Medication Adherence (2025–2029)
The goal of the multidisciplinary study ‘Medication Adherence and Treatment Efficacy in Patients with Dyslipidaemia and Achievement-oriented Novel Patient Digital Support’ is to reduce cardiovascular mortality in Estonia by increasing medication adherence and empowering patients, creating a supportive self-management environment for monitoring their health plan and actively participating in the treatment process. The LDL cholesterol values of patients at North Estonia Medical Centre (NEMC) will be analysed to identify underdiagnosed and undertreated patients. The medication adherence of patients using lipid-lowering drugs (LLD) will be studied, and groups of patients who need additional support will be defined. A novel patient support application will be developed as part of the pilot project, which will increase LLD adherence alongside personalised support. The novelty of the application lies in connecting data from the Estonian Health Information System, ePrescription, and the NEMC hospital database with data entered by the patients themselves, thus enabling two-way communication between the patient and medical staff. In the final stage of the study, an impact assessment of the support application project will be conducted.
Digital Health (2024–2028)
The goal of the project ‘Digital Health for a Healthy Society’ is to increase the number of healthy life years lived by the population. Currently, healthy life expectancy in Estonia is one of the shortest in the EU. To achieve this goal, three closely related digital health areas are being researched, developed and piloted. First, we will use the standardized data exchange environment and digital data of the Estonian Health Information System (EHIS) to develop applications that increase the evidence-based use of data collected by people for health promotion, prevention, and control of chronic conditions. Second, we will focus on sensors and digital applications supported by artificial intelligence (AI) to enable people to collect both biosignals and textual data in a machine-readable format. This will speed up the detection of health risks and reduce the workload of medical professionals. Third, we will develop various AI methods by combining data from EHIS and the Health Insurance Fund database with data collected by people themselves.
Frontiers in Medicine Special Issue (2025)
The integration of Electronic Health Records (EHRs) into healthcare has marked a significant shift in managing patient data, enhancing the accessibility and continuity of care, thereby improving patient outcomes. Despite the promise, the adoption and implementation of EHR systems have encountered persistent issues that limit their full effectiveness within varied healthcare environments. These primary issues include complex data management and custody, varying legislation, the intricacies of cross-border exchanges, and achieving true interoperability among systems. Moreover, advancing technologies like wearables, telehealth, and AI-driven tools underscore the continuous evolution in the field, challenging existing EHR frameworks to adapt and integrate.
This special issue aims to address and consolidate solutions for the substantial barriers EHRs face, spotlighting emerging advancements and fostering a deeper understanding of ongoing and future needs in EHR technologies. It seeks to bridge the gap between current limitations and the envisioned potential of fully integrated, seamless EHR systems that enhance clinical workflows, ensure privacy and data protection, and respond flexibly to patient and provider requirements.
Natural Language Processing (2025)
The AIRE project ‘Testing AI and ML Tools for Structuring Unstructured Medical Texts’ aims to test the automatic structuring and validation of radiological texts as they are entered by physicians, using RDF, SNOMED, and ContSys where possible. The project will explore and compare different AI technologies, including natural language processing (NLP), deep learning, and semantic analysis, to identify the most effective methods for formalizing medical texts. Activities will be divided into two main phases: 1) testing existing technologies, and 2) designing a prototype. If successful, the project should reduce the time required to process medical data, increase the accuracy of medical decisions, and facilitate the semantic interoperability of data, which could ultimately reduce the costs of data analysis for healthcare and research institutions and lead to greater efficiency and better data quality for both primary and secondary use.
TermX (2024)
The purpose of the applied research was to design TermX, an open-source terminology development, management and sharing platform that includes a terminology server, a Wiki environment, a model designer, a transformation editor, and authoring and publishing tools. The main development goals of TermX are to improve interoperability, simplify terminology availability, adapt data model design, and enable data transformation between models using the latest healthcare standard FHIR. Igor Bossenko defended his doctoral thesis on TermX.
Treatment Pathways (2021–2024)
The aim of the project ‘Development of universal data model and continuity of care processes based on international standards for new generation health information systems’ is to find a suitable model for digital health data that can be used to collect and store data in a next-generation health information system and to implement the same model in other large-scale electronic health records (EHR). The data model will serve as a basis for healthcare professionals to use digital decision support systems in the context of electronic medical records (EMR). Defining the data model would lead to the development of EHR processes where a minimum set of health status summaries is defined for each speciality/health problem to enable evidence-based decision-making. The defined health status summaries are needed to develop continuity of care processes, train artificial intelligence and develop intuitive EMRs.