Dr. Miklós Dezső, Deputy Director General of the HUN-REN Rényi Alfréd Institute of Mathematics, delivered a presentation on the professional background of the “patient pathway mission” at a conference titled “Data-Driven Healthcare and Cybersecurity”, organized by the Hungarian Society for Health Care Management. Researchers at Rényi Institute are building a comprehensive healthcare life-course analytics data platform and developing the necessary mathematical models.
Artificial intelligence can effectively support the operation of Hungary’s healthcare system and assist physicians in early disease detection, treatment planning, and in making the vast amount of available patient data transparent and interpretable. Key research questions currently being explored at Rényi are already beginning to yield answers.
The research is led by Dezső Miklós, Deputy Director General of the HUN-REN Rényi Alfréd Institute of Mathematics, with Adrián Csiszárik, a member of the Rényi Artificial Intelligence Research Department, serving as project leader. The research group, a.k.a. Rényi AI, works across the full scientific spectrum of the field – from theoretical foundations to practical applications – focusing on socially impactful research. A major component of this work is the processing of healthcare data.
The multidisciplinary team – including physicians, AI and healthcare IT specialists, and product developers – now has a clear vision and a structured implementation plan for the comprehensive utilization of Hungary’s patient pathway data assets. Rényi researchers aim to provide valuable tools across all levels of healthcare, from sector governance and care organization to direct patient care. The Rényi AI group is committed to continuing its work with domestic funding and developing scalable, implementable solutions.
In his presentation titled “The Patient Pathway Mission – Gaining Insight into National Healthcare Data with the Help of AI”, Dr. Miklós shared the latest research results at the annual event of the Hungarian Society for Health Care Management.
“With the development of artificial intelligence, tools such as structured data extraction, predictive modeling, and network analysis are now available to transform the previously fragmented mass of documents – structured within the database of the National Health Insurance Fund Administration (NEAK) as well as partially structured and coded within the National eHealth Infrastructure (EESZT) – into an analyzable patient life-course database. The goal of the ‘patient pathway mission’ is precisely this: to turn Hungary’s rich but currently underutilized healthcare data assets into a genuine resource for prevention, prediction, and decision support.”
Hungary’s healthcare data assets are significant even by European standards. The National Health Insurance Fund Administration has collected data on publicly financed healthcare events for more than 15 years. These include diagnostic procedures and physician – patient encounters as records of service events rather than detailed diagnostic or laboratory results.
In parallel, since its launch in 2017, the National eHealth Infrastructure has recorded an extremely large amount of healthcare documents, including outpatient reports, discharge summaries, medical findings, and e-prescriptions. The system now contains data connected to more than ten million citizens and processes millions of transactions daily. |
Dr. Miklós Szócska, Director of the Health Services Management Training Centre at Semmelweis University and visiting lecturer at Harvard University, similarly emphasized in his presentation “System Capabilities of Data-Driven Healthcare Solutions – An International Overview” that the new so-called iEESZT could become the central “engine” of Hungary’s healthcare system. It represents not merely a data warehouse, but an intelligent decision-supporting infrastructure.
Although the NEAK database is based on financing, Rényi mathematicians believe it is suitable for identifying similar patient pathways and supporting evidence-based health policy decisions. It can also help fine-tune national screening programs and refine their protocols. Preventive health screening attendance rates significantly influence disease-specific mortality rates in different Hungarian regions.
If temporal patterns, care pathways, and risk points become visible within the system, artificial intelligence may eventually be able to predict risk events for individual patients –such as missed follow-up examinations or dangerous drug interactions – based on their life-course data.
The ambition of the development extends beyond data transparency. The objective is to organize patient data into coherent patient pathways and to apply them in individual patient care, clinical decision support, administrative processes, and public health related decisions.
In the future, a general practitioner’s screen could display a clearly structured, chronological medical history – instantly usable not only by the GP but also by emergency services, out-of-hours care, or specialists.
“We leave digital footprints everywhere we receive healthcare services: in ambulances, at the GP’s office, in hospitals, in pharmacies, or even at home, and in many forms – outpatient reports, hospital discharge summaries, lab results, prescription redemptions, and more,” explained Miklós Dezső.
Over time, clinically useful systems can be built from existing data: the full medical history available at a click, with risks becoming predictable. Such a system would not merely store healthcare data, but interpret them and actively support treatment decisions.
Recognizing the importance of such developments, the Hungarian government announced a call titled Further Development of Mission-Driven National Laboratories, supported by a government decree. Researchers from the Rényi Institute’s AI group also applied.
“The mission aims to create an AI-based decision-support software capable of integrating, interpreting, and actively utilizing Hungary’s healthcare data assets,” Miklós Dezső shared at the conference.
The planned user interface will be a Hungarian development tailored to national characteristics. It will support physicians by gathering key influencing factors, calculating risks, and assisting decision-making – while leaving diagnostic tasks with the physician. The AI does not tire or become overloaded, even during peak periods such as epidemics.
Ultimately, the outcome of years of work at Rényi’s AI group and the software to be developed is to benefit patients who will receive more accurate diagnoses, more targeted therapies, fewer complications and an overall improved quality of life.
