Developing a Help Desk Service for Enhanced Coordination in Health Informatics Projects: A Sharepoint and Power Automate Approach.

Healthcare projects necessitate effective collaboration between clinical and technical partners, particularly during pivotal phases like lab testing and piloting. However, challenges in coordination often impede seamless collaboration, leading to inefficiencies and delays. This paper presents a comprehensive approach to developing a help desk service tailored for CAREPATH projects, leveraging SharePoint services and Power Automate. The solution aims to bridge communication gaps, foster collaboration, and enhance coordination among clinical and technical partners. Through iterative development and testing, we refined the system based on stakeholder feedback, resulting in streamlined workflows and improved document management. During the lab testing phase, the help desk system demonstrated significant improvements in resolution duration, communication efficiency, and success solution rates. Stakeholder feedback highlighted enhanced collaboration and improved access to project documentation. With successful testing, the help desk is poised for implementation in subsequent phases, promising further enhancements in patient engagement, technology integration, and scalability. These findings underscore the critical role of help desks in healthcare ICT projects, offering a transformative approach to project management and stakeholder collaboration. Future directions include enhancing patient engagement, leveraging advanced technologies, and conducting longitudinal studies to evaluate long-term impact. Embracing these directions will drive positive change, delivering better outcomes for patients and caregivers in healthcare ICT projects.

Deep Learning-Based Synthetic Skin Lesion Image Classification.

Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study proposed a modified VGG16-based algorithm to recognise AI-generated medical images. Initially, 10,000 synthetic medical skin lesion images were generated using a Generative Adversarial Network (GAN), providing a set of images for comparison to real images. Then, an enhanced VGG16-based algorithm has been developed to classify real images vs AI-generated images. Following hyperparameters tuning and training, the optimal approach can classify the images with 99.82% accuracy. Multiple other evaluations have been used to evaluate the efficacy of the proposed network. The complete dataset used in this study is available online to the research community for future research.

INSAFEDARE Project: Innovative Applications of Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support.

Digital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care systems. Techniques for generating synthetic data from real datasets are highly advanced and continually evolving. This paper aims to present the INSAFEDARE project's ambition regarding medical devices' regulation and how real and synthetic data can be used to check if devices are safe and effective. The project will consist of three pillars: a) assurance of new state-of-the-art technologies and approaches (such as synthetic data), which will support the validation methods as part of regulatory decision-making; b) technical and scientific, focusing on data-based safety assurance, as well as discovery, integration and use of datasets, and use of machine learning approaches; and c) delivery to practice, through co-production involving relevant stakeholders, dissemination and sustainability of the project's outputs. Finally, INSAFEDARE will develop an open syllabus and training certification for health professionals focused on quality assurance.

Advancing Healthcare Through Interoperability: Implementing Scalable Solutions for Patient Data Integration.

Healthcare faces significant challenges in exchanging and utilizing health information across diverse providers, necessitating innovative solutions for improved interoperability. This study presents a comprehensive exploration of scalable technical and semantic solutions for patient care integration, emphasizing the implementation of these solutions within the framework of the Fast Healthcare Interoperability Resources (FHIR) standard. Our approach revolves around the development and deployment of Technical Interoperability Suite (TIS) and Semantic Interoperability Suite (SIS) technology solutions to disparate health information systems, predominantly Electronic Health Records (EHRs) into a unified Patient Care Platform, fostering comprehensive data exchange and utilization. The integration process involves importing data from various EHR systems and transforming imported patient data into FHIR-standardized formats. The provided solution supports various functionalities, including automatic and manual importation of patient data, through standard computer-readable templates. The integration of TIS and SIS solutions is underpinned by a robust technological framework, incorporating technologies such as Typescript, Deno, and document-oriented databases such as MongoDB. The effectiveness of our interoperability solutions was validated through deployment in multinational EU projects: ADLIFE and CAREPATH. The scalability and generalizability of our approach underscore its potential for diverse healthcare settings.

Wettability and Bactericidal Properties of Bioinspired ZnO Nanopillar Surfaces.

Nanomaterials of zinc oxide (ZnO) exhibit antibacterial activities under ambient illumination that result in cell membrane permeability and disorganization, representing an important opportunity for health-related applications. However, the development of antibiofouling surfaces incorporating ZnO nanomaterials has remained limited. In this work, we fabricate superhydrophobic surfaces based on ZnO nanopillars. Water droplets on these superhydrophobic surfaces exhibit small contact angle hysteresis (within 2-3°) and a minimal tilting angle of 1°. Further, falling droplets bounce off when impacting the superhydrophobic ZnO surfaces with a range of Weber numbers (8-46), demonstrating that the surface facilitates a robust Cassie-Baxter wetting state. In addition, the antibiofouling efficacy of the surfaces has been established against model pathogenic Gram-positive bacteria Staphylococcus aureus (S. aureus) and Gram-negative bacteria Escherichia coli (E. coli). No viable colonies of E. coli were recoverable on the superhydrophobic surfaces of ZnO nanopillars incubated with cultured bacterial solutions for 18 h. Further, our tests demonstrate a substantial reduction in the quantity of S. aureus that attached to the superhydrophobic ZnO nanopillars. Thus, the superhydrophobic ZnO surfaces offer a viable design of antibiofouling materials that do not require additional UV illumination or antimicrobial agents.