Woman watching a video on a computer. Probably healthcare connected.

Digital healthcare – what do the terms exactly mean?

A prestigious international professional organization, ISPOR (International Society for Pharmacoeconomics and Outcomes Research) recently published an interesting study that examines the accuracy of the definitions used in digital healthcare. We interviewed one of the authors of the study, Dr. Zsombor Zrubka, who leads the Health Economics Research Center (HECON) at the University Research and Innovation Center.

How did the idea arise to examine the definitions of digital healthcare?

Dr. Zsombor Zrubka: It is the task of health economists to evaluate the clinical effectiveness of certain technologies and to inform decision-makers about the value of a technology based on this. That is, whether the health gain due to the technology and the costs are acceptable. In recent years, we have read more and more prestigious studies that summarized, for example, the effect of telemedicine on gastroenterology, or the effectiveness of mobile healthcare in cancer. Many studies came to mixed conclusions because the selected technologies or patient populations were overly heterogeneous. Thinking about the possible causes and consequences of this phenomenon, we came to the conclusion that we need to examine the definitions of the terms used in digital healthcare. How accurate are these definitions? How useful are they in health economic analyses?

Could you give an example?

Dr. Zsombor Zrubka: Yes. When determining the value of a technology, it is very important to define precisely the patient population, what exactly the intervention is, what we are comparing it to, and what criteria we are using to examine the health effect. For example, in the case of teledermatology diagnosis, it matters whether the doctor looks at a picture with the naked eye or whether artificial intelligence analyzes it. It also matters whether we are trying to recognize melanoma or dry skin, and whether we are examining this in individuals with white or black skin. For each digital technology, the patient group and goal for which the technology was developed must be precisely defined. Confusing the concepts can lead to undervaluing the effects of some technologies and overvaluing others. This can lead to effective technologies being introduced too late and scarce resources being wasted on technologies that are not effective enough.

What were the most important results of the research?

Dr. Zrubka Zsombor: With our international research group operating in the virtual space, we examined 545 review studies and collected the definitions of the four main concepts used in digital healthcare: digital healthcare, eHealth, mHealth (mobile healthcare), and telehealth/telemedicine. We found 142 different definitions for these four concepts, for example, the authors defined telemedicine in 52 different ways. The number of definitions in use has increased over the past five years. When we analyzed the content of these definitions in detail using text analysis methods, we concluded that the overlaps are significant and the concepts are too general, essentially unsuitable for describing the technologies in question with the precision expected by healthcare economists. We suggested that authors define much more precisely for which patients, for what purpose, under what circumstances, and to achieve what outcomes they use digital technologies.

What effect do you expect from this study?

Dr. Zrubka Zsombor: Obviously, one swallow doesn’t make a summer, but there are increasing numbers of studies that draw attention to methodological errors and quality deficiencies in publications related to digital healthcare or medical artificial intelligence research. For example, HECON also conducts research in this area within the framework of the TKP project. In recent decades, significant efforts have been made in the field of medical or pharmaceutical research to ensure that the results are presented in a unified, understandable, and as transparent manner as possible. There is still a lot to be done in this area as well, and hopefully a development process will also start in the field of digital healthcare, which will serve the better utilization of results and thus more effective care.

Will this research have a continuation?

Dr. Zrubka Zsombor: Yes, the second phase of the research is currently underway, in which we would like to reach a consensus with a wider panel of experts using the Delphi method on what should be the minimum criteria that need to be addressed when defining concepts related to digital healthcare so that they can be well-used in healthcare economic analyses.

We wish you further success!

Dr. Zrubka Zsombor: Thank you.

The HECON study was published in the prestigious English-language journal, Value in Health, and can be read in full at the link below:


EcoAction: successful semester!

With the support of the EIT, the first phase of the EcoAction project has been successfully completed. In addition to supporting innovation and entrepreneurship education, the Óbuda University (ÓE) also took on the communication of the project’s results, which strengthens the international visibility of the institution. This is the first EIT HEI grant that the university has won.

“This grant fits in with the processes that were started in the EIÖ – ÖKO1 project supported by the NKFIH, and continued in our ÖKO2 Innovation Ecosystem projects. We work together with our partners, the Finnish SeAMK – Seinäjoki University of Applied Sciences, who is the lead applicant, and the Hungarian Pannon Economic Network (PBN), to transform into an entrepreneurial university and to fully realize the results of the innovation ecosystem. We are proud that there is great interest from students and exciting start-up ideas are already taking shape,” said Gedeon Tímea, head of the University Research and Innovation Center (EKIK) Innovation Office and project manager of the grant.

The implementation period of the project is from July 2022 to June 2024. “In the period from July to December 2022, ÓE achieved significant results. Within the consortium, we shared the methodology of the innovation ecosystems existing at partner universities, organized several training programs and workshops both within and outside the university. On November 18, we invited the leaders who play a key role in implementing ÓE’s innovation strategy to a meeting where we jointly reviewed how the elements of the innovation ecosystem work in our institution. Lastly, on December 7, we organized the university’s industrial day, which was attended by the leaders of faculties and 50 industrial players,” summarized Dr. Zrubka Zsombor, the professional leader of EcoAction, the milestones of the past semester. The project’s English-language website and social media communication will soon be launched.

By joining the network of more than 270 organizations, the selected partners participate in the EIT Higher Education Initiative, which aims to support educational institutions with expertise, financing, and access to the EIT innovation ecosystem. The program has so far provided funding for about 50 projects. The goal of the EIT Higher Education Initiative (HEI) is to stimulate innovation in European higher education and support the dual transition towards a more sustainable, digital, and competitive Europe.

Az Óbudai Egyetem Ipari Napját hirdető oldal a Hírmondóból

Industry Day in the spirit of cooperation

Under the auspices of the EcoAction program, which aims to develop the Territorial Innovation Platform initiative and the university innovation ecosystem, an Industry Day was organized at the University of Óbuda’s University Research and Innovation Center (EKIK) on December 7th. At the event, which received great interest, the university faculties, representative deans, and vice deans presented previous good examples and detailed service packages that can strengthen cooperation between industry and academia. Dr. Zsombor Zrubka, the leader of the Health Economics Research Center (HECON) and the professional leader of the EcoAction program, was the moderator of the event.


ISPOR Europe 2022 konferencia jelkép.

ISPOR Europe 2022: HECON research results on public preferences and patient knowledge related to robot-assisted surgery in the field of orthopedic implants.

How does the Hungarian population feel about the increasing use of digital health technologies, including robot-assisted surgical procedures? How many people currently have orthopedic implants or implantable devices for bone fixation in Hungary? What is the level of patient knowledge about the devices they wear? These are some of the questions that the researchers at the Health Economics Research Center (HECON) at the University Research and Innovation Center (EKIK) recently sought to answer in a cross-sectional study.

Dr. Áron Hölgyesi, a researcher at the center, presented the preliminary results in the form of two poster presentations at the international ISPOR Europe conference, held in Vienna from November 6-9, 2022. The study provides new data on the prevalence of orthopedic implants and helps understand the relationship between patient knowledge about implantable devices and their impact on daily life. It also enables the patient-centered evaluation of the social acceptance of robot-assisted surgery.

In the first part of the study module, the self-reported frequency of orthopedic implants and bone fixation devices was found to be high, with 9.4% of participants living with at least one such implant. The participants’ average knowledge regarding the general use and safety of the implanted device, as well as their ability to recognize medical problems requiring assistance, was similar. However, higher levels of knowledge were observed among older respondents, and knowledge was positively correlated with the impact of the implanted device on daily life (r=0.262, p<0.01).

In the second part of the study module, public preferences regarding robot-assisted surgery were determined using the willingness-to-pay method. Participants were asked to choose between hip replacement surgery performed exclusively by a surgeon or with the assistance of a special robot arm and to determine how much they would be willing to pay to undergo the chosen procedure. In the total sample, a higher proportion of participants chose surgery performed with the assistance of a robot (54.4% vs. 45.6%). The amount offered for the two procedures did not differ significantly between the two groups, but higher levels of willingness to pay were observed among older respondents and those with higher incomes.

As digital technologies continue to penetrate healthcare, the use of robotic solutions is becoming increasingly widespread in various fields, including orthopedics. The study aims to contribute to a better understanding of patient attitudes and preferences regarding these innovative technologies. The original English abstracts related to the posters can be accessed through the following links:

New possibility for comprehensive evaluation of musculoskeletal health status: the Musculoskeletal Health Questionnaire (MSK-HQ)

Considering domestic and international prevalence data, musculoskeletal diseases are among the most common chronic diseases, which often come with significant pain and functional impairment, causing limitations and a drastic decrease in the quality of life of patients. Due to the significant burden of illness associated with musculoskeletal diseases, examining musculoskeletal health and related quality of life is of paramount importance, as it allows for a comprehensive assessment of individual healthcare services and the therapeutic field beyond assessing the patients’ condition.

Recently, to meet these needs, the University of Oxford has developed the Musculoskeletal Health Questionnaire (MSK-HQ), which reliably and sensitively measures respondents’ musculoskeletal symptoms and quality of life, and can be generally applied to a wide range of musculoskeletal diseases. Since its release, the questionnaire has been translated into several languages and its validity has been confirmed for various patient groups struggling with musculoskeletal problems. Given the significant international results achieved, it was important for MSK-HQ to become available in Hungary as well.

The development of the Hungarian version and its applicability was carried out by the HECON team led by experts. During the research, the members of the research group created the Hungarian version of MSK-HQ and then investigated its measurement properties in a cross-sectional population survey. Additionally, they assessed the general musculoskeletal status of the Hungarian population, the frequency of various musculoskeletal problems, and the health status of groups struggling with different problems relative to each other. They also sought to determine what basic properties affect individuals’ musculoskeletal health and quality of life.

Overall, the results support that the Hungarian version of MSK-HQ is a valid, reliable, and appropriately applicable questionnaire, with measurement properties similar to previously reported results in the literature. The number of people with musculoskeletal problems is significant in the Hungarian population. In the sample, significantly better MSK-HQ values were found for men, those living in the capital, those with higher incomes, and those with higher levels of education.

As a result of the work of the participants in the research, we have published population reference values for the MSK-HQ measure, which is now available and applicable in both clinical trials and routine clinical practice for recording and monitoring the condition of musculoskeletal patients, as well as for surveying the musculoskeletal health of the Hungarian population at the population level. The results obtained in this way provide, among other things, the possibility of comparing Hungarian patients internationally. Furthermore, the use of the tool may also serve quality assurance purposes in the future, as it is excellently applicable to assessing the effectiveness of healthcare services targeting musculoskeletal patients, as well as comparing the effectiveness of individual services even between institutions. Thus, the results of the Hungarian survey contribute significantly to both clinical and health policy decision-making.

The original English-language press release is available at the following link:


Innovation management in the health industry

It was the topic of the video available at the following, which was made on the occasion of the 60th Economist Conference by the Health and Healthcare Economics Section of the Hungarian Economic Association (MKT). The conversation was moderated by Prof. László Gulácsi, and colleagues of the HECON Health Economics Research Center participated: Prof. Márta Péntek, President of the MKT Health and Healthcare Economics Section, Doctor of the Hungarian Academy of Sciences, and university professor; Dr. Zsombor Zrubka, associate professor and head of HECON; and Dr. István Szabó, associate professor and scientific and international vice-president of the National Research, Development and Innovation Office.

Beteg egyeztet az orvosával. shared decision making

Patient-centered healthcare and shared decision-making: an international and domestic overview

Patient-centered healthcare involves involving the patient in decisions related to their health (called “shared decision making” in English). Shared decision making is a fundamental requirement in healthcare and many medical guidelines include it as a key principle. There is intense scientific research being conducted in this area. For shared decision making to be effective, the patient needs to be well-informed about their illness, possible therapies, their expected outcomes, and the risks involved, and be able to communicate and cooperate during their treatment. It is equally important for doctors to understand the patient’s thoughts, preferences, subjective expectations regarding their health and treatment, and their experiences with healthcare and treatment outcomes. This requires the use of reliable metrics to assess and track changes in the patient’s perspective. Reliable sources of information and tools to facilitate information acquisition, understanding, and communication are also needed. Internet-based decision-making tools, advanced technical solutions, and IT solutions are therefore playing an increasingly important role in shared decision making in healthcare.

A special issue of the ZEFQ journal was published on the occasion of the 2022 International Shared Decision Making Conference (https://www.zefq-journal.com/issue/S1865-9217(22)X0005-6), which provides an international overview of patient-centered healthcare and shared decision making in various countries.

A summary was prepared on behalf of the Health Economics Research Centre (HECON, EKIK, ÓE) in Hungary, which can be found here:


Prof. Péntek Márta portréja EULAR

One of our colleagues is at the forefront of European musculoskeletal research.

Professor Márta Péntek, the leader of the Health Economics Research Center (HECON) within the University Research and Innovation Center at Óbuda University, has been elected to the newly-formed Epidemiology and Public Health Research Subcommittee of the European Alliance of Associations for Rheumatology (EULAR), based in Switzerland. EULAR represents European scientific societies focused on rheumatology.

This international research team, consisting of distinguished experts, is involved in two exciting research projects at the EULAR Research Center: the establishment of a “Rheumatology and Musculoskeletal Disease Registry,” and the creation of a database called “RheumaFacts,” which provides up-to-date information on the health status, economic aspects, disease burden, care, inequalities, and research needs related to rheumatological and musculoskeletal diseases in Europe.

The research team is also responsible for developing new guidelines in areas such as expertise, research infrastructure development, training and education on the epidemiology and social aspects of rheumatological and musculoskeletal diseases.

The election of Prof. Márta Péntek DSc is not only a significant professional recognition but also an exceptional opportunity to represent scientific research at Óbuda University within this high-prestige European professional organization and to expand international research and development connections.

Our health: how much does it cost?

In the healthcare industry, innovation is happening so rapidly that decision-makers worldwide are increasingly forced to weigh which new treatments and technologies can be financed by healthcare budgets or health insurers. Healthcare economists are also increasingly being called upon to answer the question that concerns many people: how much does our health cost?

This is the basis for the latest research conducted by the HECON Health Economics Research Center, which operates at the University Research and Innovation Center of Óbuda University in Hungary. The center has developed a new method for estimating missing healthcare cost data for countries in the Middle East region. One frequent problem is that there are no adequate cost data available in a given country for making a particular decision. This is particularly true in rapidly developing and changing healthcare systems where reliable data from the past are not available to evaluate newly introduced therapies. Research experience and methods gained in the post-communist transformation of Eastern European healthcare systems can therefore be valuable to other regions as well.

During the research, the center’s staff worked with students, practicing professionals, and the internationally recognized leading authority in the field from the region to develop a simple formula that, based on costs from the region and the countries’ economic performance, can estimate missing cost data. To do this, all cost data reported from the region had to be collected and systematically analyzed. The formula provides assistance to practitioners to prepare the most accurate cost estimates possible, even with minimal data available. The research also highlights that even the most accurate estimates obtained through this method are only rough approximations in many cases, and therefore, targeted research performed with appropriate methodology should be considered for important decisions.

In digitally transforming healthcare systems, a large amount of data is generated, and modern data analysis methods can provide increasingly reliable answers to research questions. The measurement of healthcare costs requires particularly detailed and accurate data collection, as the treatment of each patient can vary greatly depending on the patient’s condition and institutional characteristics. The original announcement was published in the prestigious PharmacoEconomics journal, and can be read here.

Egészségügyi digitalizációját ábrázoló szimbolikus kép az emberi és a digitális kéz találkozásában mutatva. Michelangelo Ádám teremtése

What do machines know about our health?

The question investigated by a research group consisting of staff from the Health Economics Research Center (HECON) at the University Research and Innovation Center of Óbuda University, Eötvös Loránd University (ELTE), Budapest Corvinus University and foreign research institutions, published in a prestigious international journal, is how accurate and reliable modern machine learning methods are in providing an individual’s quality of life without actually measuring it. The researchers analyzed the anonymous data of more than 26,000 patients from 30 quality of life studies conducted in Hungary, Poland, and Slovenia using traditional and state-of-the-art machine learning methods. Most of the data was used to teach the algorithms, and a smaller part was used to verify the results. In four to five out of ten cases, the life quality was estimated with such accuracy that the differences were not noticeable from the individual’s perspective. However, in four out of ten cases, the system did not accurately predict whether the person felt completely healthy, moderately ill, or seriously ill. Although there were differences between the various methods in terms of providing more accurate estimates for healthy or sick individuals, overall, traditional and machine learning methods performed similarly. The research’s conclusion is that we still do not have enough high-quality data on people’s quality of life to use machine learning algorithms reliably and accurately. The first step is to collect good quality quality of life data from as many points in the healthcare system as possible, create patient registers, and establish a legal framework and secure infrastructure. To extract valuable information from the collected data in the future, data scientists, doctors, and health economists must work together. The digitization of healthcare and the proliferation of digital medical devices are generating an increasing amount of healthcare data worldwide. The volume and complexity of the resulting data mass are such that it can only be processed using machine learning and artificial intelligence methods. The advantage of machine learning methods over traditional data analysis techniques often represents a disadvantage. In the case of traditional data analysis methods, researchers formulate their hypotheses and obtain answers to their questions through analyses based on these assumptions. Since the question is known, the answer obtained is usually well interpreted. However, with machine learning methods, it is often not possible to understand how the result is reached, as these methods do not rely on a priori assumptions.

Compared to traditional data analysis techniques, the advantage of machine learning methods often comes with a disadvantage as well. In the application of traditional data analysis methods, researchers formulate their hypotheses and obtain answers based on these hypotheses. Since the question is known, the resulting answer is usually interpretable. However, in the case of machine learning methods, the results are often not explainable. In some areas of use, this is not a problem, for example, the quality of a photo can be visibly improved without precise knowledge of the calculations running in the background. However, it raises questions about relying on the results of analyses that are not or only partially interpretable for health-related decisions.

Today, algorithms are capable of providing more accurate diagnoses based on X-rays or photos of moles than doctors. Medical decisions are often influenced by how patients feel and their quality of life. Precise knowledge of individuals’ quality of life is fundamental in decisions related to healthcare financing.

Authors: Dr. Zsombor Zrubka, Prof. Márta Péntek, Prof. László Gulácsi, Jani Kinga, Óbuda University, Faculty of Economics, 1034 Budapest, Bécsi út 96/B

Source: https://hecon.uni-obuda.hu The original English-language article, “Predicting Patient-Level 3-Level Version of EQ-5D Index Scores From a Large International Database Using Machine Learning and Regression Methods” by Zsombor Zrubka, István Csabai, Zoltán Hermann, Dominik Golicki, Valentina Prevolnik-Rupel, Marko Ogorevc, László Gulácsi, and Márta Péntek, can be found at this link.