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PurposeOne 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 costsDesign/methodology/approachLean 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).FindingsThe 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/valueThe 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.

Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision.

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 &lt, 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.

PurposeThe rise of the mean age incremented the occurrence of femur fractures with respect to the past, leading thus to serious consequences, as regards morbidity and socio-economic impact. The direction of the A.O.R.N. Cardarelli of Naples has introduced a DTAP whose aim was the reduction of LOS. The paper aims to discuss this issue.Design/methodology/approachThe aim of this paper is to analyze the introduction of DTAP, employing Lean Thinking and Six Sigma methodology based on the DMAIC cycle. To evaluate the effectiveness of DTAP, two groups of patients have been observed for 14 months (before and after the implementation of DTAP).FindingsStatistical tests were performed on the groups and graphics were provided to visualize the decrease of LOS (29.9 per cent). The overall population was also divided in subgroups according to six variables potentially influencing LOS.Research limitations/implicationsAuthors considered six variables of influences; yet, others could be taken into account in the future.Practical implicationsThe decrease of costs due to the management of elderly patients with femur fracture, the optimization of care processes in hospitals and a faster recovery for patients is the tangible contribute of DTAP.Originality/valueThe implementation of DTAP allowed the hospital to obtain a significant reduction of LOS with a consequently decrease of costs alleviating the hospital and the society from the socio-economic burden and the morbidity of this pathology.

Healthcare Associated Infection (HAI) is a major health problem in several departments of the hospital sector. These infections cause prolonged length of stay (LOS), complications, and increased hospital costs. In this paper, medical record data of 415 patients admitted to in the Adult and Neonatal Intensive Care Unit (ICU) where there was a high risk of contracting HAIs, were used collectively. The aim was to create models capable of predicting LOS, measured in days, considering preoperative clinical information. Multiple linear regression analysis and Machine Learning (ML) regression analysis were performed. Subsequently, the LOS was grouped by weeks and classified with the ML classification algorithms. Multiple linear regression was implemented with IBM SPSS, the coefficient of determination (R2) was equal to 0.343. A regression with ML algorithms is performed with the Knime analysis platform. The best R2 was obtained from the Random Forest (R2 =0.414) and Gradient Boosted Tree (R2 =0.382) algorithms. Regarding the classification analysis, the RF and Multi-Layer Perceptron algorithms showed accuracy respectively 49.398% and 46.988%, an error of 50.602% and 53.012%. The goal was to create a model to support physicians in evaluating the hospitalization of patients at risk of HAI in the ICU. (H. De Koning, 2006)

Tongue cancers are among the most frequent malignancies in the population and their influence can be affected by many risk factors. Patients undergoing tongue surgery face different complications and can experience a long length of hospital stay (LOS). The aim of this paper is to compare two pharmacological therapies in order to understand which one decreases the LOS. At the University hospital of Naples “Federico II” two antibiotics were employed: Cefazolin plus Clindamycin and Ceftriaxone. Six Sigma methodology was employed to analyse two group of patients treated with these two different antibiotics: 55 patients treated with the antibiotic Cefazolin plus Clindamycin and 66 patients with the antibiotic Ceftriaxone. This is the first time that this methodology is used in order to compare two antibiotics in the oncology field. The results obtained show clearly and with a statistical evidence that patients treated with Ceftriaxone experienced a lower LOS (−28.6% in terms of percentage between medians). Reducing the LOS for patients means limiting the number of complications and, therefore, reducing the hospitalization costs. It would be valuable for both hospital and patients: the former would save money that they could invest in other important care activities; the latter would experience a higher quality of care with fewer complications.

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.

Introduction. Maxillary and mandibular cancer represents the sites where cystic and neoplastic conditions, of a benign or malignant nature, can occur. In general, for patients undergoing surgery to remove cancer of the oral cavity, the administration of oral antibiotics, Ceftriaxone and Cefazolin plus Clindamycin, can affect the Length of Stay (LOS).

Publisher's version (útgefin grein), 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., The authors wish to thank Alec Shawn for his contribute as regards "grammar and spell check". This work has been realized thanks to the collaboration of the Department of Advanced Biomedical Sciences of the University Hospital "Federico II" of Naples. The authors wish to thank Sabrina De Vita, Francesca D'Agostino, Giuseppina Toscano and Tania Di Monda for their valuable contribution to the implementation of data mining algorithm during their MS thesis. The work has been partially carried out under TablHealth [CUP B49J17000720008] project and AK12 s.r.l.", "Peer Reviewed"