Gokce Banu Laleci Erturkmen

Background: Evidence-based clinical guidelines are essential for high-quality care yet translating them into personalized clinical decision support remains resource-intensive and time-consuming. Large language models (LLMs) show promise for supporting clinical decision-making, but their limited access to patient-specific data and explicit guideline sources constrains trustworthiness, personalization, and clinical applicability. Retrieval-augmented generation (RAG) addresses part of this challenge by grounding model outputs in curated evidence sources; however, true personalization requires structured access to electronic health record data. Methods: This study presents FHIR-RAG-MEDS, a medical decision support system that integrates HL7 Fast Healthcare Interoperability Resources (FHIR) with an RAG-enhanced LLM to enable patient-specific, guideline-concordant clinical recommendations. Through SMART on FHIR, the system retrieves real-time patient data from FHIR servers, generates structured medical summaries, and incorporates this personalized context into the RAG pipeline, grounding responses in evidence-based clinical guidelines stored in a vector database. Results: FHIR-RAG-MEDS was evaluated using 139 physician-generated clinical questions covering dementia, chronic obstructive pulmonary disease, hypertension, and sarcopenia. Performance was assessed using automated metrics, RAG-specific evaluation frameworks, and independent expert physician review. The system consistently outperformed state-of-the-art medical LLMs, demonstrating higher semantic accuracy, improved faithfulness to guideline content, and stronger clinical relevance. Conclusions: Integrating HL7 FHIR with RAG-based LLMs enables trustworthy, personalized clinical decision support, bridging the gap between static language models and real-world, patient-centered care.

Digital health technologies offer promising opportunities for longitudinal health monitoring, yet their adoption in research settings faces significant barriers including usability concerns, participant burden, and data privacy issues. This paper presents the design, implementation, and usability evaluation of a person-centered digital health system for scalable, privacy-preserving longitudinal health monitoring in ageing populations. The system was developed within the STAGE project to support a two-year study (2026–2028) with approximately 10,000 participants from the Northern Finland Birth Cohort (NFBC1966). The system employs a modular, privacy-centered architecture comprising a cross-platform mobile application and a cloud backend infrastructure, all designed to be GDPR-compliant with comprehensive security measures. Development was guided by structured co-creation activities and patient and public involvement (PPI) through interviews, questionnaires, and workshops with diverse stakeholders including ageing citizens, caregivers, clinicians, and researchers. The mobile application enables periodic self-reported assessments of wellbeing, cognitive and physical functioning, mental health, nutrition, anthropometric measurements, falls, and mobility patterns, complemented by GPS-based mobility tracking during four-week monitoring periods. The system employs a foreground-first data collection approach requiring explicit user interaction to maintain transparency and trust. Two pilot studies demonstrated strong usability outcomes. Pilot #1 (N = 11) yielded high Single Ease Question scores (all means > 5.6/7). Pilot #2 (N = 17) achieved a System Usability Scale score of 87.95 (SD = 8.18), classified as “Excellent,” with no critical issues reported. The platform includes FHIR-compliant data export capabilities (17 profiles, 12 valuesets) to facilitate healthcare system integration. While challenges remain regarding long-term engagement and digital literacy variability, the system provides a robust foundation for sustainable, participant-centered longitudinal health research in ageing populations.

This paper presents the HL7 FHIR–based interoperability architecture developed within the STAGE Project to enable the seamless integration of personalised decision support, health monitoring, and health coaching applications for the prevention and management of ageing with multi-morbidity. The architecture leverages international standards, including CDS Hooks, SMART on FHIR, and the International Patient Summary (IPS), to support vendor-agnostic data exchange and scalable deployment across healthcare settings.

Evidence-based clinical guidelines are essential for high-quality care yet translating them into personalized clinical decision support remains resource-intensive and time-consuming. Large language models (LLMs) show promise for supporting clinical decision-making, but their limited access to patient-specific data and explicit guideline sources constrains trustworthiness, personalization, and clinical applicability. Retriev-al-augmented generation (RAG) addresses part of this challenge by grounding model outputs in curated evidence sources; however, true personalization requires structured access to electronic health record data. This study presents FHIR-RAG-MEDS, a medical decision support system that integrates HL7 Fast Healthcare Interoperability Resources (FHIR) with a RAG-enhanced LLM to enable patient-specific, guideline-concordant clinical recommendations. Through SMART on FHIR, the system retrieves real-time patient data from FHIR servers, generates structured medical summaries, and incorpo-rates this personalized context into the RAG pipeline, grounding responses in evi-dence-based clinical guidelines stored in a vector database. FHIR-RAG-MEDS was evaluated using 70 physician-generated clinical questions covering dementia, chronic obstructive pulmonary disease, hypertension, and sarcopenia. Performance was assessed using automated metrics, RAG-specific evaluation frameworks, and independent expert physician review. The system consistently outperformed state-of-the-art medical LLMs, demonstrating higher semantic accuracy, improved faithfulness to guideline content, and stronger clinical relevance. Integrating HL7 FHIR with RAG-based LLMs enables trustworthy, personalized clinical decision support, bridging the gap between static language models and real-world, patient-centered care.

This study evaluates the user experience and usability of a web-based tool, AICP, that supports healthcare professionals in creating personalized care plans with the help of clinical decision support (CDS), using the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Single Ease Questions (SEQs). The results show high usability (SUS: 75.45) and positive user experience, particularly in stimulation and novelty. Following CDS recommendations in a structured way and displaying patients’ care plan adherence in charts and tables were found effective. However, dynamic content changes and raw sensor data presentation were identified as areas for improvement. Combining data through reasoning or allowing users to make inferences could further improve usability, supporting better patient care coordination.

This study explores the relationship between user perceptions, as measured by the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, and actual usage patterns of the ADLIFE Digital Personalized Care Platform. Data were collected from healthcare professionals and patients/informal caregivers across multiple pilot sites. Significant correlations were found between usability factors, technology anxiety, performance expectancy, and platform usage metrics, underscoring the importance of user-centred design and support systems. These findings offer actionable insights for optimising digital health platforms to enhance user engagement and adoption.

Introduction: The European Health Data Space (EHDS) initiative was launched to create a unified framework for health data exchange across Europe. Central to this initiative is the European Electronic Health Record Exchange Format, designed to achieve interoperability of electronic health record data across Europe. Despite these advancements, the readiness of current guidelines and implementations, such as the European Patient Summary, to support secondary use in clinical research, particularly in cardiology, remains underexplored.Methods: This study aims to evaluate the European Patient Summary guidelines and their implementations, specifically the HL7 FHIR International Patient Summary Implementation Guide, to determine their suitability for secondary use in clinical research. The focus is on identifying gaps and extensions needed to enhance the utility of the European Patient Summary for building artificial intelligence models in assisting heart failure management.Results: We selected two European Union-funded research projects, DataTools4Heart and AI4HF, that aim to reuse electronic health record data to develop artificial intelligence models for personalized decision support services for heart failure patients. We analyzed their clinical use cases and the specific data items required, and we compared these with the current European Patient Summary guidelines and provided a detailed gap analysis indicating similarities and required extensions. In our gap analysis, we also compared the needs of DataTools4Heart and AI4HF with the HL7 FHIR International Patient Summary Implementation Guide to assess the extensions needed to support clinical research.Discussion: The EHDS is a transformative initiative to establish a European health data ecosystem that supports healthcare delivery and clinical research. Our comparative analysis demonstrates that, with minor extensions, these guidelines have significant potential to facilitate access to electronic healthcare record data for the secondary use, particularly in training AI models. We advocate for the adoption of an International Patient Summary format as a semantically interoperable core set of data elements, which will enhance global clinical research efforts and improve patient outcomes through precision medicine.

Introduction: This paper outlines the design, implementation, and usability study results of the patient empowerment process for chronic disease management, using Patient Reported Outcome Measurements and Shared Decision-Making Processes. Background: The ADLIFE project aims to develop innovative, digital health solutions to support personalized, integrated care for patients with severe long-term conditions such as Chronic Obstructive Pulmonary Disease, and/or Chronic Heart Failure. Successful long-term management of patients with chronic conditions requires active patient self-management and a proactive involvement of patients in their healthcare and treatment. This calls for a patient-provider partnership within an integrated system of collaborative care, supporting self-management, shared-decision making, collection of patient reported outcome measures, education, and follow-up. Methods: ADLIFE follows an outcome-based and patient-centered approach where PROMs represent an especially valuable tool to evaluate the outcomes of the care delivered. We have selected 11 standardized PROMs for evaluating the most recent patients’ clinical context, enabling the decision-making process, and personalized care planning. The ADLIFE project implements the "SHARE approach’ for enabling shared decision-making via two digital platforms for healthcare professionals and patients. We have successfully integrated PROMs and shared decision-making processes into our digital toolbox, based on an international interoperability standard, namely HL7 FHIR. A usability study was conducted with 3 clinical sites with 20 users in total to gather feedback and to subsequently prioritize updates to the ADLIFE toolbox. Results: User satisfaction is measured in the QUIS7 questionnaire on a 9-point scale in the following aspects: overall reaction, screen, terminology and tool feedback, learning, multimedia, training material and system capabilities. With all the average scores above 6 in all categories, most respondents have a positive reaction to the ADLIFE PEP platform and find it easy to use. We have identified shortcomings and have prioritized updates to the platform before clinical pilot studies are initiated. Conclusions: Having finalized design, implementation, and pre-deployment usability studies, and updated the tool based on further feedback, our patient empowerment mechanisms enabled via PROMs and shared decision-making processes are ready to be piloted in clinal settings. Clinical studies will be conducted based at six healthcare settings across Spain, UK, Germany, Denmark, and Israel.

Background: The increasing population of older adults has led to a rise in the demand for health care services, with chronic diseases being a major burden. Person-centered integrated care is required to address these challenges; hence, the Turkish Ministry of Health has initiated strategies to implement an integrated health care model for chronic disease management. We aim to present the design, development, nationwide implementation, and initial performance results of the national Disease Management Platform (DMP). Objective: This paper’s objective is to present the design decisions taken and technical solutions provided to ensure successful nationwide implementation by addressing several challenges, including interoperability with existing IT systems, integration with clinical workflow, enabling transition of care, ease of use by health care professionals, scalability, high performance, and adaptability. Methods: The DMP is implemented as an integrated care solution that heavily uses clinical decision support services to coordinate effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines and, hence, to increase the quality of health care delivery. The DMP is designed and implemented to be easily integrated with the existing regional and national health IT systems via conformance to international health IT standards, such as Health Level Seven Fast Healthcare Interoperability Resources. A repeatable cocreation strategy has been used to design and develop new disease modules to ensure extensibility while ensuring ease of use and seamless integration into the regular clinical workflow during patient encounters. The DMP is horizontally scalable in case of high load to ensure high performance. Results: As of September 2023, the DMP has been used by 25,568 health professionals to perform 73,715,269 encounters for 16,058,904 unique citizens. It has been used to screen and monitor chronic diseases such as obesity, cardiovascular risk, diabetes, and hypertension, resulting in the diagnosis of 3,545,573 patients with obesity, 534,423 patients with high cardiovascular risk, 490,346 patients with diabetes, and 144,768 patients with hypertension. Conclusions: It has been demonstrated that the platform can scale horizontally and efficiently provides services to thousands of family medicine practitioners without performance problems. The system seamlessly interoperates with existing health IT solutions and runs as a part of the clinical workflow of physicians at the point of care. By automatically accessing and processing patient data from various sources to provide personalized care plan guidance, it maximizes the effect of evidence-based decision support services by seamless integration with point-of-care electronic health record systems. As the system is built on international code systems and standards, adaptation and deployment to additional regional and national settings become easily possible. The nationwide DMP as an integrated care solution has been operational since January 2020, coordinating effective screening and management of chronic diseases in adherence to evidence-based clinical guidelines.

Introduction: This paper outlines the design, implementation, and usability study results of the patient empowerment process for chronic disease management, using Patient Reported Outcome Measurements and Shared Decision-Making Processes. Background The ADLIFE project aims to develop innovative, digital health solutions to support personalized, integrated care for patients with severe long-term conditions such as Chronic Obstructive Pulmonary Disease, and/or Chronic Heart Failure. Successful long-term management of patients with chronic conditions requires active patient self-management and a proactive involvement of patients in their healthcare and treatment. This calls for a patient-provider partnership within an integrated system of collaborative care, supporting self-management, shared-decision making, collection of patient reported outcome measures, education, and follow-up. Methods ADLIFE follows an outcome-based and patient-centered approach where PROMs represent an especially valuable tool to evaluate the outcomes of the care delivered. We have selected 11 standardized PROMs for evaluating the most recent patients’ clinical context, enabling the decision-making process, and personalised care planning. ADLIFE project implements the "SHARE approach’ for enabling shared decision-making via two digital platforms for healthcare professionals and patient. We have successfully integrated PROMs and shared decision-making processes to our digital toolbox, based on an international interoperability standard, namely HL7 FHIR. A usability study was conducted with 3 clinical sites with 20 users in total to gather feedback and to subsequently prioritize updates to the ADLIFE toolbox. Results User satisfaction is measured in the QUIS7 questionnaire on a 9-point scale in the following aspects: overall reaction, screen, terminology and tool feedback, learning, multimedia, training material and system capabilities. With all the average scores above 6 in all categories, most respondents have a positive reaction to the ADLIFE PEP platform and find it easy to use. We have identified shortcomings and have prioritized updates to the platform before clinical pilot studies are initiated. Conclusions Having finalized design, implementation, and pre-deployment usability studies, and updated the tool based on further feedback, our patient empowerment mechanisms enabled via PROMs and shared decision-making processes are ready to be piloted in clinal settings. Clinical studies will be conducted based at six healthcare settings across Spain, UK, Germany, Denmark, and Israel.