Giovanni Improta

Bio: Giovanni Improta is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 22, co-authored 92 publications receiving 1313 citations.

The application of Lean Six Sigma methodology to reduce the risk of healthcare-associated infections in surgery departments.

Abstract: Rationale Nowadays, the monitoring and prevention of healthcare–associated infections (HAIs) is a priority for the healthcare sector. Aims and objectives In this article, we report on the application of the Lean Six Sigma (LSS) methodology to reduce the number of patients affected by sentinel bacterial infections who are at risk of HAI. Methods The LSS methodology was applied in the general surgery department by using a multidisciplinary team of both physicians and academics. Data on more than 20 000 patients who underwent a wide range of surgical procedures between January 2011 and December 2014 were collected to conduct the study using the departmental information system. The most prevalent sentinel bacteria were determined among the infected patients. The preintervention (January 2011 to December 2012) and postintervention (January 2013 to December 2014) phases were compared to analyze the effects of the methodology implemented. The methodology allowed the identification of variables that influenced the risk of HAIs and the implementation of corrective actions to improve the care process, thereby reducing the percentage of infected patients. Results The improved process resulted in a 20% reduction in the average number of hospitalization days between preintervention and control phases, and a decrease in the mean (SD) number of days of hospitalization amounted to 36 (15.68), with a data distribution around 3 σ. The LSS is a helpful strategy that ensures a significant decrease in the number of HAIs in patients undergoing surgical interventions. The implementation of this intervention in the general surgery departments resulted in a significant reduction in both the number of hospitalization days and the number of patients affected by HAIs. Conclusions This approach, together with other tools for reducing the risk of infection (surveillance, epidemiological guidelines, and training of healthcare personnel), could be applied to redesign and improve a wide range of healthcare processes.

Improving performances of the knee replacement surgery process by applying DMAIC principles

Abstract: Rationale, Aims, and Objectives The work is a part of a project about the application of the Lean Six Sigma to improve health care processes. A previously published work regarding the hip replacement surgery has shown promising results. Here, we propose an application of the DMAIC (Define, Measure, Analyse, Improve, and Control) cycle to improve quality and reduce costs related to the prosthetic knee replacement surgery by decreasing patients' length of hospital stay (LOS) Methods The DMAIC cycle has been adopted to decrease the patients' LOS. The University Hospital “Federico II” of Naples, one of the most important university hospitals in Southern Italy, participated in this study. Data on 148 patients who underwent prosthetic knee replacement between 2010 and 2013 were used. Process mapping, statistical measures, brainstorming activities, and comparative analysis were performed to identify factors influencing LOS and improvement strategies. Results The study allowed the identification of variables influencing the prolongation of the LOS and the implementation of corrective actions to improve the process of care. The adopted actions reduced the LOS by 42%, from a mean value of 14.2 to 8.3 days (standard deviation also decreased from 5.2 to 2.3 days). Conclusions The DMAIC approach has proven to be a helpful strategy ensuring a significant decreasing of the LOS. Furthermore, through its implementation, a significant reduction of the average costs of hospital stay can be achieved. Such a versatile approach could be applied to improve a wide range of health care processes.

Lean thinking to improve emergency department throughput at AORN Cardarelli hospital

Abstract: Throughout the world, emergency departments (ED) are characterized by overcrowding and excessive waiting times. Furthermore, the related delays significantly increase patient mortality and make inefficient use of resources to the detriment of the satisfaction of employees and patients. In this work, lean thinking is applied to the ED of Cardarelli Hospital of Naples with the aim of increasing patient flow, improving the processes that contribute to facilitating the flow of patients through the various stages of medical treatment and eliminating all bottlenecks (queue) as well as all activities that generate waste. This project was performed at National Hospital A.O.R.N. A. Cardarelli of Naples. The historical times of access to the ED were analysed from January 2015 to June 2015, for a total of 16,563 records. Subsequently, starting in November 2015, corrective actions were implemented according to the Lean Approach. Data collected after the introduced improvements were collected from April 2016 to June 2016 and compared to those collected during the starting period. The results acquired before application of the Lean Thinking strategy illustrated the as-is process with its drawbacks. An analysis of the non-added value activities was performed to identify the procedures that need to be improved. After implementation of the corrective actions, we observed a positive increase in the performance of the ED, quantified as percentages of hospitalized patients according to triage codes and waiting times. This work demonstrates the applicability of Lean Thinking to ED processes and its effectiveness in terms of increasing the efficiency of services and reducing waste (waiting times).

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Death and dying

Abstract: Death is the new sex, last great taboo in Western society and Western medicine, as Richard Smith discusses in his editorial (p 129). In the trinity of births, marriages, and deaths, only death does not have glossy magazines devoted to stylish consumption at the attendant ceremonies. On the web, of course, …

The return of a forgotten polymer : Polycaprolactone in the 21st century

Abstract: During the resorbable-polymer-boom of the 1970s and 1980s, polycaprolactone (PCL) was used in the biomaterials field and a number of drug-delivery devices. Its popularity was soon superseded by faster resorbable polymers which had fewer perceived disadvantages associated with long term degradation (up to 3-4 years) and intracellular resorption pathways; consequently, PCL was almost forgotten for most of two decades. Recently, a resurgence of interest has propelled PCL back into the biomaterials-arena. The superior rheological and viscoelastic properties over many of its aliphatic polyester counterparts renders PCL easy to manufacture and manipulate into a large range of implants and devices. Coupled with relatively inexpensive production routes and FDA approval, this provides a promising platform for the production of longer-term degradable implants which may be manipulated physically, chemically and biologically to possess tailorable degradation kinetics to suit a specific anatomical site. This review will discuss the application of PCL as a biomaterial over the last two decades focusing on the advantages which have propagated its return into the spotlight with a particular focus on medical devices, drug delivery and tissue engineering.