Health technology assessment (HTA) of optoelectronic biosensors for oncology by analytic hierarchy process (AHP) and Likert scale.

BACKGROUND: The multicriteria decision method (MCDM) aims to find conflicts among alternatives by comparing and evaluating them according to various criteria to reach the best compromise solution. The evaluation of a new health technology is extremely important in the health sciences field. The aim of this work is to evaluate a new health technology to assay thyroglobulin in patients with differentiated thyroid cancer to improve its service from an organizational point of view, by planning new and appropriate training activities, ensuring proper use of resources and satisfying the needs of different users. METHODS: The evaluation was performed using two methodologies: the analytic hierarchy process (AHP) and the Likert scale. The AHP is a multicriteria decision approach that assigns a weight to each evaluation criterion according to the decision maker's pairwise comparisons of the criteria. The Likert scale is a psychometric scale employed to study the degree of user satisfaction by measuring opinions. RESULTS: Results show the need of particularly improving clinical efficiency, effectiveness, and return on sales (ROS) related to the technology; technological safety, human resources and other parameters do not need to be improved because of the high satisfaction results of the users. CONCLUSIONS: The application of both methods provided the necessary information to improve the quality of the service, allowing the decision maker to identify the most valuable service features and to improve these to ensure user satisfaction and to identify possible service improvements.

A Regressive Model to Study the Hospitalization for Laparoscopic Appendectomy: A Multicenter Study.

Appendicitis is a most common abdominal condition worldwide, and appendectomy especially laparoscopic appendectomy is among the most commonly performed general surgeries. In this study, data were collected from patients who underwent laparoscopic appendectomy surgery at the Evangelical Hospital "Betania" in Naples, Italy. Linear multiple regression was used to obtain a simple predictor that can also assess which of the independent variables considered to be a risk factor. The model with R2 of 0.699 shows that comorbidities and complications during surgery are the main risk factors for prolonged LOS. This result is validated by other studies conducted in the same area.

Effect of Clinical and Demographic Variables on the Hospital Stay of Patients Undergoing Total Knee Arthroplasty.

The knee is the joint most affected by osteoarthritis and in its severe form can significantly affect people's physical and functional abilities. The increased demand for surgery leads to greater attention by health care management to be able to keep costs down. A major expense item for this procedure is Length of Stay (LOS). In this study, several Machine Learning algorithms were tested in order to construct not only a valid predictor of LOS but also to know among the selected variables the main risk factors. To do so, activity data from the Evangelical Hospital "Betania" in Naples, Italy, from 2019-2020 were used. Among the algorithms, the best are the classification algorithms with accuracy values exceeding 90%. Finally, the results are in line with those shown by two other comparison hospitals in the area.

The Classification Algorithms Applied to the Inpatient Stay for Lower Limb Surgery.

The revolutions of recent years in health care have involved several areas ranging from patient treatment to resource management. Therefore, several strategies have been put in place to increase patient value while trying to reduce spending. Several indicators have arisen to evaluate the performance of healthcare processes. The main one is Length of Stay (LOS). In this study, classification algorithms were used to predict the LOS of patients undergoing lower extremity surgery, an increasingly common condition given the progressive aging of the population. The context is the Evangelical Hospital "Betania" in Naples (Italy) in 2019-2020, which augments a multicenter study conducted by the same research team on several hospitals in southern Italy. All selected algorithms show an Accuracy above 90% but among them, the best is Logistic Regression with a value reaching 94%.

Use of the AHP methodology in system dynamics: Modelling and simulation for health technology assessments to determine the correct prosthesis choice for hernia diseases.

Health technology assessments (HTAs) are often difficult to conduct because of the decisive procedures of the HTA algorithm, which are often complex and not easy to apply. Thus, their use is not always convenient or possible for the assessment of technical requests requiring a multidisciplinary approach. This paper aims to address this issue through a multi-criteria analysis focusing on the analytic hierarchy process (AHP). This methodology allows the decision maker to analyse and evaluate different alternatives and monitor their impact on different actors during the decision-making process. However, the multi-criteria analysis is implemented through a simulation model to overcome the limitations of the AHP methodology. Simulations help decision-makers to make an appropriate decision and avoid unnecessary and costly attempts. Finally, a decision problem regarding the evaluation of two health technologies, namely, the evaluation of two biological prostheses for incisional infected hernias, will be analysed to assess the effectiveness of the model.

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

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.