospital "Federico II" of Naples, Italy (Europe).The study is structured in 3 phases: phase 1, application of each proactive risk management tool (March 18-April 15, 2019); phase 2, analysis and integration of the results, and elaboration of an overview of critical and control points (April 15-20, 2019); and phase 3, evaluation of the outcomes as variation of surgical site infection's incidence between the 3-month period of the 2019 and the same period of the 2018, when each tool was implemented separately (April 30-June 30, 2019).

RESULTS

(1) The application of the single tool has detected different criticalities; (2) the combined system allowed us to draw a risk map and identify "improving" macroareas; and (3) the infection rate, with the application of this system, was equal to 1.9%; in the same period of the previous year, it was equal to 4%.

CONCLUSIONS

Our study demonstrates that "integrated system" has been more effective to proactively identify surgical route risks compared with the application of each single instrument.

The role of lean in healthcare during COVID-19 pandemic

Purpose

The impact of the COVID-19 pandemic on healthcare operations has raised questions about the applicability and capacity of the lean approach to respond to critical events. Thus, with a dearth of studies addressing this issue, this study aims to understand the role of lean in healthcare operations under the disruptive impact of the COVID-19 pandemic.

Design/methodology/approach

Drawing on a case study carried out in an emergency department in Brazil during the COVID-19 outbreak, the author presents results from semi-structured interviews and document analysis.

Findings

The results show three prominent themes that respond to this study's purpose: lean applicability during the pandemic, lean challenges during the pandemic and the pandemic impact on the lean processes. Furthermore, the study underscores that lean is not the panacea to operational problems caused by the pandemic in healthcare organisations, but it eases the impact on their operations. Finally, this study contributes to the discipline of operations management and highlights the need to rethink lean applications during disruptive events, focusing on flexibility, adaptability and patients' needs.

Research limitations/implications

The literature addressing the pandemic impact on healthcare operations is still new and emerging; therefore, it is possible that some of the studies that are under review and could contribute to this study were not considered.

Practical implications

The study provides a better understanding of the lessons learned from the real-world experiences gained during the pandemic, helping managers to make informed decisions when developing contingency plans to improve healthcare readiness and responsiveness under crisis conditions (e.g. untenable demand and constrained capacity).

Originality/value

Given the contemporary nature of this pandemic, only few emerging studies addressing the impact of the pandemic on lean healthcare operations are available and scholars are calling for more empirical studies. Furthermore, there is an increasing criticism and scepticism about the applicability of lean in healthcare during a pandemic. Thus, this research both provides original contributions by responding to scholars' calls for novel research in this area and further contributes towards filling the void in the literature.

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.

Comparing Two Approaches for Thyroidectomy: A Health Technology Assessment through DMAIC Cycle

Total thyroidectomy is very common in endocrine surgery and the haemostasis can be obtained in different ways across surgery; recently, some devices have been developed to support this surgical phase. In this paper, a health technology assessment is conducted through the define, measure, analyse, improve, and control cycle of the Six Sigma methodology to compare traditional total thyroidectomy with the surgical operation performed through a new device in an overall population of 104 patients. Length of hospital stay, drain output, and time for surgery were considered the critical to qualities in order to compare the surgical approaches which can be considered equal regarding the organizational, ethical, and security impact. Statistical tests (Kolmogorov–Smirnov, t test, ANOVA, Mann–Whitney, and Kruskal–Wallis tests) and visual management diagrams were employed to compare the approaches, but no statistically significant difference was found between them. Considering these results, this study shows that the introduction of the device to perform total thyroidectomy does not guarantee appreciable clinical advantages. A cost analysis to quantify the economic impact of the device into the practice could be a future development. Healthy policy leaders and clinicians who are requested to make decisions regarding the supply of biomedical technologies could benefit from this research.

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.

A Health Technology Assessment in Maxillofacial Cancer Surgery by Using the Six Sigma Methodology

Squamous cell carcinoma represents the most common cancer affecting the oral cavity. At the University of Naples "Federico II", two different antibiotic protocols were used in patients undergoing oral mucosa cancer surgery from 2006 to 2018. From 2011, there was a shift; the combination of Cefazolin plus Clindamycin as a postoperative prophylactic protocol was chosen. In this paper, a health technology assessment (HTA) is performed by using the Six Sigma and DMAIC (Define, Measure, Analyse, Improve, Control) cycle in order to compare the performance of the antibiotic protocols according to the length of hospital stay (LOS). The data (13 variables) of two groups were collected and analysed; overall, 136 patients were involved. The American Society of Anaesthesiologist score, use of lymphadenectomy or tracheotomy and the presence of infections influenced LOS significantly (p-value < 0.05) in both groups. Then, the groups were compared: the overall difference between LOS of the groups was not statistically significant, but some insights were provided by comparing the LOS of the groups according to each variable. In conclusion, in light of the insights provided by this study regarding the comparison of two antibiotic protocols, the utilization of DMAIC cycle and Six Sigma tools to perform HTA studies could be considered in future research.

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.

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.

Implementing fast track surgery in hip and knee arthroplasty using the lean Six Sigma methodology

Purpose

One of the biggest challenges in the health sector is that of costs compared to economic resources and the quality of services. Hospitals register a progressive increase in expenditure due to the aging of the population. In fact, hip and knee arthroplasty surgery are mainly due to primary osteoarthritis that affects the elderly population. This study was carried out with the aim of analysing the introduction of the fast track surgery protocol, through the lean Six Sigma, on patients undergoing knee and hip prosthetic replacement surgery. The goal was to improve the arthroplasty surgery process by reducing the average length of stay (LOA) and hospital costs

Design/methodology/approach

Lean Six Sigma was applied to evaluate the arthroplasty surgery process through the DMAIC cycle (define, measure, analyse, improve and control) and the lean tools (value stream map), adopted to analyse the new protocol and improve process performance. The dataset consisted of two samples of patients: 54 patients before the introduction of the protocol and 111 patients after the improvement. Clinical and demographic variables were collected for each patient (gender, age, allergies, diabetes, cardiovascular diseases and American Society of Anaesthesiologists (ASA) score).

Findings

The results showed a 12.70% statistically significant decrease in LOS from an overall average of 8.72 to 7.61 days. Women patients without allergies, with a low ASA score not suffering from diabetes and cardiovascular disease showed a significant a reduction in hospital days with the implementation of the FTS protocol. Only the age variable was not statistically significant.

Originality/value

The introduction of the FTS in the orthopaedic field, analysed through the LSS, demonstrated to reduce LOS and, consequently, costs. For each individual patient, there was an economic saving of € 445.85. Since our study takes into consideration a dataset of 111 patients post-FTS, the overall economic saving brought by this study amounts to €49,489.35.

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.

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.

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

Surgical intervention within 48 h of hospital admission is the gold standard procedure for the management of elderly patients with femur fractures, since the increase in preoperative waiting time is correlated with the onset of complications and longer overall length of stay (LOS) in the hospital. However, national evidence demonstrates that there is still the need to provide timely intervention for this type of patient, especially in some regions of central southern Italy. Here we discuss the introduction of a diagnostic–therapeutic assistance pathway (DTAP) to reduce the preoperative LOS for patients undergoing femur fracture surgery in a university hospital. A Lean Six Sigma methodology, based on the DMAIC cycle (Define, Measure, Analyze, Improve, Control), is implemented to evaluate the effectiveness of the DTAP. Data were retrospectively collected and analyzed from two groups of patients before and after the implementation of DTAP over a period of 10 years. The statistics of the process measured before the DTAP showed an average preoperative LOS of 5.6 days (standard deviation of 3.2), thus confirming the need for corrective actions to reduce the LOS in compliance with the national guidelines. The influence of demographic and anamnestic variables on the LOS was evaluated, and the impact of the DTAP was measured and discussed, demonstrating the effectiveness of the improvement actions implemented over the years and leading to a significant reduction in the preoperative LOS, which decreased to an average of 3.5 days (standard deviation of 3.60). The obtained reduction of 39% in the average LOS proved to be in good agreement with previously developed DTAPs for femur fracture available in the literature.

Measuring operational efficiency of isolation hospitals during COVID-19 pandemic using data envelopment analysis: a case of Egypt

Purpose

This study used Data Envelopment Analysis (DEA) to measure and evaluate the operational efficiency of 26 isolation hospitals in Egypt during the COVID-19 pandemic, as well as identifying the most important inputs affecting their efficiency.

Design/methodology/approach

To measure the operational efficiency of isolation hospitals, this paper combined three interrelated methodologies including DEA, sensitivity analysis and Tobit regression, as well as three inputs (number of physicians, number of nurses and number of beds) and three outputs (number of infections, number of recoveries and number of deaths). Available data were analyzed through R v.4.0.1 software to achieve the study purpose.

Findings

Based on DEA analysis, out of 26 isolation hospitals, only 4 were found efficient according to CCR model and 12 out of 26 hospitals achieved efficiency under the BCC model, Tobit regression results confirmed that the number of nurses and the number of beds are common factors impacted the operational efficiency of isolation hospitals, while the number of physicians had no significant effect on efficiency.

Research limitations/implications

The limits of this study related to measuring the operational efficiency of isolation hospitals in Egypt considering the available data for the period from February to August 2020. DEA analysis can also be an important benchmarking tool for measuring the operational efficiency of isolation hospitals, for identifying their ability to utilize and allocate their resources in an optimal manner (Demand vs Capacity Dilemma), which in turn, encountering this pandemic and protect citizens' health.

Originality/value

Despite the intensity of studies that dealt with measuring hospital efficiency, this study to the best of our knowledge is one of the first attempts to measure the efficiency of hospitals in Egypt in times of health' crisis, especially, during the COVID-19 pandemic, to identify the best allocation of resources to achieve the highest level of efficiency during this pandemic.

Evaluation of System Modelling Techniques for Waste Identification in Lean Healthcare Applications

Purpose

Waste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes.

Methods

This study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi.

Results

VSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste.

Conclusion

As behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.

Implementation and validation of a new method to model voluntary departures from emergency departments. Running Title: Modeling Voluntary departures from emergency departments

In the literature, several organizational solutions have been proposed for determining the probability of voluntary patient discharge from the emergency department. Here, the issue of self-discharge is analyzed by Markov theory-based modeling, an innovative approach diffusely applied in the healthcare field in recent years. The aim of this work is to propose a new method for calculating the rate of voluntary discharge by defining a generic model to describe the process of first aid using a "behavioral" Markov chain model, a new approach that takes into account the satisfaction of the patient. The proposed model is then implemented in MATLAB and validated with a real case study from the hospital "A. Cardarelli" of Naples. It is found that most of the risk of self-discharge occurs during the wait time before the patient is seen and during the wait time for the final report; usually, once the analysis is requested, the patient, although not very satisfied, is willing to wait longer for the results. The model allows the description of the first aid process from the perspective of the patient. The presented model is generic and can be adapted to each hospital facility by changing only the transition probabilities between states.

Factors Influencing Private Hospitals' Participation in the Innovation of Biomedical Engineering Industry: A Perspective of Evolutionary Game Theory

The innovation of the biomedical engineering (BME) industry is inseparable from its cooperation with medical institutions. China has considerable medical institutions. Although private hospitals account for more than half of Chinese medical institutions, they rarely participate in biomedical engineering industry innovation. This paper analyzed the collaborative relationship among biomedical engineering enterprises, universities, research institutes, public hospitals and private hospitals through evolutionary game theory and discussed the influence of different factors on the collaborative innovation among them. A tripartite evolutionary game model is established which regards private hospitals as a stakeholder. The results show that (1) the good credit of private hospitals has a positive effect on their participation in collaborative innovation; (2) it is helpful for BME collaborative innovation to enhance the collaborative innovation ability of partners; (3) the novelty of innovation projects has an impact on BME collaborative innovation. The specific impacts depend on the revenue, cost and risk allocation ratio of innovation partners; (4) the higher the practicability of innovation projects, the more conducive to collaborative innovation.

Lean Six Sigma competitiveness for micro, small and medium enterprises (MSME): an action research in the Indian context

Purpose

The aim of the article is to ascertain the challenges, lessons learned and managerial implications in the deployment of Lean Six Sigma (LSS) competitiveness to micro, small and medium Enterprises (MSME) in India and to establish doctrines to strengthen the initiatives of the government.

Design/methodology/approach

The research adopts the Action Research methodology to develop a case study, which is carried out in the printing industry in a Tier III city using the LSS DMAIC (Define-Measure-Analyze-Improve-Control) approach. It utilizes LSS tools to deploy the strategy and to unearth the challenges and success factors in improving the printing process of a specific batch of a product.

Findings

The root cause for the critical to quality (CTQ) characteristic, turn-around-time (TAT) is determined and the solutions are deployed through the scientifically proven data-based approach. As a result of this study, the TAT reduced from an average of 1541.2–1303.36 min, which in turn, improved the sigma level from 0.55 to 2.96, a noteworthy triumph for this MSME. The company realizes an annual savings of USD 12,000 per year due to the success of this project. Top Management Leadership, Data-Based Validation, Technical Know-how and Industrial Engineering Knowledge Base are identified as critical success factors (CSFs), while profitability and on-time delivery are the key performance indicators (KPIs) for the MSME. Eventually, the lessons learned and implications indicate that LSS competitiveness can be treated as quality management standards (QMS) and quality tools and techniques (QTT) to ensure competitive advantage, sustainable green practices and growth.

Research limitations/implications

Even though the findings and recommendations of this research are based on a single case study, it is worth noting that the case study is executed in a Tier III city along with novice users of LSS tools and techniques. This indicates the applicability of LSS in MSME and thus, the modality adopted can be further refined to suit the socio-cultural aspects of India.

Originality/value

This article illustrates the deployment of LSS from the perspective of novice users, to assist MSME and policymakers to reinforce competitiveness through LSS. Moreover, the government can initiate a scheme in line with LSS competitiveness to complement the existing schemes based on the findings of the case study.

COVID-19 outbreak: implications on healthcare operations

Purpose

The COVID-19 pandemic is considered a major disruptive event of this decade, raising unforeseen socio-economic implications worldwide. This novel virus has increased the influx of patients in hospitals, and healthcare organisations are facing unprecedented constraints in their operations to deal with increased demand and pressed capacity. Thus, this article evaluates the impact of the COVID-19 pandemic on healthcare systems' demand, resources and capacity and provides research directions.

Design/methodology/approach

This is a viewpoint article and uses timely information on healthcare operations from both scholars and managers, published by diverse sources during the COVID-19 outbreak.

Findings

The authors discuss the focus on “flattening the curve of infection” as a measure to protect healthcare, delay the impact of increased demand and reorientate healthcare supply chain practices. Furthermore, the authors evaluate the role of lean practices on managing demand and capacity and improving quality across healthcare operations and supply chain. Finally, the authors suggest research directions on modern operational issues that emerged during this pandemic, such as discussions around the sustainability of lean post-pandemic, “just in time” practices, inventory trade-offs and lack of organisational responsiveness during untenable events.

Originality/value

In this article, the authors provide a contemporary assessment of the implications of the COVID-19 pandemic on healthcare operations, underscoring main economic and operational elements that can be affected, such as unforeseen demand, resources and capacity shortage. Therefore, the authors assess that healthcare organisations, practitioners and governments have to anticipate operational and economic impacts and, ultimately, to reassess their plans to deal with such adverse events.

A health technology assessment between two pharmacological therapies through Six Sigma: the case study of bone cancer

Purpose

Head and neck cancers are multi-factorial diseases that can affect many sides of people's life and are due to a lot of risk factors. According to their characteristics, the treatment can be surgical, use of radiation or chemotherapy. The use of a surgical treatment can lead to surgical infections that are a main theme in medicine. At the University hospital of Naples “Federico II”, two antibiotics were employed to tackle the issue of the infections and they are compared in this paper to find which one implies the lowest length of hospital stay (LOS) and the reduction of infections.

Design/methodology/approach

The Six Sigma methodology and its problem-solving strategy DMAIC (define, measure, analyse, improve, control), already employed in the healthcare sector, were used as a tool of a health technology assessment between two drugs. In this paper the DMAIC roadmap is used to compare the Ceftriaxone (administered to a group of 48 patients) and the association of Cefazolin plus Clindamycin (administered to a group of 45 patients).

Findings

The results show that the LOS of patients treated with Ceftriaxone is lower than those who were treated with the association of Cefazolin plus Clindamycin, the difference is about 41%. Moreover, a lower number of complications and infections was found in patients who received Ceftriaxone. Finally, a greater number of antibiotic shifts was needed by patients treated with Cefazolin plus Clindamycin.

Research limitations/implications

While the paper enhances clearly the advantages for patients' outcomes regarding the LOS and the number of complications, it did not analyse the costs of the two antibiotics.

Practical implications

Employing the Ceftriaxone would allow the Department of Maxillofacial Surgery to obtain lower LOS and a limited number of complications/infections for recovered patients, consequently reducing the hospitalization costs.

Originality/value

There is a double value in this paper: first of all, the comparison between the two antibiotics gives an answer to one of the main issues in medicine that is the reduction of hospital-acquired infections; secondly, the Six Sigma through its DMAIC cycle can be employed also to compare two biomedical technologies as a tool of health technology assessment studies.

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.