Ebook: pHealth 2024

The pHealth 2024 conference is the 20th in a series of scientific events which bring together expertise from medical, technological, political, administrative, and social domains, and even from philosophy and linguistics. It opens a new chapter in the success story of this series of international conferences on wearable or implantable micro and nano technologies for personalised medicine.

Started in 2003 as a dissemination activity in the framework of a European project on wearable micro and nano technologies for personalised health with personal health management systems, pHealth conferences have evolved to become truly interdisciplinary and global events. All aspects of pHealth are comprehensively represented in the conference series, which also covers technological and biomedical facilities, legal, ethical, social, and organisational requirements and impacts, as well as the basic research necessary to enable future-proof care paradigms. It has advanced from P medicine (personalised medicine) through P2 medicine (also addressing prevention), P3 medicine (including prediction), P4 medicine (the patient is included as an active participant in the process), up to the current P5 medicine: personalised, participative, preventive, predictive, precision medicine. In that context, the conference series attracts experts from all over the world and from many scientific domains, including mathematics, data sciences, system sciences, philosophy, ethics and social sciences, as well as developers and practitioners from various technologies, medical and health disciplines, legal affairs, politics, and administration. The 2024 conference brought together health-service vendor and provider institutions, payer organisations, government departments, academic institutions and professional bodies, as well as patients and citizen representatives.

Smart mobile systems such as microsystems, smart textiles, smart implants, sensor-controlled medical devices, and innovative sensor and actuator principles and techniques, as well as related body, local and wide-area networks up to cloud services, are part of the next-generation of health services, and have become important enablers for telemedicine and ubiquitous pervasive health, , while social media and gamification have added another dimension to pHealth as an eco-system.

The OECD has defined four basic areas which must be managed in the new care model: addressing the big data challenges; fostering meaningful innovation; understanding and addressing potential new risks; and supporting a concerted effort to un-silo communities for a virtual care future. The benefits of pHealth technologies – including artificial intelligence, learning systems and intelligent robots – offer enormous potential for all stakeholder communities, including patients, citizens, health professionals, politicians, healthcare establishments, and companies from biomedical technology, pharmaceutical, and telecommunications domains, not only for the improvement of medical quality and industrial competitiveness, but also for managing healthcare costs and, last but not least, for improving patient experiences.

The pHealth 2024 conference benefits from the experience and lessons learned by the organising committees of previous pHealth events, particularly 2009 in Oslo, 2010 in Berlin, 2011 in Lyon, 2012 in Porto, 2013 in Tallinn, 2014 in Vienna, 2015 in Västerås, 2016 in Heraklion, 2017 in Eindhoven, 2018 in Gjøvik, 2019 in Genoa, 2020 in Prague, 2021, again in Genoa, and 2022, again in Oslo. The 2009 conference introduced the idea of special sessions focused on a particular topic and organised by a mentor or moderator. The Berlin event in 2010 initiated pre-conference workshops on particular topics prior to the main event. Lyon, in 2011, launched so-called dynamic demonstrations, allowing participants to show software and hardware solutions on the fly without the need for a booth. Implementing pre-conference events, pHealth 2012 in Porto gave attendees a platform for presenting and discussing recent developments and provocative ideas, which helped to animate the sessions. The highlights of pHealth 2013 in Tallinn were a special session on the success stories of European projects and the presentations on the newest paradigm changes and challenges associated with Big Data, analytics, translational and nano medicine. Vienna in 2014 focused on lessons learned from national and international R&D activities and practical solutions, particularly those from Horizon 2020, the new EU Framework Programme for Research and Innovation. In addition to reports about technology transfer support and building ecosystems and value chains to ensure better time to market and higher impact of knowledge-based technologies, the acceptability of solutions, especially with regard to security and privacy aspects, were presented and discussed in depth. pHealth 2015, in Västerås, addressed mobile technologies, knowledge-driven applications and computer-assisted decision support, as well as apps designed to support the elderly and chronic patients in their daily, and possibly independent, living. The fundamental scientific and methodological challenges of adaptive, autonomous, and intelligent pHealth approaches, the new role of patients as consumers and an active party with growing autonomy and related responsibilities, as well as requirements and solutions for mHealth in low- and medium-income countries were also considered. The pHealth 2016 conference in Heraklion was aimed at the integration of biological and medical data and the deployment of mobile technologies through the development of micro-nano-bio smart systems. The emphasis was on personalised health, virtual care, precision medicine, big bio-data management and analytics. The 2017 pHealth event in Eindhoven provided an inventory of former conferences, summarising requirements and solutions for pHealth systems, highlighting the importance of trust and the new focus on behavioural aspects in the design and use of pHealth systems. One specific aspect addressed was the need for flexible, adaptive and knowledge-based systems, as well as decision intelligence. pHealth 2018 in Gjøvik established national and European satellite workshops, complementing the more theoretical nature of the majority of papers with organisational and practical experiences. Borrowing from the good experiences of former events, pHealth 2018 responded to national and regional needs to advance healthcare systems and their services to citizens and health professionals. pHealth 2019, in Genoa, put a special emphasis on artificial intelligence (AI) and machine learning (ML) and their deployment for decision support, and ethical challenges and related international manifests were discussed in depth in that context. pHealth 2020, organised in Prague as virtual event, addressed AI and robots, bio-data management and analytics for health and social care, security, privacy and safety challenges, integrated care, and also the intelligent management of specific diseases including the Covid-19 pandemic. pHealth 2021, in Genoa, once again organised as a virtual event, focused on digital health ecosystems in transformation. Topics considered included the deployment of mobile technologies, micro-nano-bio smart systems, bio-data management and analytics, autonomous and intelligent systems, as well as the Health Internet of Things (HIoT) for personalised health, systems medicine, public health and virtual care. pHealth2022, in Oslo combined the former organisational schemes in a hybrid event. pHealth 2022 focused on personalised, preventive, predictive, participative precision (P5) medicine and the integration and interoperability between health informatics standards, and also on practical experiences with the deployment of HL7 FHIR. The conference also addressed new potential risks for security and privacy, as well as safety opportunities and challenges, trustworthiness of partners and processes, and the motivation and empowerment of patients in the care processes.

The pHealth2024 conference has been organised under the patronage of the Italian Scientific Society of Biomedical Informatics (SIBIM) and of the Department of Informatic, Modelling, Electronic and System Engineering of the University of Calabria. Following a long-standing tradition, the Working Group ‘Translational Health Informatics’ of the European Federation for Medical Informatics (EFMI) have also been actively involved in the preparation and realisation of the pHealth 2024 event. pHealth2024 was held at Rende (Cosenza – Italy) within the Conference Centre of the University of Calabria. This volume of proceedings covers 1 Keynote Paper, 24 Full Papers, 9 Poster Papers and 1 Panel from 111 authors from 11 countries around the world. All submissions have been carefully and critically reviewed by at least two independent experts from a country other than the author’s home country, and additionally by at least one member of the Scientific Programme Committee. This very selective process guarantees the high scientific level of the accepted and ultimately published papers.

The editors are indebted to the internationally acknowledged and highly experienced reviewers for having essentially contributed to the quality of the conference and the book at hand.

The editors are also grateful to the members of the international Scientific Programme Committee, and especially the dedicated efforts of the Local Organising Committee members and their supporters for their careful preparation and the smooth operation of the conference.

Mauro Giacomini, Bernd Blobel, Pierangelo Veltri

pHealh2024 Co-chairs

Health and social care systems around the globe currently undergo a transformation towards personalized, preventive, predictive, participative precision medicine (5PM), considering the individual health status, conditions, genetic and genomic dispositions, etc., in personal, social, occupational, environmental and behavioral context. This transformation is strongly supported by technologies such as micro- and nanotechnologies, advanced computing, artificial intelligence, edge computing, etc. For enabling communication and cooperation between actors from different domains using different methodologies, languages and ontologies based on different education, experiences, etc., we have to understand the transformed health ecosystems and all its components in structure, function and relationships in the necessary detail ranging from elementary particles up to the universe. That way, we advance design and management of the complex and highly dynamic ecosystem from data to knowledge level. The challenge is the consistent, correct and formalized representation of the transformed health ecosystem from the perspectives of all domains involved, representing and managing them based on related ontologies. The resulting business view of the real-world ecosystem must be interrelated using the ISO/IEC 21838 Top Level Ontologies standard. Thereafter, the outcome can be transformed into implementable solutions using the ISO/IEC 10746 Open Distributed Processing Reference Model. Model and framework for this system-oriented, architecture-centric, ontology-based, policy-driven approach have been developed by the first author and meanwhile standardized as ISO 23903 Interoperability and Integration Reference Architecture.

Most clinical guidelines for the assessment and management of atrial fibrillation emphasize the importance of decision support provided by Patients Decision Aids, but they are to be used and evaluated only in the context of Shared Decision-Making. Detailed examination of 10 clinical decision support tools reveals that many do not engage with patient’s preferences at all. Only two take them seriously in terms of their formation, elicitation and processing, aimed at identifying the optimal personalised decision for the patient. This failure is traced to a reluctance to accept the ontological nature of preferences, as instantiations of comparative magnitudes, and to set them in an analytical framework that facilitates their transparent integration with individualised evidence.

Hospital@home is a healthcare approach, where patients receive active treatment from health professionals in their own home for conditions that would normally necessitate a hospital stay.

This cross-sectional study aimed to investigate the perspective and caregiving practices of Thai adolescents towards the elderly in the Northeastern region of Thailand. The study was carried out during July 1st to September 30th, 2023 among 1,551 participants in grades 4-6 from eight randomly selected schools. The analyzing using descriptive statistics. The results found that the average age was 15.30±1.66 years old, 62.4% were female, and most lived with their parents and relatives. About 36.4% of parents have experienced either widowhood or separation, 69.4% of families had a monthly income less than 15,000 THB. While 33.7% had an elderly person in the family, 1.6% lived with bedridden patients, and 40.3% required assistance in daily activities such as cooking, mobility, while 58.7% had diabetes and high blood pressure. 59.3% did not have a primary caregiver in the family, only parents or relatives usually taking on this role. Adolescent grandchildren spent the majority of their time on education. Almost one of three rarely took care of the elderly, even though their parents and teachers taught about moral responsibility. Regarding the belief in the merit that would arise from taking care of the elderly, one of five was indifferent or did not believe, while half of them believed to some extent. From self-assessment of their ability to take care of the elderly, most were in the moderate and low levels, despite receiving information from family, teachers, or various media. The predominant perspective is that caregiving is perceived as the responsibility of parents or health professionals, and the belief that the elderly is captious and irksome. Therefore, it is advisable to present policy-oriented information across education, health, and societal dimensions to support children and young people to learn about elderly individuals and instilling their responsibility within families and societies, fostering a sustainable and well-being-oriented community.

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.

This study focuses on the complex interplay of healthcare, economic factors, and population dynamics, addressing a research gap in regional-level models that integrate diverse features within a temporal framework. Our primary objective is to develop an advanced temporal model for predicting cardiovascular mortality in Russian regions by integrating global and local healthcare features with economic and population dynamics. Utilizing a dataset from the Almazov Center’s Department of Mortality Performance Monitoring, covering 94 regions and 752 records from January 1, 2015, to December 31, 2023, our analysis incorporates key parameters such as angioplasty procedures, population morbidity rates, Ischemic Heart Disease (IHD) and Cardiovascular Diseases (CVD) monitoring, and demographic data. Employing XGBoost and a regression model, our methodology ensures the model’s robustness and generalizability.

The care model Hospital@Home offers hospital-level treatment at home, aiming to alleviate hospital strain and enhance patient comfort. Despite its potential, integrating digital health solutions into this care model still remains limited. This paper proposes a concept for integrating laboratory testing at the Point of Care (POC) into Hospital@Home models to improve efficiency and interoperability.

The analysis of data on waiting lists in Italy is regulated by the PNGLA (National Plan for the Governance of Waiting Lists). However, the Plan does not specify the characteristics of the data to be returned by the Regions for the purposes of monitoring, with the result that it is frequently either in aggregate form, unreadable, or incomplete, and therefore cannot be analysed in any meaningful way.

Fondazione the Bridge and AGENAS, with the University of Genoa and the University of Pavia, conducted a pilot study on a methodological model for the collection of waiting lists data.

The model proved to be effective and replicable, also providing a more valuable opportunity to analyse waiting lists data.

Stroke remains a significant global health burden, with substantial costs and morbidity associated with its occurrence. To address this challenge, STROKE 5.0 proposes a comprehensive approach to stroke care management, integrating advanced digital technologies and clinical expertise. This paper presents the rationale, design, and potential impact of the STROKE 5.0 platform, which aims to optimize stroke care delivery from pre-hospital assessment through acute hospitalization. The platform facilitates early symptom recognition, efficient emergency response, and streamlined hospital management through intelligent decision support systems. By leveraging predictive analytics and personalized care pathways, STROKE 5.0 seeks to enhance clinical outcomes while providing a platform capable of optimizing the efficiency of service delivery. This innovative model represents a proactive shift towards evidence-based, patient-centered stroke care, with implications for healthcare quality improvement and resource allocation in the digital health domain.

Clinical guidelines for the assessment and management of atrial fibrillation emphasize the importance of taking the patient’s preferences into account. A detailed examination of those from the National Institute for Excellence in Health and Social Care (NICE) raise serious questions about whether the recommendations embed preferences about crucial trade-offs that pre-empt those of the patient; do not stress the need to provide them with the information on option consequences necessary for them to become an informed patient; and characterise them as ‘concordant’ or ‘discordant’ rather than independently valid. American and European guidelines do not differ significantly in these respects.

The verdict of the UK Supreme Court in the case of Bellman versus Boojum-Snark Integrated Care Trust (2027) will have profound implications for medical practice, medical education, and medical research, as well as the regulation of medicine and allied healthcare fields. Major changes will result from the definition of person-centred care built into the expanded definition of informed and preference-based consent central to the judgment made in favour of Bellman’s negligence claim. (For the avoidance of doubt this is a vision paper.)

Empathetic and emotive design is becoming increasingly important in the digital age. In this research we describe the results of a combined cognitive walkthrough and heuristic evaluation using newly developed, empirically derived empathy or emotive design heuristics. We applied the heuristics to the evaluation of four commonly used survey platforms. Our preliminary findings revealed that the heuristics performed effectively in scoring survey platforms on their level of empathy. Survey platforms that are highly empathetic were scored highest.

The design of user interfaces and systems that promote positive emotional interaction and reaction from end users is becoming a critical area in the design of applications and systems for use by the general population. In this paper we describe our work in the creation of a set of empathetic design heuristics that were developed from examination of the literature in this area within the context of healthcare user interface design. The heuristics and their potential application are explored.

With the advent of the digital health era, there has emerged a new emphasis on collecting health information from patients and their families using technology platforms that are both empathetic and emotive in their design to meet the needs and situations of individuals, who are experiencing a health event or crisis. Digital empathy has emerged as an aspect of interactions between individuals and healthcare organizations especially in times of crises as more empathetic and emotive digital health platforms hold greater capacity to engage the user while collecting valuable health information that could be used to respond to the individuals’ needs. In this paper we report on the results of a scoping review used to derive an initial set of evidence-based empathetic or emotive design heuristics.

Inconsistent disease coding standards in medicine create hurdles in data exchange and analysis. This paper proposes a machine learning system to address this challenge. The system automatically matches unstructured medical text (doctor notes, complaints) to ICD-10 codes. It leverages a unique architecture featuring a training layer for model development and a knowledge base that captures relationships between symptoms and diseases. Experiments using data from a large medical research center demonstrated the system’s effectiveness in disease classification prediction. Logistic regression emerged as the optimal model due to its superior processing speed, achieving an accuracy of 81.07% with acceptable error rates during high-load testing. This approach offers a promising solution to improve healthcare informatics by overcoming coding standard incompatibility and automating code prediction from unstructured medical text.

This paper explores the potential of leveraging electronic health records (EHRs) for personalized health research through the application of artificial intelligence (AI) techniques, specifically Named Entity Recognition (NER). By extracting crucial patient information from clinical texts, including diagnoses, medications, symptoms, and lab tests, AI facilitates the rapid identification of relevant data, paving the way for future care paradigms. The study focuses on Non-small cell lung cancer (NSCLC) in Italian clinical notes, introducing a novel set of 29 clinical entities that include both presence or absence (negation) of relevant information associated with NSCLC. Using a state-of-the-art model pretrained on Italian biomedical texts, we achieve promising results (average F1-score of 80.8%), demonstrating the feasibility of employing AI for extracting biomedical information in the Italian language.

The Prediabetes impacts one in every three individuals, with a 10% annual probability of transitioning to type 2 diabetes without lifestyle changes or medical interventions. It’s crucial to manage glycemic health to deter the progression to type 2 diabetes. In the United States, 13% of individuals (18 years of age and older) have diabetes, while 34.5% meet the criteria for prediabetes. Diabetes mellitus and prediabetes are more common in older persons. Currently, nevertheless, there aren’t many noninvasive, commercially accessible methods for tracking glycemic status to help with prediabetes self-management. This study tackles the task of forecasting glucose levels using personalized prediabetes data through the utilization of the Long Short-Term Memory (LSTM) model. Continuous monitoring of interstitial glucose levels, heart rate measurements, and dietary records spanning a week were collected for analysis. The efficacy of the proposed model has been assessed using evaluation metrics including Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R2).

The growing integration of Internet of Things (IoT) technology within the healthcare sector has revolutionized healthcare delivery, enabling advanced personalized care and precise treatments. However, this raises significant challenges, demanding robust, intelligible, and effective monitoring mechanisms. We propose an interpretable machine-learning approach to the trustworthy and effective detection of behavioral anomalies within the realm of medical IoT. The discovered anomalies serve as indicators of potential system failures and security threats. Essentially, the detection of anomalies is accomplished by learning a classifier from the operational data generated by smart devices. The learning problem is dealt with in predictive association modeling, whose expressiveness and intelligibility enforce trustworthiness to offer a comprehensive, fully interpretable, and effective monitoring solution for the medical IoT ecosystem. Preliminary results show the effectiveness of our approach.

Multiple sclerosis (MS) is an inflammatory autoimmune demyelinating disorder of the central nervous system, leading to progressive functional impairments. Predicting disease progression with a probabilistic and time-dependent approach might help suggest interventions for a better management of the disease. Recently, there has been increasing focus on the impact of air pollutants as environmental factors influencing disease progression. This study employs a Continuous-Time Markov Model (CMM) to explore the impact of air pollution measurements on MS progression using longitudinal data from MS patients in Italy between 2013 and 2022. Preliminary findings indicate a relationship between air pollution and MS progression, with pollutants like Particulate Matter with a diameter of 10 micrometers (PM₁₀) or 2.5 micrometers (PM_2.5), Nitrogen Dioxide (NO₂), and Carbon Monoxide (CO) showing potential effects on disease activity.