Zinonas Antoniou

Objective: Design, implement and evaluate eHealth4U, a prototype national electronic health record (EHR) system for Cyprus, so that the national eHealth law of Cyprus is operationalised into a citizen-centred service that remains interoperable with myHealth@EU and the International Patient Summary (IPS). Methods: The provisions of the Cyprus eHealth law were analysed and translated into functional and non-functional requirements that structured the system's architecture, services and user-facing workflows. eHealth4U was implemented as a cloud-based microservices platform with authentication and authorisation controls, and interoperability based on the Fast Healthcare Interoperability Resources of Health Level Seven, including the IPS. The system verification comprised more than 400 unit test cases. Usability was assessed through task-based testing and questionnaires with 30 medical doctors and 57 patients, using key performance indicators such as task success, satisfaction and observed error occurrence. Interoperability was assessed using two x-eHealth hackathons and three IPS Connectathons. Results: eHealth4U supports the IPS and myHealth@EU cross-border service requirements. Usability was rated 4.5/5, with a task success rate of 98% for doctors and 99% for patients, and an overall satisfaction of 95%. The observed error occurrence affected approximately 10% of doctors and 15% of patients at least once during task execution, whereas tasks were typically completed successfully after correction within the session. The IPS exchange was demonstrated with foreign partners in Connectathons, with successful participation documented through certificates issued by HL7 and Integrating the Healthcare Enterprise. Conclusion: eHealth4U provides a legally grounded, citizen-centred and interoperable national EHR prototype for Cyprus, combining an implementable architecture with demonstrated cross-border interoperability and positive early usability outcomes, thereby supporting the establishment of the Cyprus Health Data Space.

Artificial intelligence (AI) is increasingly applied in a wide range of healthcare and Intensive Care Unit (ICU) areas to serve—among others—as a tool for disease detection and prediction, as well as for healthcare resources’ management. Since sepsis is a high mortality and rapidly developing organ dysfunction disease afflicting millions in ICUs and costing huge amounts to treat, the area can benefit from the use of AI tools for early and informed diagnosis and antibiotic administration. Additionally, resource allocation plays a crucial role when patient flow is increased, and resources are limited. At the same time, sensitive data use raises the need for ethical guidelines and reflective datasets. Additionally, explainable AI is applied to handle AI opaqueness. This study aims to present existing clinical approaches for infection assessment in terms of scoring systems and diagnostic biomarkers, along with their limitations, and an extensive overview of AI applications in healthcare and ICUs in terms of (a) sepsis detection/prediction and sepsis mortality prediction, (b) length of ICU/hospital stay prediction, and (c) ICU admission/hospitalization prediction after Emergency Department admission, each constituting an important factor towards either prompt interventions and improved patient wellbeing or efficient resource management. Challenges of AI applications in ICU are addressed, along with useful recommendations to mitigate them. Explainable AI applications in ICU are described, and their value in validating, and translating predictions in the clinical setting is highlighted. The most important findings and future directions including multimodal data use and Transformer-based models are discussed. The goal is to make research in AI advances in ICU and particularly sepsis prediction more accessible and provide useful directions on future work.

This paper introduces a mobile framework designed to enhance citizen access to and sharing of health data, aiming to empower individuals with greater control over their personal health information. Accessing and sharing health-related data is essential in everyday scenarios, from routine doctor visits or viewing your health on your own to emergencies where swift access can save lives. It addresses the challenges posed by the fragmented nature of healthcare services and the barriers of language differences in patient records. The framework utilizes the EU eHealth Digital Service Infrastructure (eHDSI) OpenNCP for translating patient summaries and the FHIR Smart Health Links Protocol for secure sharing. A pilot study with 40 participants was conducted to assess the usability and effectiveness of the app, revealing a strong demand among citizens for such innovative health services.

Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision-making processes. By elucidating the reasoning behind AI-driven diagnoses and treatment recommendations, explainability can gain the trust of healthcare experts and assist them in difficult diagnostic tasks. Sepsis is characterized as a serious condition that happens when the immune system of the body has an extreme response to an infection, causing tissue and organ damage and leading to death. Physicians face challenges in diagnosing and treating sepsis due to its complex pathogenesis. This work aims to provide an overview of the recent studies that propose explainable AI models in the prediction of sepsis onset and sepsis mortality using intensive care data. The general findings showed that explainable AI can provide the most significant features guiding the decision-making process of the model. Future research will investigate explainability through argumentation theory using intensive care data focused on sepsis patients.

This paper presents a comprehensive mobile eHealth application that integrates teleconsultation functionality to deliver high-quality healthcare and telemedicine services. Patients can have virtual consultations with healthcare professionals from the convenience of their own homes providing real time telemedicine services. The application supports the patient medical history, schedule teleconsultations module based on the doctors’ time plan availability, but also the secure environment to exchange medical data such as, laboratory reports, and imaging results. The platform supports end-to-end encryption, and the secure data storage are key characteristics of the teleconsultation function protecting patient privacy and confidentiality. Moreover, the application has tools for managing electronic prescriptions, dispensation actions and patient summary information. For the patients who might find it challenging to get conventional in-person visits, such as those with mobility challenges or those who live in distant and remote places, the teleconsultation option has the potential to increase access to high quality healthcare services. Overall, the application offers a user-friendly platform for patient-centric teleconsultation, boosting patient-provider communication and healthcare service delivery, yet further evaluation of its effectiveness, usability, and security measures is necessary before widespread deployment can be recommended.

Increasing vaccination hesitancy that burdens global health and safety can be attributed to multiple reasons. Individuals’ social environment seems to be the catalyst for vaccination hesitancy perpetuation, thus it is important to examine the influence of different social network mechanisms in vaccination attitudes’ contagion. The proposed pilot experiment will examine the social contagion of childhood vaccination attitudes within a parental community using social network interventions. By identifying centrally-located people or groups of like-minded individuals from a parents’ community, we will examine whether the position of a person within a social group can have a greater impact in spreading positive vaccination messages to other community members. Parents will be recruited from social media and will be randomly assigned into three groups. Firstly, each group will participate in an online game to map their social networks and identify members with certain network position, who will then receive a short training about valid vaccination information provisions. All groups’ members will participate in daily vaccination discussion groups for one week, where the selected members will spread positive vaccination attitudes to others. We hypothesize that centrally located individuals and like-minded group of people will more likely cause a change on the childhood-vaccination attitudes and will sustain a long-term change at 3 months follow-up, compared to randomly located people.

The wider adoption of mobile Health video communication systems in standard clinical practice requires real-time control to provide for adequate levels of clinical video quality to support reliable diagnosis. The latter can only be achieved with real-time adaptation to time-varying wireless networks' state to guarantee clinically acceptable performance throughout the streaming session, while conforming to device capabilities for supporting real-time encoding. We propose an adaptive video encoding framework based on multi-objective optimization that jointly maximizes the encoded video's quality and encoding rate (in frames per second) while minimizing bitrate demands. For this purpose, we construct a dense encoding space and use linear regression to estimate forward prediction models for quality, bitrate, and computational complexity. The prediction models are then used in an adaptive control framework that can fine-tune video encoding based on real-time constraints. We validate the system using a leave-one-out algorithm applied to ten ultrasound videos of the common carotid artery. The prediction models can estimate structural similarity quality with a median accuracy error of less than 1%, bitrate demands with deviation error of 10% or less, and encoding frame rate within a 6% margin. Real-time adaptation at a group of pictures level is demonstrated using the high efficiency video coding standard. The effectiveness of the proposed framework compared to static, nonadaptive approaches is demonstrated for different modes of operation, achieving significant quality gains, bitrate demands reductions, and performance improvements, in real-life scenarios imposing time-varying constraints. Our approach is generic and should be applicable to other medical video modalities with different applications.

This study proposes a unifying framework for m-Health video communication systems that provides for the joint optimization of video quality, bitrate demands, and encoding time. The framework is video modality and infrastructure independent and facilitates adaptation to the best available encoding mode that satisfies underlying technology and application imposed constraints. The scalability of the proposed algorithm is demonstrated using different HEVC encoding configurations and realistic modelling of 802.11× wireless infrastructure for emergency scenery and response videos. Extensive experimentation shows that a jointly optimal solution in the encoding time, bitrate, and video quality space is feasible.

There is a huge need for open source software solutions in the healthcare domain, given the flexibility, interoperability and resource savings characteristics they offer. In this context, this paper presents the development of three open source libraries - Specific Enablers (SEs) for eHealth applications that were developed under the European project titled “Future Internet Social and Technological Alignment Research” (FI-STAR) funded under the “Future Internet Public Private Partnership” (FI-PPP) program. The three SEs developed under the Electronic Health Record Application Support Service Enablers (EHR-EN) correspond to: a) an Electronic Health Record enabler (EHR SE), b) a patient summary enabler based on the EU project “European patient Summary Open Source services” (epSOS SE) supporting patient mobility and the offering of interoperable services, and c) a Picture Archiving and Communications System (PACS) enabler (PACS SE) based on the dcm4che open source system for the support of medical imaging functionality. The EHR SE follows the HL7 Clinical Document Architecture (CDA) V2.0 and supports the Integrating the Healthcare Enterprise (IHE) profiles (recently awarded in Connectathon 2015). These three FI-STAR platform enablers are designed to facilitate the deployment of innovative applications and value added services in the health care sector. They can be downloaded from the FI-STAR cataloque website. Work in progress focuses in the validation and evaluation scenarios for the proving and demonstration of the usability, applicability and adaptability of the proposed enablers.