Ebook: Collaboration across Disciplines for the Health of People, Animals and Ecosystems

The European Federation for Medical Informatics Special Topic Conference, EFMI STC 2024, was held in Timisoara, Romania, from 27 to 29 November 2024. This event was organized by the Romanian Society of Medical Informatics, a national member of the European Federation for Medical Informatics, and hosted by University Politehnica Timişoara. The EFMI Working Groups, “Healthcare Informatics for Interregional Cooperation” (HIIC), “One Digital Health” (ODH), “Education” (EDU), and “Evolution and Trends in Medical Informatics” (ETMI), were actively involved in preparing this event, and Professor Lăcrămioara Stoicu-Tivadar and Professor Arriel Benis chaired the Scientific Program Committee.

The theme of EFMI STC 2024 is “Collaboration across Disciplines for the Health of People, Animals, and Ecosystems”. This theme is not just a topic for discussion, but a crucial area in which innovation and collaboration are shaping the future of healthcare. It underscores the significant intersection of healthcare with other fields, offering a future rich with potential. The concept of One Health underscores the complex interconnections between human, animal, and environmental health, and integrating data and technology with the One Digital Health framework across these domains is crucial to building robust health ecosystems that will empower communities to tackle complex challenges and improve public health. This vision is not just a distant goal; it is within reach through the collaborative efforts of our communities, leveraging the power of medical informatics to inform disease surveillance, prevention, and management.

Communication is the cornerstone of effective healthcare, and the digital age has ushered in new paradigms for interprofessional collaboration. Digital tools are not just redefining how healthcare providers, academics, industries, caregivers, and patients interact, they are revolutionizing these interactions. They enhance workflows, promote teamwork, and lead to better health outcomes. The aim of EFMI STC 2024 is to provide each participant with the opportunity to contribute to the crucial development of platforms that break down silos and geographic borders, creating a cohesive environment where every voice is heard and valued. This digital revolution in healthcare is not merely a trend, it is a powerful tool that can empower us to shape a healthier future.

This means that the educational paradigms that shape the entire healthcare landscape must also adapt. In a field that is constantly evolving, continuous learning is essential to equip current and future professionals with the skills they will need to thrive in this complex and dynamic environment. By continuously enhancing the digital literacy of actors in the healthcare system and adapting educational frameworks, learning experiences can be improved and proactive thinking developed, preparing the next generation of practitioners and researchers to meet the challenges of tomorrow.

The evolution of medical informatics is deeply intertwined with cross-disciplinary collaboration. Once centered on electronic health records and clinical data management, medical informatics has expanded to include predictive analytics, telemedicine, and the integration of the One Health and One Digital Health frameworks. Understanding the roots of our field better will allow us to look at the future and reshape the ways in which we collaborate, educate, interact, and support healthcare.

This EFMI STC is an opportunity for knowledge sharing and a call to action. Together, we can profile a future in which digital health and medical informatics lead the way to a healthier world.

These 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.

Timişoara, October 2024

Editors

Lăcrămioara Stoicu-Tivadar, Arriel Benis, Thomas M. Deserno, Sorana D. Bolboacă, Kaija Saranto, Mihaela Crişan-Vida, Parisis Gallos, Oana S. Chirila, Patrick Weber, George Mihalaş and Oscar Tamburis

Healthcare systems worldwide face escalating costs and demographic changes, necessitating effective evaluation tools to understand their underlying challenges. Switzerland’s high-quality yet costly healthcare system underscores the need for robust assessment methods. Existing international rankings often lack transparency and comparability, highlighting the value of structured frameworks like the Health System Performance Assessment (HSPA) by the World Health Organization (WHO). This framework evaluates healthcare systems across multiple dimensions including governance, resource generation, financing, and service delivery. This paper aims to integrate Swiss healthcare indicators from the Swiss Health Observatory (Obsan) into the HSPA framework, addressing the central research question: How can these indicators be mapped to the HSPA framework, and what insights does this integration provide? Our methodology includes selecting and categorizing Obsan indicators, manually mapping them to HSPA sub-functions, and validating these mappings using word embeddings and cosine similarity. An R Shiny application was developed for interactive visualization. Results demonstrate accurate indicator assignment, enabling intuitive visualization and enhancing data structuring.

Usability is understood as a critical component to the success of electronic health records and other related healthcare technologies. Usability testing methods routinely employ scripts that help researchers understand how a particular tool works under real world conditions. This scoping review sought to better understand the guiding frameworks, principles, and methodologies employed when generating usability testing scripts to better understand how script generation occurs. Three main themes emerged through qualitative analysis: researchers sought to observe the baseline functionality being tested, the most representative tasks, or the most complex tasks. This scoping review highlights a lack of consistent processes in usability test script generation. There is a need to create standardized usability testing scripts for usability testing.

Access to the internet and online resources changes the concept of health and increases people’s autonomy. In this context, Health Literacy (HL) is a critical determinant of health-related choices. At World Health Organization (WHO) level, M-POHL (Action Network on Measuring Population and Organizational Health Literacy of WHO-Europe) created and validated on European population four questionnaires: digital HL (HLS19-DIGI), communication HL (with doctors from health care services - HLS19-COM-P-Q11 long version and HLS19-COM-P-Q6 short version), online navigation HL (HLS19-NAV), and vaccination HL (HLS19-VAC). Based on the expertise of the team, the present study aimed to report the study protocol for Romanian translation, culturally adapting and psychometric testing the following three M-POHL health literacy tools: HLS19-DIGI, HLS19-NAV, and HLS19-COM-P-Q11, HLS19-COM-P-Q6. We will conduct a qualitative descriptive study design in seven steps to translate and adapt the HLS19-DIGI, HLS19-NAV, and HLS19-COM-P-Q11, HLS19-COM-P-Q6 to the Romanian speakers. The study will begin with the translation of English (En)-Romanian (Ro) (2 researchers involved) (step 1), followed by the evaluation of the translation by a bilingual researcher independent of the two researchers who did the En-Ro translation (step 2), the translation of Ro-En (2 researchers but not those in step 1; step 3), the evaluation of the translation by a bilingual researcher independent of the two researchers who did the Ro-En translation (step 4), evaluation of the translation of the tool in an expert group (step 5), pilot testing on a sample of the target population (step 6) and full psychometric testing of the version resulting from step 6 (step 7).

Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This study leverages machine learning (ML) to enhance the understanding of disease progression and uncover gender-based differences in MS by analyzing clinical data integrated with patient-reported outcomes (PROMs). We conducted a prospective cohort study involving 250 MS patients at a secondary care hospital in Spain over an 18-month period. Using REDCap for data management, we collected comprehensive demographic, clinical, and PROMs data. Our analysis utilized Decision Trees, Random Forest, and Support Vector Machine algorithms to classify patients based on disease evolution and infer Expanded Disability Status Scale (EDSS) levels. Additionally, we employed propensity score matching to analyze gender differences, focusing on clinical outcomes and quality of life measures. The results could indicate that integrating diverse data sets through ML would significantly improve the diagnostic accuracy and serve as a support for clinician’s decision making. Our models achieved high accuracy in classifying MS types and predicting disability levels, demonstrating the potential of ML in personalized treatment planning. Furthermore, our findings suggest notable gender differences in disease progression and response to treatment. These insights advocate for a gender-specific approach in MS management and highlight the importance of personalized medicine. This study underscores the transformative potential of ML in enhancing the understanding and management of MS through integrated data analysis.

The paper presents perceptions and feedback from speech therapists and parents embracing the idea of using digital tools in improving the language of the children with speech disorders. The authors investigated the perception of speech therapists and parents and their readiness to use digital tools in speech therapy starting from several digital applications from the domain. The feedback was positive, 88.3% of the parents agree to use digital apps at home, between face-to-face speech therapy sessions coordinated by speech therapists and 75% of parents agree to use them several days a week.

Precision Forestry is an emerging approach that uses digital technologies for data-driven decision-making in environmental management. Traditional methods for assessing tree risk are often subjective and focus on individual trees using mechanical approaches. The #SecureTree model offers an innovative alternative by deploying sensors to measure biophysical parameters like temperature, humidity, and acceleration. Data from these sensors is processed to create a risk assessment map based on the progression of trees’ behaviors. This model is non-invasive and objective, addressing risk more effectively than current methods. Field tests validated the model’s accuracy and highlighted its potential to identify long-term risk trends, enabling better planning for disruptive events and the development of digital strategies for emergency management.

Digital Twins, represents an innovative approach consisting of real-time digital replicas of physical entities. This innovative concept has already many applications in Industry, automotive, business environment and is also transforming the healthcare industry. Digital Twins have the potential to provide comprehensive models of individual patients, hospital facilities, and medical processes, integrating data from various sources such as electronic health records, wearable devices, and imaging systems. This paper discusses Digital Twin models and their application in healthcare, focusing on care coordination, treatment planning and hospital management development.

Incorporating digital medicine into medical education equips students for the evolving landscape of healthcare. This study aimed to assess a digital medicine course developed at Bielefeld University by evaluating student attainment of learning outcomes outlined by Foadi et al. In the course, the students designed a digital application for various medical conditions, taking into account interdisciplinary factors. The course took place in 2023 with medical students who attended the course due to the focus of their studies. In a pilot study, the progress of ten participants was assessed using a pre-post survey design. Results revealed substantial improvement in students’ achievement of learning outcomes post-course (median = 2, IQR 1-2) compared to pre-course (median = 3, IQR 3-4), suggesting the course’s efficacy in effectively teaching digital medicine.

Medical informatics is a multidisciplinary field combining clinical and technical expertise. Addressing the challenge of aligning software design with clinicians’ real-world needs, Dedalus established the Medical Office, a dedicated department designed to integrate clinical expertise directly into the software development process, in 2022. This paper details the approach and impact of the Medical Office. An international team of 15 healthcare professionals with experience in medical informatics was assembled. The team employed a multifaceted approach, incorporating global communication sessions and a ticketing system to track and analyze service requests. Over two years, 398 tickets were received, categorized into nine areas: clinical content curation, medical terminologies, clinical safety, clinical evaluation, design support, clinical UX, research & publication, real-world medical cases, and pre-sales support. The average duration of ticket resolution decreased over time, attributed to process fine-tuning and the formation of a relevant expert group. A preliminary satisfaction survey indicated positive feedback from technical teams. The collaborative model improved software design, usability, and clinical safety, demonstrating the value of clinician involvement. While preliminary results are promising, ongoing evaluation and adaptation are essential. The study emphasizes the importance of interdisciplinary collaboration in medical informatics and the benefits of clinician involvement in healthcare technology development. Future studies should explore this model’s long-term impacts and scalability in other organizations and healthcare systems.

Hospital at Home (HaH) is a model of care that provides hospital-level care in the patient’s home, requiring a unique set of competencies and skills from both multidisciplinary care teams and informal caregivers. These skills are often different from those required in traditional hospital settings. The aim of this paper is to consolidate the information of HaH-related education and training to support the development of standardized curricula to ensure safe hospitalization at home. We compiled relevant information from the scientific literature on HaH approaches and studies and conducted a web search. Our results indicate that healthcare professionals are trained in short training sessions, covering specific skills needed in the HaH context. These skills comprise, among others, communication, medication safety, infection control, and wound care. Patients and their families receive training in recognizing symptoms of deterioration and self-care. Concrete guidelines or standardized training programs are still missing. Future research should thus focus on developing standardized HaH training protocols and programs for both staff and patients to ensure patient safety at home.

Sesterterpenoids, a subset of the terpene family, exhibit notable biological activities. These natural compounds are present in a variety of organisms such as plants, fungi, bacteria, insects and marine life. The therapeutic potential and structural diversity of sesterterpenoids have attracted considerable interest in pharmacological and chemical research. This study illustrates the development of a database to structure and manage data on these compounds. The design process involves the collection of user requirements, creation of a conceptual model with and Entity-Relationship Diagram (ERD), development of a logical model, and implementation in Microsoft SQL Server 2022. Data collection began with an extensive literature review and organization in an Excel spreadsheet. The resulting database improves data acquisition, organization, and accessibility. Future work will include building a website to facilitate data entry, editing, reading and extraction, and automation of data updates via external web services.

Medication review is a collaborative process between pharmacists and general practitioners (GPs) aimed at optimizing patient care by identifying and eliminating harmful medications. This paper proposes a collaborative platform to enhance pharmacist-GP interactions, assess drug-drug interactions, evaluate adverse effects, and manage dosages. The platform uses the issue mapping function of IBIS to structure dialogues and systematically evaluates proposed actions using the QuAD framework to support decision-making. An ontology based on medical knowledge ensures consistency, while visual enhancements such as varying edge width, color coding, and highlighting preferred actions enable swift, informed decisions. These tools improve collaboration and patient care outcomes.

This scoping review explores mobile health (mHealth) technologies and their features affecting medication adherence in cancer patients. Among 11 selected studies, predominantly from the USA, mHealth tools, particularly smartphone apps, were examined for their features in managing cancer patient’s medication adherence. The studies highlighted the importance of adherence in continuous cancer therapy, with mHealth tools offering reminders and interactive features, that aim to enhance patient engagement. However, the review identified research gaps, emphasizing the need for broader investigations into diverse mHealth tools beyond apps, including electronic capsules and smart pill dispensers. Additionally, it underscored the absence of information on costs, user input, integration with electronic health records, and data management. While acknowledging potential positive impacts on adherence, the review calls for more comprehensive research to substantiate these findings in clinical oncology.

The AKTIN Emergency Department Registry, a German health data network, faces operational challenges due to rapid growth. Manual data request processes have become inefficient, hindering timely research and straining personnel. To address these challenges, we undertook a user-centered analysis utilizing Design Thinking principles to identify pain points and functional requirements in current data request creation and management processes. Future work will prioritize iterative implementation of the created concepts with continuous user engagement and rigorous software validation.

Pediatric growth hormone deficiency (PGHD) is a chronic condition where the pituitary gland fails to produce sufficient growth hormone, leading to delayed growth and developmental challenges. Patient journey maps can provide insight into pain points and potential opportunities for new or improved interventions to enhance care. However, a patient journey map does not yet exist for PGHD. Secondary data analysis was performed on interviews and focus groups from five cohorts in Sweden, the United Kingdom, Luxembourg, France, and The Netherlands. Participants included 62 patients and caregivers who used a prototype digital health solution, which was used to guide discussions. Grounded theory was used to analyze the data, resulting in a patient journey map comprising six stages: awareness, diagnosis, treatment planning, treatment initiation, treatment maintenance and transition. This provides the first detailed PGHD patient journey map, revealing emotional sensitivities and challenges at each stage, and suggesting areas for targeted interventions to improve adherence and long-term outcomes.

The development of medical science allows the treatment of more and more health problems that in the past were not a factor of consumption of health resources, because at that time medical science did not have protocols for their treatment. Health problems that are now treatable, hereafter referred as “new health problems”, often affect large population groups and require increased consumption of health resources. It therefore becomes necessary to increase the number of staff providing health services (doctors, nurses, etc.) and other resources. This raises the question: is it feasible to manage the “new health problems” by the existing medical staff? If not, are there other solutions? Could technology help the existing Medical Staff to sufficiently manage the “new health problems”? We will examine a pilot system “Recording and visualizing of outpatient monitoring data with smart mobile phones”, which seeks to ensure the competence of existing medical staff in the effective treatment of the ever-increasing volume of transplant patients.

This study leverages the DCTPep database, a comprehensive repository of cancer therapy peptides, to explore the application of machine learning in accelerating cancer research. We applied Principal Component Analysis (PCA) and K-means clustering to categorize cancer therapy peptides based on their physicochemical properties. Our analysis identified three distinct clusters, each characterized by unique features such as sequence length, isoelectric point (pI), net charge, and mass. These findings provide valuable insights into the key properties that influence peptide efficacy, offering a foundation for the design of new therapeutic peptides. Future work will focus on experimental validation and the integration of additional data sources to refine the clustering and enhance the predictive power of the model, ultimately contributing to the development of more effective peptide-based cancer treatments.

The COVID-19 pandemic has accelerated the adoption of telemedicine in healthcare. This study explores the feasibility of telemedicine for foot and ankle care in primary settings, using a mixed-methods approach with online questionnaires, focus groups, and interviews. Stakeholders, including patients, podiatrists, and senior healthcare managers, agreed on the need for a telemedicine service. Recommendations include creating evidence-based guidelines, providing professional training, and enhancing community education. The research highlights the necessity for structured telemedicine services, identifying gaps in existing pandemic responses and the need for further guidelines and training.

This research investigates the application of a hybrid Retrieval-Augmented Generation (RAG) and Generative Pre-trained Transformer (GPT) pipeline for extracting and categorizing substance use information from unstructured clinical notes. The aim is to enhance the accuracy and efficiency of identifying substance use mentions and determining their status in patient documentation. By integrating RAG to pre-filter and focus the input for GPT, the pipeline strategically narrows the scope of analysis to the most relevant text segments, thereby improving the precision and recall of the extraction. Utilizing the Medical Information Mart for Intensive Care III dataset, the performance of the pipeline was evaluated through manual verification, assessing various metrics including recall, precision, F1-score, and accuracy. The results demonstrated high precision rates (up to 0.99 for drug and alcohol mentions), and substantial recall (0.88 across all substances for status of the usage).

The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of symptoms following the acute COVID-19 infection. The patient’s baseline clinical features were used as predictors. The analysis showed that older patients with comorbidities are at higher risk of developing more long-lasting post COVID-19 symptoms. Further integration with a personal monitoring device and combination with the Fast Healthcare Interoperability Resources extends the standardization, interoperability and possibility of integration and harmonization with other hospital systems. By employing advanced machine learning techniques, insights can be derived and further examined to improve the outcome and early treatment options for patients.

Prostate cancer (PC) is one of the leading causes of cancer-related mortality among men. Androgen Deprivation Therapy (ADT) has been shown to increase survival in men with metastatic PC. Despite its efficacy, ADT’s long-term use adversely impacts quality of life (QoL) due to multiple side effects. Multimodal cancer rehabilitation (CR) programs improve QoL but face limited uptake. This study explores perspectives of patients with metastatic PC on a Home Automated Telemanagement (HAT) system using semi-structured interviews. Findings indicate that patients appreciated the ease of use, guided exercise content, and progress tracking features of the HAT system. Significant benefits in symptom management, cognitive support, and QoL improvements were reported, suggesting the program’s potential for long-term health management. In summary, the HAT system is a promising tool for supporting CR in patients with metastatic PC. Future developments should address technical issues, incorporate motivational elements, and enhance user feedback to optimize patient engagement and satisfaction.