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SummaryBackground: A gap exists between evidence-based medicine and clinical-practice. Every day, healthcare professionals (HCPs) combine empirical evidence and subjective experience in order to maximize the effectiveness of interventions. Consequently, it is important to understand how HCPs interpret the research evidence and apply it in everyday practice. We focused on the prevention of falls, a common cause of injury-related morbidity and mortality in later life, for which there is a wide range of known risk factors.Objectives: To use the Analytic Hierarchy Process (AHP) to investigate the opinions of HCPs in prioritizing risk factors for preventing falls.Methods: We used the AHP to develop a hierarchy of risk factors for falls based on the knowledge and experience of experts. We submitted electronic questionnaires via the web, in order to reach a wider number of respondents. With a web service, we pooled the results and weighted the coherence and the experience of respondents.Results: Overall, 232 respondents participated in the study: 32 in the technical pilot study, nine in the scientific pilot study and 191 respondents in the main study. We identified a hierarchy of 35 risk factors, organized in two categories and six sub-categories.Conclusions: The hierarchy of risk factors provides further insights into clinicians’ perceptions of risk factors for falls. This hierarchy helps understand the relative importance that clinicians place on risk factors for falls in older people and why evidence-based guidelines are not always followed. This information may be helpful in improving intervention programs and in understanding how clinicians prioritize multiple risk factors in individual patients. The AHP method allows the opinions of HCPs to be investigated, giving appropriate weight to their coherence, background and experience.

There is currently an international trend to involve computer and Internet in medical record. Electronic Medical Record (EMR) hold great promise for improving quality of patient care, but there are many limits to EMR adoption. We propose a technology that would overcome this limits, a single instrument that include the drug trolley and workstation computer. To asses the benefit of the introduction of this technology we propose an evaluation based on a scientific method, Analytic Hierarchy Process (AHP).

In this paper we have investigated the differences of Heart Rate Variability (HRV) features between normal subjects and patients suffering from Congestive Heart Failure (CHF) at several levels of NYHA scale. We analyzed 1914.4 hours of ECG of 83 patients of which 54 normal and 29 suffering from CHF with NYHA I, II, III, extracted by public databases. Following international guidelines, we computed time and frequency features of HRV. The measured features have been statistically analyzed per each NHYA class. Finally, features correlation matrix for each class has been studied. The results suggest that differences exist between HRV features of normal subjects and patients suffering from CHF. These differences seems to be related to the severity of the pathology. Finally, we observe that also the correlation matrix changes according to the severity of NYHA.

Health Technology Assessment (HTA) is a multidimensional and multidisciplinary methodology, whose aim is to prioritize different concurrent technologies in order to support decision making in healthcare systems. A crucial point in any medical decision is to prioritize clinical, economical and patients’ needs on which decision makers base their choices. AHP can improve HTA in this critical task. In this paper, we applied HTA to assess the effectiveness and the efficacy of Home Monitoring (HM) as a model of continuity of care for patients suffering from congestive heart failure (CHF). We compare HM to other models of care: ambulatory follow-up (benchmark) and Disease Management Programs (DMP). The results lead to conclude that HM is more effective and efficient if compared to the benchmark, but seems to be not cost-effective if compared to DMPs. This conclusion changes when pervasive remote processing is applied to the monitored parameters with the aim to precociously predict critical events. In this case HM seems to be more effective and efficient than the other models of continuity of care. AHP contributed consistently in achieving these conclusions.

In this paper we analyze the conditions for the preservation of the CE mark of a medical device further to maintenance process, especially in the case in which the maintenance is managed by a third company. Analyzing the European Directives on medical devices, we observed that the conditions, in which the manufacturer obtained the CE mark, might change when another company manages maintenance. We propose three solutions to face this situation.

In this paper we describe an ICT solution which allows patients not skilled with web technology to access a web service for clinical surveillance and advanced processing of biomedical signals. Today this is important because of increase in the number of patients suffering from chronic diseases, who need more than other home-monitoring and usually are not skilled with web technology. The solution uses an Interactive Voice Response (IVR) in order to provide the elderly with an audio user interface for access complex service via web.

The method of HTA is applied to the decisional process for a Service Contract. The standard method of HTA is improved with standard techniques of graphical representation. The goal is to facilitate the communication with the decision maker which could be not strongly skilled in mathematical methods. As case study we consider the needs of a public hospital of medium dimensions, allocated in a medium town of north Italy, which need to purchase a Service Contract for a CT Equipment.

The method of Health Technology Assessment (HTA) has been applied to assess the effectiveness and the efficacy of Home Monitoring (HM) as model of continuity of care for patient suffering from congestive heart failure (CHF). We compare HM to other model of care: ambulatory follow-up (benchmark) and Disease Management Programs (DMP). The results lead to conclude that HM is more effective and efficient if compared to the benchmark, but seems to be not costeffective if compared to DMPs. This conclusion changes when pervasive remote processing is applied to the monitored parameters with the aim to precociously predict critical events. In this case HM seems to be more effective and efficient than the other models of continuity of care.

This article describes a multi-scale methodology for Health Technology Assessment. Multi-scale means that this method focuses the attention on the needs to be satisfied taken as a whole, by categories and one by one. For this reason a graphic representation has been used to identify the strengths of every technology to be compared. Than an objective function has been established in order to individuate the technology that better fill the needs as whole and by category. In order to establish a scale of satisfaction, it was necessary to define an algorithm that could distinguish parameters in objectivequantitative and subjective-qualitative. These last items have been quantified by auditing an un-polarized sample of experts trough questionnaires. Finally the chosen method was applied to neurosurgery, especially to spine surgery. Two different surgical techniques have been evaluated for different pathologies, each method applying different biocompatible elements: the traditional treatment, which employs rigid fixers (Cage and Titanium bars) and a new procedure, which employs elastic fixers with shape memory (Somafix and Nitinol bars).

Accidental falls are a major problem in later life, reducing the overall well-being, independence, mobility and quality of life of the elderly and those who care for them. The social and economic burden of falls is becoming unsustainable, considering the huge direct and indirect costs associated with each fall and the progressive ageing of the population in industrialised countries. Falls are related to complex and dynamic interactions between intrinsic, subject-specific, and extrinsic, circumstance-related, risk factors. Therefore, the prevention and management of falls in later life still represents a critical challenge for active and healthy ageing. Many healthcare technologies have been proposed to address this in recent years, ranging from fall risk screening and assessment to fall detection and, in more recent studies, fall prediction. However, several critical issues remain, mainly regarding the effectiveness of the proposed solutions, their usability in real environments, sustainability, performance, assessment and technological limitations. The lack of specificity in fall risk assessment, a limited effectiveness in fall prediction and the inability to reliably monitor spontaneous falls in real life conditions with un-obstructive technological solutions remain key unsolved problems. This special issue collects a selection of works across these healthcare technologies giving a snapshot of the latest research in this area. The first paper is an invited literature review on automatic methods for the assessment of fall risk based on statistical machine learning approaches applied to features extracted from wearable accelerometers and/or gyroscopes. This study identified five major recurrent limits identified among the 30 studies included in this review, which the reader of this special issue may consider while designing future studies: publication bias, limited/inadequate sample size, poor model validation, inappropriate model complexity with respect to the available data and poor outcome definition. The second paper in this issue reports a collaborative research effort between Italy, UK and Croatia, investigating the associations between depressed heart rate variability (HRV) and the risk of falling in patients suffering from hypertension. This cross section study enrolled 170 patients over two years, concluding that there is a significant association between depressed HRV and risk of falling, suggesting that it is possible to automatically detect autonomous nervous system states that may lead to falls. The third paper investigates the impact of various parameters on the accuracy vs. energy efficiency of a wearable activity recognition system, based on accelerometers. This study proved that sampling frequency, transmission rate, and method of nodal processing can impact significantly in satisfying the requirement of an activity recognition system. The final paper proposes a framework for fall detection based on wearable accelerometric sensors and machine learning techniques. The paper offers an interesting perspective and reports promising results, although preliminarily tested on a small sample of simulated falls. We hope that you will enjoy reading this special issue, in which some of the challenges of healthcare technologies for fall prediction, detection and risk assessment are examined through a range of different applications.

This paper aimed to assess the early effectiveness of a disease management program (DMP), called “Dimissione Protetta Difficile” (Di.Pro.Di) conducted by personnel from the intensive care unit (ICU) of Public Hospital S. Paolo, Naples, Italy. Di.Pro.Di significantly reduces the number and risk of rehospitalizations and total hospital length of stay.

This paper proposes a systematic literature review on ethics and CoviD-19, aiming to understand the impact and the perception of the pandemic during the first wave (January-June 2020) and the consequences one year later. PubMed was systematically searched up May 2020 to identify studies that took into consideration various ethical issues that have been arising from the Covid-19 outbreak. The eligibility of the papers was determined by two authors, who screened the results mediated by a third author. In order to facilitate the screening, the titles were divided into five sub-thematic macro-areas, namely allocation, policy, specialist, clinical trials, and technology and, when possible, per geographical area. Specifically, a posteriori, we decided to focus on the papers referring to policies and technology, as they highlighted ethical issues that are not overused and worthy of particular attention. Thus, 38 studies out of 233 met our inclusion criteria and were fully analysed. Accordingly, this review touches on themes such as fairness, equity, transparency of information, the duty of care, racial disparities, the marginalisation of the poor, and privacy and ethical concerns. Overall, it was found that despite the increased awareness of interdisciplinarity and the essential reference to ethics, many scientific articles use it with little competence, considering it only a "humanitarian" enrichment. In fact, as we understand, reflecting a year after the outbreak of the pandemic, although Covid-19 is leading scientists to increasingly recognise the importance of ethical issues, there is still a lot of confusion that could be helped by establishing international guidelines to act as a moral compass in times of crisis.

Falls occur frequently among older people and represent the most common cause of injury-related morbidity and mortality in later life. Preventing falls is an important way to reduce injuries, hospitalizations, and injury-related morbidity and mortality among older people. The research literature has identified hundreds of risk factors for falls among elderly people. Prioritizing risk factors for falls is useful for designing effective and efficacious prevention programs. The aim of this study was to use the Analytic Hierarchy Process to develop a hierarchy of risk factors for falls based on the knowledge and experience of experts working in this field. We designed and developed a web portal for participants to submit responses to electronic questionnaires in order to reach the highest number of respondents quickly and to reduce errors in responding. We contacted the person responsible for the Falls sections of four scientific societies. Finally, we propose a correction method to modify respondents’ relative importance on based on the coherence of their responses, in order not to exclude experts who had submitted the questionnaire twice.http://dx.doi.org/10.13033/ijahp.v2i2.61

In the last few years, the enormous growth in electronic equipments which communicate a radiofrequency have only emphasized the problem of so-called electromagnetic smog. At the same time, the continuous attempts to improve the quality of life of the patient (because of the research of new technology) has permitted the emergence of devices to control and cure the health conditions especially for the electrical problems of the heart: pacemakers. The main conflict between these two evolutionary processes is the interference of the elements with the pacemaker. Pacemakers are composed of electronic circuits and can sense and elaborate the electrical signals from the heart, as well as conduct electricity into it. Therefore, they are very sensitive to electromagnetic interaction. The RFID (Radio Frequency Identification) is the most important example of using radio frequency signals to exchange different types of data at a distance. The aim of this paper is to create a setup and protocol for the measurements of the interactions between pacemaker and RFID technology. An electrical model has been realized to simulate the way in which the pacemaker regulates the cardiological activity. In addition, we created a test protocol to evaluate the interaction between them.

The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.