User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner

Background: The rigorous elicitation of user needs is a crucial step for both medical device design and purchasing. However, user needs elicitation is often based on qualitative methods whose findings can be difficult to integrate into medical decision-making. This paper describes the application of AHP to elicit user needs for a new CT scanner for use in a public hospital. Methods: AHP was used to design a hierarchy of 12 needs for a new CT scanner, grouped into 4 homogenous categories, and to prepare a paper questionnaire to investigate the relative priorities of these. The questionnaire was completed by 5 senior clinicians working in a variety of clinical specialisations and departments in the same Italian public hospital. Results: Although safety and performance were considered the most important issues, user needs changed according to clinical scenario. For elective surgery, the five most important needs were: spatial resolution, processing software, radiation dose, patient monitoring, and contrast medium. For emergency, the top five most important needs were: patient monitoring, radiation dose, contrast medium control, speed run, spatial resolution. Conclusions: AHP effectively supported user need elicitation, helping to develop an analytic and intelligible framework of decision-making. User needs varied according to working scenario (elective versus emergency medicine) more than clinical specialization. This method should be considered by practitioners involved in decisions about new medical technology, whether that be during device design or before deciding whether to allocate budgets for new medical devices according to clinical functions or according to hospital department

Bioelectronic technologies and artificial intelligence for medical diagnosis and healthcare

The application of electronic findings to biology and medicine has significantly impacted health and wellbeing. Recent technology advances have allowed the development of new systems that can provide diagnostic information on portable point-of-devices or smartphones. The decreasing size of electronics technologies down to the atomic scale and the advances in system, cell, and molecular biology have the potential to increase the quality and reduce the costs of healthcare. Clinicians have pervasive access to new data from complex sensors; imaging tools; and a multitude of other sources, including personal health e-records and smart environments. Humans are from being able to process this unprecedented volume of available data without advanced tools. Artificial intelligence (AI) can help clinicians to identify patterns from this huge amount of data to inform better choices for patients. In this Special Issue, some original research papers focusing on recent advances have been collected, covering novel theories, innovative methods, and meaningful applications that could potentially lead to significant advances in the field

Big Data in Critical Infrastructures Security Monitoring: Challenges and Opportunities

Critical Infrastructures (CIs), such as smart power grids, transport systems, and financial infrastructures, are more and more vulnerable to cyber threats, due to the adoption of commodity computing facilities. Despite the use of several monitoring tools, recent attacks have proven that current defensive mechanisms for CIs are not effective enough against most advanced threats. In this paper we explore the idea of a framework leveraging multiple data sources to improve protection capabilities of CIs. Challenges and opportunities are discussed along three main research directions: i) use of distinct and heterogeneous data sources, ii) monitoring with adaptive granularity, and iii) attack modeling and runtime combination of multiple data analysis techniques.Comment: EDCC-2014, BIG4CIP-201

Pupillometry via smartphone for low-resource settings

The photopupillary reflex regulates the pupil reaction to changing light conditions. Being controlled by the autonomic nervous system, it is a proxy for brain trauma and for the conditions of patients in critical care. A prompt evaluation of brain traumas can save lives. With a simple penlight, skilled clinicians can do that, whereas less specialized ones have to resort to a digital pupilometer. However, many low-income countries lack both specialized clinicians and digital pupilometers. This paper presents the early results of our study aiming at designing, prototyping and validating an app for testing the photopupillary reflex via Android, following the European Medical Device Regulation and relevant standards. After a manual validation, the prototype underwent a technical validation against a commercial Infrared pupilometer. As a result, the proposed app performed as well as the manual measurements and better than the commercial solution, with lower errors, higher and significant correlations, and significantly better Bland-Altman plots for all the pupillometry-related measures. The design of this medical device was performed based on our expertise in low-resource settings. This kind of environments imposes more stringent design criteria due to contextual challenges, including the lack of specialized clinicians, funds, spare parts and consumables, poor maintenance, and harsh environmental conditions, which may hinder the safe operationalization of medical devices. This paper provides an overview of how these unique contextual characteristics are cascaded into the design of an app in order to contribute to the Sustainable Development Goal 3 of the World Health Organization: Good health and well-being

A MATLAB app to assess, compare and validate new methods against their benchmarks

Emerging technologies for physiological signals and data collection enable the monitoring of patient health and well-being in real-life settings. This requires novel methods and tools to compare the validity of this kind of information with that acquired in controlled environments using more costly and sophisticated technologies. In this paper, we describe a method and a MATLAB tool that relies on a standard sequence of statistical tests to compare features obtained using novel techniques with those acquired by means of benchmark procedures. After introducing the key steps of the proposed statistical analysis method, this paper describes its implementation in a MATLAB app, developed to support researchers in testing the extent to which a set of features, captured with a new methodology, can be considered a valid surrogate of that acquired employing gold standard techniques. An example of the application of the tool is provided in order to validate the method and illustrate the graphical user interface (GUI). The app development in MATLAB aims to improve its accessibility, foster its rapid adoption among the scientific community and its scalability into wider MATLAB tools

Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis

With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle

Interactive management control via analytic hierarchy process (AHP). An empirical study in a public university hospital.

Management control in public university hospitals is a challenging task because of continuous changes due to external pressures (e.g. economic pressures, stakeholder focuses and scientific progress) and internal complexities (top management turnover, shared leadership, technological evolution, and researcher oriented mission). Interactive budgeting contributed to improving vertical and horizontal communication between hospital and stakeholders and between different organizational levels. This paper describes an application of Analytic Hierarchy Process (AHP) to enhance interactive budgeting in one of the biggest public university hospital in Italy. AHP improved budget allocation facilitating elicitation and formalization of units’ needs. Furthermore, AHP facilitated vertical communication among manager and stakeholders, as it allowed multilevel hierarchical representation of hospital needs, and horizontal communication among staff of the same hospital, as it allowed units’ need prioritization and standardization, with a scientific multi-criteria approach, without using complex mathematics. Finally, AHP allowed traceability of a complex decision making processes (as budget allocation), this aspect being of paramount importance in public sectors, where managers are called to respond to many different stakeholders about their choices

Non-equilibrium Green's functions in density functional tight binding: method and applications

We present a detailed description of the implementation of the non-equilibrium Green's function (NEGF) technique on the density-functional-based tight-binding (gDFTB) simulation tool. This approach can be used to compute electronic transport in organic and inorganic molecular-scale devices. The DFTB tight-binding formulation gives an efficient computational tool that is able to handle a large number of atoms. NEGFs are used to compute the electronic density self-consistently with the open-boundary conditions naturally encountered in quantum transport problems and the boundary conditions imposed by the potentials at the contacts. The efficient block-iterative algorithm used to compute the Green's functions is illustrated. The Hartree potential of the density-functional Hamiltonian is obtained by solving the three-dimensional Poisson equation. A scheme to treat geometrically complex boundary conditions is discussed, including the possibility of including multiterminal calculations