Part I — Situation overview

Three mutually reinforcing pieces of news about Hungarian healthcare came together on 24 and 25 August 2026. According to a recent analysis by GKI Gazdaságkutató Zrt., Hungarian healthcare and the social sphere are persistently underfunded in EU comparison: in both 2023 and 2024 Hungary spent the least on healthcare relative to GDP of the 27 EU member states, 4.7 per cent of gross domestic product (GDP), against an EU average of 7.4 per cent. In the Czech Republic this ratio was 9 per cent, in Slovakia 6.9 and in Poland 6.1 per cent. The analysis also records the fall in expenditure on social protection: from 17.3 per cent in 2010 it fell to 12.3 per cent of GDP by 2024, which is the lowest value in the EU ranking apart from Malta and Ireland. In the same days the health minister Zsolt Hegedűs announced that the implementation of the waiting list reduction programme starts in the autumn, and that the relationship between state and private healthcare will also be regulated. Third, the Hungarian Medical Chamber (MOK) published its assessment of the first hundred days after the setting up of a separate health ministry.

MOK’s assessment is deliberately two-directional. On the positive side it lists the setting up of a separate health ministry, the start of the transformation of the background institutions, the raising of primary care, public health and digital healthcare to the level of separate state secretariats, the removal of facial recognition cameras from hospitals, the abolition of VAT on medicines, and two transparency steps: the lifting of the ban on hospitals making statements and the publication of hospital infection data. On the critical side the body urges an acceleration of the replacement of institutional heads, and names three concrete gaps: there is no comprehensive survey of the current state of the care system, which could serve as the starting point for later measurement of results; there is no detailed plan on what and in what distribution the government will spend the HUF 500 billion a year of extra resources promised in the election programme; and there is no public timetable for settling doctors’ pay. MOK also gives a graphic example of the problems of the inherited structure: the National Health Insurance Fund Manager (NEAK) and the National Hospital Directorate General (OKFŐ) previously had no access to the Electronic Health Service Space (EESZT), and could therefore not meaningfully link care data with financing information. According to the minister’s reply, the rebuilding of the health system will not be a single spectacular turn but a longer process built on milestones.

In the background a fourth, long-term set of data also appeared: according to Portfolio’s survey, interest in general medicine has grown further at all four Hungarian medical universities — at Semmelweis University the number of those admitted rose in five years from 403 to 517 — but the students admitted now will obtain their degree in 2032 at the earliest, and may appear as independent specialists between 2036 and 2039. The universities indicated unanimously that training capacity — infrastructure, teaching staff, practice places — is already at its maximum. On MIAK’s reading the three threads — the level of resources, access and the workforce — are not separate problems but meet in a single question of measurement: the result of the waiting list reduction programme now starting will be judgeable only if the baseline is fixed and the methodology of measurement is public. Otherwise a “falling waiting list” can also be produced by administrative reclassification, and the debate will once again be about the credibility of the figures rather than about patients’ waiting time.

Part II — Foundations in the literature

Before turning to MIAK’s concrete proposals it is worth fixing the framework in the literature. The joint publication Health at a Glance — Europe 2024 of the OECD (the economic cooperation organisation of the developed market economies) and the European Commission measures access not by the length of the waiting list but by the indicator of unmet medical need: by how many people say that because of cost, distance or waiting time they did not receive necessary care — and the volume shows that this burden is on average more than three times higher in the lowest income group than in the highest. The publication Policy Brief 66 of the European Observatory on Health Systems and Policies (a policy summary built on the results of the Magnet4Europe research programme) systematises the evidence on nurse retention, and shows that professional career paths and an organisational structure supporting teamwork measurably improve retention — this is the empirical basis for handling the Hungarian workforce shortage. And the World Bank’s health financing analyses add the distinction between technical and allocative efficiency: the former measures how much the production of a given service costs, the latter whether the composition of services corresponds to the actual needs of the population — from which it follows that raising the level of resources does not on its own improve outcomes if the structure of expenditure remains unchanged. The detailed treatment of the literature — author by author, with quotations — can be found in section 6.4 Literature in detail.

Part III — MIAK’s concrete proposal

MIAK proposes three measurable measures which together make the programme now starting auditable.

3.1 Public waiting list data at intervention level, simultaneously with the start of the programme (autumn 2026)

MIAK proposes that simultaneously with the start of the waiting list reduction programme a public, machine-readable waiting list data service should be set up, publishing by institution and by intervention the median wait (the waiting time of the middle patient, which is less sensitive to extreme cases than the average), the number of those on the list, and — this is the key element — the reason for coming off the list: whether the intervention took place, whether the patient moved to another institution, whether they withdrew, or whether they were deleted for administrative reasons. This is the direct application of the E3 waiting list transparency programme point, and its technical basis is the E2 digital health system. Without publication of the reason for coming off the list, a shortening of the waiting list cannot be interpreted: the same figure can be produced both by more operations performed and by more administrative deletions. The data connection problem signalled by MOK — that the financing body and the hospital maintainer previously had no access to the electronic health data space — is precisely the obstacle whose removal makes this publication technically possible.

3.2 A baseline survey by the start of the programme, with a public methodology (autumn 2026, before the programme starts)

The most important policy observation in MOK’s assessment is that there is no comprehensive survey of the current state of the care system that could serve as a starting point for measuring later results. MIAK shares this, and proposes that before the programme starts a baseline survey with a public methodology should be completed, recording at least the following: capacity and utilisation by institution, the number of unfilled medical and nursing posts broken down by age group, the median wait for the twenty highest-volume interventions, and the estimated share of health expenditure paid out of one’s own pocket (out-of-pocket expenditure). According to the logic of the G20 impact assessment system (Drucker audit — a subsequent balance sheet of whether a measure really brought the expected result), subsequent assessment is possible only if the prior state is documented; in the case of a programme starting without a baseline survey, both success and failure will be assertable, and neither will be refutable.

3.3 A public distribution plan by purpose for the HUF 500 billion a year of extra resources (autumn 2026, with the 2027 budget)

MOK calls the itemisation of the use of the HUF 500 billion a year of extra resources promised in the election programme a key question. MIAK proposes that this distribution should appear together with the 2027 budget bill, broken down by purpose — separated at least into the following items: waiting list reduction (capacity purchase and extra shifts), settlement of nurses’ pay and working conditions, primary care and prevention, digital infrastructure. The E6 nurse retention package and the E4 prevention data programme acquire budgetary content here. The allocative efficiency argument of the World Bank analyses (see 6.4.3) speaks for this breakdown not being a formality: the underfunding of Hungarian healthcare is real, but the return on the extra resources depends on which point of the system they reach. And according to the principle of the G1 data-driven budget, this same breakdown is at the same time the basis of the following year’s accounting.

The three proposals are linked by a single principle: the waiting list is not primarily a capacity problem but also a measurement problem. As long as there are no public, intervention-level data and no fixed baseline, one can only believe something about the programme’s result — according to MIAK, in healthcare that is not enough.

Part IV — Expected effects and risks

Dimension Expected effect Risk
Healthcare The patient sees where the queue is shorter and can use the possibility of choosing an institution; the programme’s result becomes auditable Public data create ranking pressure: institutions may be given an incentive to move easier cases forward, which may increase the waiting of more seriously ill patients
Economy The breakdown of resources by purpose links the extra resources to a measurable outcome and lays the basis for the following year’s accounting A detailed breakdown may make the distribution rigid: if a need arises elsewhere during the year, reallocation carries a political cost
Employment policy The publicity of post and age structure data makes visible where there is a real shortage, and makes the retention programme targeted The publicity of shortage data damages the reputation of certain institutions in the short term, which may accelerate the outflow of workers from where there are already few specialists

The main point of judgement is the choice of indicator. Public waiting list data work if, alongside the median wait, the reason for coming off the list is also published — for without this exactly the distortion appears that international experience regularly shows: the optimisation of list management instead of an improvement in actual care. The proposal tips to the risk side where publicity becomes a mass of data uninterpretable for the patient; MIAK therefore proposes, alongside the raw data, a simple patient-side view as well. The workforce thread is a separate question of timing: the expansion of university admissions gives no answer to the present shortage, because the effect appears after 2032 — the capacity of the next five to ten years depends exclusively on retaining the specialists already in the system.

Part V — Measurability and summary

5.1 What is worth following? (proposed KPIs)

MIAK proposes the following performance indicators (KPIs, Key Performance Indicators) for monitoring — these are proposals, not government decisions:

  • Appearance of public waiting list data: whether they are available in machine-readable form, broken down by institution and intervention, together with the reason for coming off the list (yes/no, fourth quarter of 2026).
  • Median wait for the twenty highest-volume interventions: worth following a fall of at least 20 per cent within 12 months, alongside an unchanged or growing number of interventions performed (end of 2027).
  • Unmet medical need: whether the Hungarian value measured in the EU survey (EU-SILC) and the difference between the lowest and the highest income quintile are falling (2028 data release).
  • Health expenditure relative to GDP: whether the Hungarian value moves from the level of 4.7 per cent towards the EU average, and whether the accounting of the extra resources by purpose appears (annually, from 2027).
  • Unfilled nursing and medical posts: whether the number of unfilled posts is falling, especially in positions covered by colleagues over 65 (annually, from 2027).

5.2 Summary

MIAK’s request to the decision-maker is simple: simultaneously with the start of the waiting list reduction programme, an intervention-level, public waiting list data service should be set up, the baseline survey should be completed, and together with the 2027 budget the distribution plan by purpose for the HUF 500 billion a year of extra resources should appear. And of the public it asks that in judging the programme it should look not at the sum announced but at the median wait and at the evolution of unmet medical need — the former measures intention, the latter two measure results.

This request follows from two MIAK foundational values. Data-drivenness is engaged because healthcare is the area in which the absence of measurement most quickly turns into political dispute: without operations performed and patients deleted from the list, the same figure can be presented as success and as failure alike. And universal representation, because according to the EU data the lack of access is not evenly distributed: unmet medical need is several times higher in the lowest income group, which means that the question of waiting lists is not a technical but an equity question — those who can, pay and move forward; those who cannot, wait.

Part VI — Justifications and further sources

6.1 The framing of the press, spectrum by spectrum

The liberal-left band placed the emphasis on the level of financing. HVG processed the GKI analysis in a separate article and highlighted the EU comparison in the headline as well; the structure of the text starts from the disproportion in the structure of public expenditure — it is about not simply too little money but a bad distribution, and it names healthcare as its “most striking example”. This framing essentially anticipates the question of allocative efficiency, even if it does not use the technical term. Telex ran the ministerial announcement, focusing on the start of the programme and on the regulation of the relationship between state and private healthcare.

The economic band brought in the workforce and time horizon thread, which barely appeared in the other bands. Portfolio was present with three separate articles: one processed the minister’s hundred-day assessment, the second MOK’s criticism, the third the admission data for medical training. This last is the most valuable contribution to the day’s material, because it alone makes clear that the students entering now will be independent specialists between 2036 and 2039, and that training capacity is already at its upper limit — that is, an improvement in admission numbers gives no answer to the present care situation.

Népszava ran the thread of the MOK assessment and the shortage of doctors, but on that day only a title-level reference can be made to it. On this day the pro-government and conservative band did not bring the health topic into the daily focus: Magyar Nemzet and Mandiner dealt with the budget and the asset recovery matters. The overall picture of the spectrum is thus asymmetrical: detailed material was produced on the level of financing and on the replacement of the workforce, but on the implementation risks of the programme — on the methodology of measurement, on the possibilities of distortion in list management — in none of the bands. MIAK’s analysis deliberately fills this gap.

6.2 Facts and data

Indicator Value Source
Hungarian health expenditure (relative to GDP, 2023–2024) 4.7% — the lowest of the 27 EU member states GKI Gazdaságkutató, HVG, 25 August 2026
EU average 7.4% GKI Gazdaságkutató
Czech Republic / Slovakia / Poland 9.0% / 6.9% / 6.1% GKI Gazdaságkutató
Expenditure on social protection (relative to GDP) 17.3% (2010) → 12.3% (2024) GKI Gazdaságkutató
Public expenditure (relative to GDP) 48.9% (2010) → 47.1% (2024) GKI Gazdaságkutató
Public revenue (relative to GDP) 44.5% (2010) → 42.6% (2024) GKI Gazdaságkutató
Promised annual extra health resources HUF 500 billion / year MOK’s hundred-day assessment, Portfolio, 25 August 2026
Semmelweis University — admissions to general medicine 403 → 517 (in five years) Portfolio, 24 August 2026
Graduation of the students admitted now 2032 at the earliest Portfolio
Becoming an independent specialist 2036–2039 Portfolio
Unmet medical need in the EU (2023) 2.4 per cent of the population OECD – European Commission: Health at a Glance — Europe 2024

Two pairs of data in the table call for separate interpretation. The GDP ratio of public expenditure essentially did not change between 2010 and 2024 (from 48.9 to 47.1 per cent), while the share of healthcare and social protection fell substantially — so the state did not spend less overall, it spent on something else; the problem is therefore not simply a shortage of resources but a series of structural decisions. And the training data pair shows that the time horizon for replacing the workforce is 6–13 years: the growing popularity of the medical career is welcome, but it has no effect on the care situation of 2026 and of the early 2030s.

6.3 Policy dimensions

  • Healthcare (programme points) — waiting list transparency, the digital health system and the nurse retention package are directly engaged; the centre of gravity of the topic.
  • Economy (programme points) — the data-driven budget and subsequent impact assessment give the framework for the accountability of the extra resources.
  • Employment policy and the labour market (background material) — the age structure of the health workforce and the time horizon of its replacement is also a labour market question.

6.4 Literature in detail

6.4.1 OECD – European Commission: Health at a Glance — Europe 2024

For measuring access the volume uses not the length of the waiting list but the indicator of unmet medical need, which it calculates on the basis of the EU survey on income and living conditions (EU-SILC). In interpreting the results the authors place particular emphasis on the fact that the burden is not evenly distributed:

“While unmet needs for medical care for financial, waiting time or geographic reasons remained at relatively low levels in most EU countries in 2023, they have increased since 2019. These unmet needs are on average more than three times higher in the lowest income group than in the highest.”

The volume’s methodological warning is also important: if the calculation is narrowed to those who actually had a care need — that is, if those who did not need a doctor are left out — the share of unmet need rises significantly in every country. This can be applied directly to the measurement of the Hungarian waiting list reduction programme: a fall in the number of those on the list says nothing in itself if at the same time the circle grows of those who never even reach the list. MIAK’s proposal 3.1 — mandatory publication of the reason for coming off the list — makes precisely this distortion visible, and the unmet need indicator proposed in point 5.1 also takes into account the population outside the list.

📖 Source: OECD – European Commission: Health at a Glance — Europe 2024

6.4.2 European Observatory on Health Systems and Policies: Policy Brief 66 — Strengthening Europe’s Nursing Workforce

The publication systematises the evidence on retaining the nursing workforce, and its most important observation is that the effect of interventions depends strongly on the organisational context: new roles and task-sharing models brought results where the organisational structure also supported teamwork, and where professional advancement appeared as a real possibility. The publication highlights the same conditionality for technological investment as well: systems easing access to data may improve satisfaction with work, but too often they do not take into account the actual work processes of their users. For the Hungarian situation this yields two conclusions. One is that the E6 nurse retention package cannot be reduced to a question of pay: the professional career path and organisational autonomy matter at least as much. The other is that the data connection gap objected to by MOK — the shutting out of the financing body and the hospital maintainer from the electronic health data space — is not only an accounting but also a workforce retention question: badly designed or missing systems consume health workers’ time.

📖 Source: European Observatory on Health Systems and Policies: Policy Brief 66 — Strengthening Europe’s Nursing Workforce

6.4.3 World Bank: Health financing reports

The World Bank’s health financing analyses consistently separate two types of efficiency. Technical efficiency is about how much the production of a given service costs — at this level cost awareness and the flexibility of contracts concluded with providers bring improvement. Allocative efficiency, by contrast, measures whether the composition of services and expenditure corresponds to the actual needs of the population; the volumes also link this to equity, because better targeted expenditure reaches vulnerable groups more effectively. The analyses separately highlight the high share of expenditure paid out of one’s own pocket (out-of-pocket expenditure) as an indicator of the lack of financial protection, and recommend the merging of risk pools as a solution.

This distinction can be applied directly to the Hungarian debate. GKI’s data — expenditure of 4.7 per cent of GDP against an EU average of 7.4 per cent — is a problem of the level of resources, and MIAK does not dispute that this has to be raised. The World Bank’s argument, however, warns that the return on the extra resources depends on which point of the system they reach: if the surplus flows into acute hospital care while primary care and prevention remain underfunded, outcome indicators improve more slowly than spending grows. MIAK therefore proposes in point 3.3 a breakdown by purpose — not as a limitation on the extra resources but as a way of making their return measurable.

📖 Source: World Bank: Health financing reports

6.5 International comparison

EU experience of waiting list management points in two mutually complementary directions. In the Nordic and Baltic systems — where waiting time has traditionally been the main constraint on access, not cost — the solution has typically been the institution of a guaranteed maximum waiting time: if a patient does not obtain a given intervention within the time fixed in law, they acquire an entitlement to have it performed at another institution, even under different financing. This model works because public, intervention-level data are its precondition — the entitlement can be enforced only if waiting time is credibly measured. In the Hungarian situation this connection also holds in reverse: MIAK’s proposal 3.1 on measurement is not an end in itself but the technical condition of a later entitlement system. At the same time the OECD volume also warns that unmet medical need has risen in most EU countries since 2019, that is, the problem is not a Hungarian peculiarity; what is a Hungarian peculiarity is the lowest level of expenditure in the EU field. The second direction is the workforce-side answer: in recent years the Western European systems have increasingly placed the emphasis on retention and task sharing instead of expanding training numbers, precisely because — as the Hungarian admission data also show — the turnaround time of training is of the order of a decade, while that of retention interventions is one or two years.

Healthcare

  • E2 — Digital health system
  • E3 — Transparency of waiting lists
  • E4 — Prevention data programme
  • E6 — Nurse retention package

Economy

  • G1 — Data-driven budget
  • G20 — Economic policy impact assessment system (Drucker audit)

Proposed new programme point: Baseline survey of the care system — for the Healthcare area: a mandatory survey with a public methodology before the start of every larger health programme, as the authenticated starting point for later measurement of results.

6.7 List of sources

Press sources (MIAK press monitor, 25 August 2026 — topic 3):

Knowledge base references (literature):

  • 📖 OECD – European Commission: Health at a Glance — Europe 2024
  • 📖 European Observatory on Health Systems and Policies: Policy Brief 66 — Strengthening Europe’s Nursing Workforce
  • 📖 World Bank: Health financing reports

Note: in the visible text of the blog only the author and the title are given for the books; the local file path is an internal matter of generation.

MIAK internal materials:

  • MIAK policy area: Healthcare (programme points; programme point ID: E3)
  • MIAK policy area: Economy (programme points; programme point ID: G20)
  • MIAK policy area: Employment policy and the labour market (background material)
  • MIAK press monitor, 25 August 2026 — topic 3, score: 90/100

Supplementary public data sources:

  • GKI Gazdaságkutató Zrt. — EU comparison of health and social expenditure, August 2026; Eurostat EU-SILC — unmet medical need; Eurostat SHA — system of health accounts; NEAK — financing data; KSH — health statistics; MOK — assessment of the first hundred days, August 2026.

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