Developing an Open-Source, User-Friendly, OWASP-Compliant Architecture for Healthcare Web Application Testing.

Nowadays, web applications are fundamental in the healthcare sector. However, with the widespread use of this technology, risks related to cybersecurity attacks also increase. To mitigate this phenomenon, every 3-4 years, the nonprofit foundation Open Worldwide Application Security Project (OWASP) compiles a top 10 ranking of the most critical web application security risks. Along with the top 10 Web Application Security Risks, OWASP also provides the Web Security Testing Guide, which offers comprehensive guidelines for conducting security tests. This guide includes suggestions for specific tools to use when performing different tests, among other valuable insights. However, the use of these recommended tools can be costly and can require advanced technical skills and a deep understanding of security best practices and web technologies. In addition, since the OWASP work on web security is generic, it would be useful to restrict and adapt it to the healthcare area. This would help in reducing the overhead when dealing with the needed tools. The goal of this study is to make web application security assessment in healthcare more accessible by developing tools that simplify the process and makes it user- friendly. Before developing such tools, an in-depth feasibility study must be conducted to verify the existence of open-source libraries to carry out the necessary testing procedures. It will be also necessary to identify how tools could be simplified and enhanced when focusing on healthcare.

Optimizing Integration with the Italian EHR: The Use of LOINC Codes.

According to the regulation "Decreto del Presidente del Consiglio dei Ministri" (DPCM) of September 29, 2015, n.178, the Logical Observation Identifiers Names and Codes (LOINC) system is included among the coding systems adopted in the Italian Electronic Health Record (EHR). As part of the Digital Health Solutions in Community Medicine (DHEAL-COM) project, one key goal is to categorize parameters using international classification systems. This enables the identification of appropriate Information and Communication Technology (ICT) solutions tailored to support people's health needs. Our objective is to incorporate LOINC codes for parameter categorization, thus anticipating the future use of EHR.

Transforming Ontology Web Language Elements into Common Terminology Service 2 Terminology Resources.

Communication and cooperation are fundamental for the correct deployment of P5 medicine, and this can be achieved only by correct comprehension of semantics so that it can aspire to medical knowledge sharing. There is a hierarchy in the operations that need to be performed to achieve this goal that brings to the forefront the complete understanding of the real-world business system by domain experts using Domain Ontologies, and only in the last instance acknowledges the specific transformation at the pure information and communication technology level. A specific feature that should be maintained during such types of transformations is versioning that aims to record the evolution of meanings in time as well as the management of their historical evolution. The main tool used to represent ontology in computing environments is the Ontology Web Language (OWL), but it was not created for managing the evolution of meanings in time. Therefore, we tried, in this paper, to find a way to use the specific features of Common Terminology Service-Release 2 (CTS2) to perform consistent and validated transformations of ontologies written in OWL. The specific use case managed in the paper is the Alzheimer's Disease Ontology (ADO). We were able to consider all of the elements of ADO and map them with CTS2 terminological resources, except for a subset of elements such as the equivalent class derived from restrictions on other classes.

Tuberculosis Infection Control: Experiences and Considerations from a Web Based Tool Implementation.

Tuberculosis (TB) remains a significant global health challenge. Indeed, according to the World Health Organization (WHO), TB is classified as the second most common cause of death worldwide due to a single infectious agent in 2022, following COVID-19. To effectively manage tuberculosis patients, it is necessary to ensure accurate diagnosis, prompt treatment initiation, and vigilant monitoring of patients' progress. In 2017, the TB Ge network was implemented and launched in two primary hospitals within the Liguria Region in Italy, with the main purpose to manage tuberculosis infections. This system, organized as a web-based tool, simplifies the manual input of patient's data and therapies, while automating the integration of test results from hospitals' Laboratory Information Systems (LIS), without requiring human intervention. The goal of this paper is to highlight the outcomes achieved through the implementation of the TB Ge network in a period seriously affected by the COVID-19 pandemia and outline future directions. More specifically, the aim is to extend its adoption to all hospitals in the Liguria Region, thus improving the management of tuberculosis infections across healthcare facilities.

Artificial intelligence and prescription of antibiotic therapy: present and future.

INTRODUCTION: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription. AREAS COVERED: In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024. EXPERT OPINION: Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

Standardization Proposal for the Transmission of Waiting List Data in Italy.

Each Italian region is required to manage and disclose data relating to waiting times for healthcare services which are provided by both public and private hospitals and local health units accredited to the Sistema Sanitario Nazionale (SSN - in English, National Healthcare System). The current law governing data relating to waiting times and their sharing is the Piano Nazionale di Governo delle Liste di Attesa (PNGLA - in English National Government Plan for Waiting Lists). However, this plan does not propose a standard to monitor such data, but only provides a few guidelines that the Italian regions are required to follow. The lack of a specific technical standard for managing sharing of waiting list data and the lack of precise and binding information in the PNGLA make the management and transmission of such data problematic, reducing the interoperability necessary to have an effective and efficient monitoring of the phenomenon. The proposal for a new standard for the transmission of waiting list data derives from these shortcomings. This proposed standard promotes greater interoperability, is easy to create with an implementation guide, and has sufficient degrees of freedom to assist the document author.