Ebook: Digital Health and Informatics Innovations for Sustainable Health Care Systems

The 34th Medical Informatics Europe Conference, MIE 2024, was held in Athens, Greece, from the 25–29 August 2024. The Conference was co-hosted by the European Federation for Medical Informatics (EFMI) and the Greek Biomedical and Health Informatics Association (GBHIA). The Scientific Programme Committee was chaired by Professor John Mantas with Professor Arie Hasman as co-chair.

The theme of MIE 2024 is Digital Health and Informatics Innovations for Sustainable Healthcare Systems. Given the current and future shortages in the healthcare workforce, it is crucial to gain insight into the approaches and innovations available to mitigate the negative impact of these shortages on healthcare. For quite some time now, the WHO has been promoting the use of information and communications technologies for healthcare and medical purposes to enhance the efficiency of health systems in the delivery of quality, affordable, and equitable care. The WHO developed a global strategy on digital health for 2020–2025, in which digital health was defined as the field of knowledge and practice associated with the development and use of digital technologies to improve health. The success of this global strategy depends, to a great extent, on the state of the art of health-information technology and the outcomes of health-informatics research. The availability of professionals with a knowledge of system design, implementation, interoperability, standards and usability, in addition to a sufficient knowledge of medicine and healthcare, is critical in obtaining digital-health capabilities which will improve population health and access to healthcare delivery. It is equally crucial that healthcare professionals possess adequate knowledge of and skills in health informatics to ensure the seamless use of the new systems. It is therefore essential to provide health-informatics education to users, developers, and researchers. This conference has solicited contributions concerning new approaches in digital health, and the outcomes of health informatics research, development, and education, to support evaluations of the extent to which the consequences of the shortages in the healthcare workforce can be compensated for in the near future.

Artificial Intelligence (AI), especially machine learning, is increasingly applied in healthcare. The large number of AI-related contributions in this publication reflects the prevalence of AI in medicine and healthcare. Natural language processing and generation, decision support and image analysis are important areas in medicine which can make use of AI methods, but it is crucial that all the systems developed are thoroughly tested. The doctor or nurse will make a decision based on the system’s advice, it is therefore vital that systems based on machine learning provide explainable artificial intelligence, or xAI, to enable the user to assess the accuracy of the advice.

These proceedings demonstrate the breadth of health informatics. There are contributions covering topics such as databases, user-centred design, information systems, usability, privacy, security, secondary use of health data, data integration, interoperability, and standardisation, but also clinical guidelines, AI and xAI, decision support, pattern recognition, medical imaging, natural language processing, deep learning, doctor-chatbot interaction, health literacy, and education, to name but a few.

The Proceedings are published by IOS Press as an e-book in the series Studies in Health Technology and Informatics (HTI). Volumes in this series are submitted (for evaluation) for indexing by MEDLINE/PubMed; Web of Science: Conference Proceedings Citation Index – Science (CPCI-S) and Book Citation Index – Science (BKCI-S); Google Scholar; Scopus, and EMCare.

The Editors

John Mantas, Arie Hasman, George Demiris, Kaija Saranto, Michael Marschollek, Theodoros Arvanitis, Ivana Ognjanović, Arriel Benis, Parisis Gallos, Emmanouil Zoulias and Elisavet Andrikopoulou.

Athens, 10.07.2024

Currently, there are no adequate methods for dealing with changes in the healthcare system brought about by electronic health applications (eHealth) or the associated ethical implications in practice. This can be attributed to the lack of comprehensive interdisciplinary approaches that could support teams in integrating ethical considerations into the agile software development process. To close this gap, the DARE approach has been developed and tested in interdisciplinary collaborative research. The DARE method is a modular system designed to improve the development of ethically sound software in a deliberative, agile, and responsive manner.

The aim of this online-survey study is the development and evaluation of a mobile health application specifically designed to meet the needs of individuals who have previously undergone intensive-care treatment. User acceptance and perception play a crucial role in refining and optimizing the app’s features and functionalities. By actively incorporating suggestions and insights from users, the goal is to enhance the overall usability and better cater to the diverse needs of individuals in post-intensive care recovery. This iterative approach ensures that the application remains responsive to the evolving requirements of its target audience. Overall, the emphasis is on creating a user-centric and adaptive tool for former intensive care patients, to develop a user-friendly mobile app.

Data quality deficiencies significantly limit the applicability of real-world data in data-driven medical research. In this study, using an oncological use case, we report and discuss common quality deficiencies in real-world medical datasets, such as missing data, class imbalances, and timeliness issues. We compiled a multi-departmental real-world dataset comprising 13861 cancer cases diagnosed at University Hospital Cologne and examined data quality throughout the data integration process.

Loneliness can be studied through social media and web data to gain insights into the dynamic phenomenon. In this paper, we present our proposed framework through a case study on how to deploy various social media and online data sources to study loneliness comprehensively. The framework is important to understand loneliness from data perspective available online and to complement the theoretical and psychosocial understanding of loneliness. The data on loneliness is gathered through surveys and self-reporting mechanisms. This requires complementing the dynamic and vast data available on the web and through social media. Our results found that tools like Google Trend and News Analysis can give the starting point to explore loneliness in a particular region. Tools like X (formerly Twitter), Reddit, and other social media can give behavioral data on loneliness which can be analyzed through sentiment analysis and other social intelligence analysis. The framework’s utility lies in its potential to inform policies, interventions, and initiatives addressing loneliness through data. However, it is crucial to acknowledge limitations, such as data availability, biases in user-generated content, and ethical considerations.

Childhood mental health problems are a leading cause of disability and frequently go untreated. Barriers to children receiving the most effective care available include shortfalls in three areas: identification, referral to specialists, and delivery of evidence-based treatment (EBT). The current paper details an effort to develop a digital health intervention, the Mental Health Advisor (MHA), to increase the number of children with mental health problems who receive optimal care through identification, specialty referral, and fidelity to EBT. We present this pilot as a case example to help guide other efforts to improve mental health care through technology.

The increased utilization of continuous glucose monitors (CGM) and smart insulin pens (SIP) among people with type 2 diabetes generates significant health data. This study explored possible patterns in long term CGM and SIP data.

Unexpected downtime and long response times of electronic health record (EHR) systems not only impact user satisfaction and clinicians’ work efficiency but also bring about potential harm for patients. Despite improvements in the performance of EHR systems’ architecture, hardware, and networks, technical challenges continue to cause problems. We explored the end-user experiences of EHR technical functionality and quality from four large national cross-sectional surveys conducted among Finnish physicians in 2010–21. The results were analyzed by healthcare sector/specialty groups. In most groups, the experiences of stability and reaction speed became worse in 2010–17, which is readily explained by the implementation of the national patient data repository services, but improvements were seen in 2021, suggesting that EHR vendors have solved at least some of the slowness problems. The proportion of physicians reporting having experienced faulty system function with potential or actualized harm for the patient had decreased in operative and medical specialties and in the private sector but remained stable in other groups. Our findings underline the importance of continuing to develop technical qualities – including the implementations of national integrations.

The MOLD-US framework has been developed to synthesize knowledge for (usability) researchers on aging- and disease-related barriers that can hamper the use of health information technology (HIT). However, dissemination in terms of practical applications of the framework is currently unknown and could inform industry and researchers for applying MOLD-US in practice, but also provide insights on the use of theoretical frameworks in HIT research. Therefore, a citation analysis was conducted on the paper presenting the MOLD-US framework. Nine of the 241 citations were found to report practical application(s) of the MOLD-US framework in their methods section: (1) qualitative research input (n=3), (2) research design (n=3), (3) design approaches (n=2), and (4) conceptual framework development (n=2). Future work aims to explore MOLDUS-US practical applications in the industry, through for example grey literature, but also continuously monitor novel applications to enhance the development of HIT for the aging population.

Although eHealth interventions are increasingly recognized as a useful tool to support healthcare, relatively few studies focus on the physician-end’s usability. This study aims to evaluate the Healthcare Professional’s (HCP) platform of the Take-A-Breath project, a Greek initiative for personalized respiratory disease monitoring, training and self-management. The pre-pilot usability study, involving 10 participants, combines qualitative methods, behavioral observations, and standardized measures of user experience and usability. While relatively high scores indicate overall acceptance, concerns are also discussed, particularly related with the volume of information provided and actions available to the users, hindering the usability of the system due to an overload effect. Findings emphasize also the need for more tailored in-app wordings as well as the integration of similar systems with the already set up electronic health record systems. This study contributes to understanding digital intervention success among HCPs in respiratory healthcare.

Adherence to recombinant human growth hormone (r-hGH; somatropin, [Saizen^®], Merck Healthcare KGaA, Darmstadt, Germany) treatment is important to achieve positive growth and other outcomes in children with growth disorders. Automated injection devices can facilitate the delivery of r-hGH, injections of which are required daily for a number of years. The ability to adjust injection device settings may improve patient comfort and needle anxiety, influencing adoption and acceptance of such devices, thereby improving treatment adherence. Here, we present the results of a retrospective observational study which investigated the association between injection device settings and adherence in the first 3 months of treatment in patients with growth disorders. Patients aged ≥2 and <18.75 years of age at treatment start, with ≥3 months of adherence data from start of treatment with the third generation of the easypod^® device (EP3; Merck Healthcare KGaA, Darmstadt, Germany) were selected (N=832). The two most chosen combinations of device settings at treatment start were the default settings for injection speed, depth and time, or a slow injection speed and default depth and time. These combinations also demonstrated the highest adherence rates (94% and 95%, respectively) compared to other device settings (89%). A higher proportion of patients with intermediate/low adherence in the first month of treatment (31%, n=18/59) changed the device settings during treatment compared with those with high adherence (16%, n=128/803) (p=0.005). The ability to adjust injection device settings offers a valuable opportunity for personalizing treatment, improving patient comfort and treatment adherence.

Over the last decade, the exponential growth in patient data volume and velocity has transformed it into a valuable resource for researchers. Yet, accessing comprehensive, unique patient data sets remains a challenge, particularly when individuals have received treatments across various practices and hospitals. Traditional record linkage methods fall short in adequately protecting patient privacy in these scenarios. Privacy Preserving Record Linkage (PPRL) offers a solution, employing techniques such as data cryptographic methods to identify common patients occurring in multiple datasets, while maintaining the privacy of other patients. This paper proposes an investigation into combined approaches of two common German PPRL tools, namely E-PIX and MainSEL. Each tool, while aiming for ‘privacy preservation’, employs distinct methods that offer unique advantages and drawbacks. Our research aims to explore these in a combined approach to leverage their respective strengths and mitigate their limitations. We anticipate that this synergistic approach will not only enhance data privacy but also allow for easier synchronisation of research data. This study is particularly pertinent in light of evolving privacy regulations and the increasing complexity of healthcare data management. By advancing PPRL methodologies, we aim to contribute to more robust, privacy-compliant data analysis practices in healthcare research.

This paper presents an implementation of an architecture based on open-source solutions using ELK Stack – Elasticsearch, Logstash, and Kibana – for real-time data analysis and visualizations in the Medical Data Integration Center, University Hospital Cologne, Germany. The architecture addresses challenges in handling diverse data sources, ensuring standardized access, and facilitating seamless analysis in real-time, ultimately enhancing the precision, speed, and quality of monitoring processes within the medical informatics domain.

Background: The existence of multiple code systems and standards has highlighted the necessity for innovative solutions to bridge these discrepancies. Objectives: This research investigates the utilisation of TermX to tackle the challenges of interoperability in radiology procedures, with a specific emphasis on angiography and X-ray modalities. Results: The study produced a revised RadLex data model and mapping guide, designed to classify radiology services using TermX. In total, 380 concepts were required to comprehensively describe all 622 procedures examined. Conclusions: Our study demonstrates the effectiveness of TermX in simplifying the process of mapping between code systems, thus enabling more efficient analysis, and reporting of data.

This paper provides insights into user perspectives on telemedicine for cancer based on Focus Group Discussions (FGDs) within the eCAN Joint Action. Two FGDs centered on the eCAN mobile app and the eCAN dashboard, aiming to confirm user acceptance and understand cancer patients’ and healthcare professionals’ views. The findings highlight the importance of personalized deployment of telemedicine technologies to meet the specific needs of end users.

This study evaluated the feasibility of utilizing routinely collected EHR data to calculate pre-developed quality indicators on antibiotic use. Three out of four indicators were found feasible. Main barriers included local codes for lab tests and surveillance cultures and lack of data on empirical prescription. Future studies should include data from multiple ICUs to test the variations in QIs between ICUs.

This study aimed to gain insight into the success rate of linking the NICE registry with SES data from CBS and to examine whether the characteristics of linked and non-linked patients differ. Although clinically relevant differences were found, in total 93,4% of the admissions were successfully linked.

Many see the role of health informatics research as informing the development and implementation of information technology in clinical practice. The aim of this study is to see if this role is realized in the ongoing implementation of a large-scale health information system in central Norway. By doing a document analysis of the planning documents for the implementation, we assess to what extend evidence from the scientific community is explicitly referenced and used in the implementation planning. We found that evidence available is not explicitly used, and that evidence required is not widely available.

The reuse of real-world symptom monitoring data is essential in improving the quality of hospice care. A framework for achieving this is a Learning Health System, in which the development of a well-defined dataset is essential. This paper discusses the challenges in the design of a comprehensive dataset, focusing on variations in two electronic health record systems and divergent care processes.

Digital health can enhance self-management of patients such as usage of medication reminders, thereby improving health outcomes. However, for successful implementation of such interventions, integration with the electronic health record (EHR) is useful. We evaluated the implementation of an integrated patient portal medication reminder tool in kidney transplant patients. Overall, 40.5% of the patients agreed that integrated EHR medication reminders assisted them in taking their medication on time.

Introduction: Basal insulin non-adherence is a challenge in people with type 2 diabetes (T2D). Methods: Using injection data recorded by a connected insulin pen, we employed a novel three-step methodology to assess three aspects of adherence (overall adherence, adherence distribution, and dose deviation) in individuals with insulin-treated T2D undergoing telemonitoring. Results: Among participants, 52% were considered overall adherent. However, deviations from the recommended dose were observed in all participants, with increased and reduced doses being the predominant forms of non-adherence. Conclusions: Our study underscores the prevalence of basal insulin dosing irregularities in individuals with insulin-treated T2D undergoing telemonitoring.

Despite their variability, Digital Health Apps (DHAs) typically share functionalities (i.e. core assets) and can thus be considered as a family of similar products with unique features adapted to specific use cases.

Access to healthcare data for secondary use in clinical research is often restricted due to privacy concerns or business interests, hindering comprehensive analysis across patient pathways. The Smart FOX project seeks to address this challenge by developing concepts, methods, and tools to facilitate citizen/patient-driven donations of health data for clinical research. Leveraging the groundwork, laid by the national Electronic Health Record implementation in Austria (called ELGA), Smart FOX aims to harness structured datasets from ELGA for research purposes through an opt-in approach. With funding secured from the Austrian Research Promotion Agency, the project embarks on innovative solutions encompassing governance frameworks, community engagement, and technical infrastructure. The Smart FOX consortium, comprising key stakeholders across various healthcare-associated domains, will evaluate these efforts through demonstrators focusing on clinical registries, patient-generated data, and recruitment services. The project targets to accompany the development of future data donation infrastructure while ultimately advancing clinical research efficiency and bolstering Austria’s preparedness for the European Health Data Space. This paper presents the first systematic evaluation of the technical concept and proposal for the federated system architecture of the Austrian Health Data Donation Space, which is the socio-technical goal of Smart FOX.

The necessity for robust, enduring, and relevant healthcare interoperability is universal across all clinical domains. However, we identified a gap in the availability of open-source, no-cost, high-quality tools that offer multilingual support and an advanced graphical interface. To address this, we developed TermX, an open-source platform to harmonise terminology and support interoperability between healthcare institutions and systems. TermX incorporates a terminology server, a Wiki, a model designer, a transformation editor, and tools for authoring and publishing. TermX is designed to develop terminology and implementation guides for healthcare systems at both the national and regional levels. It aims to ensure open, standardised access to published data and guarantee semantic interoperability based on the FHIR standard.