Background: Slovakia adopted a comprehensive hospital network reform in 2021 (Act 540/2021 Coll.) under its Recovery and Resilience Plan (RRP), motivated by a fragmented network of small, low-volume hospitals and limited transparency on services, quality and waiting times. Reform Content: The reform reorganises hospitals into five statutory levels, each defined by a portfolio of medical programmes, maximum waiting times, minimum case volumes and quality indicators. This paper provides a policy and regulatory overview of the reform’s design, its content and the trajectory of its implementation. Expected Results: The reform aims to concentrate complex care, improve clinical outcomes, reduce variation, shorten waiting times and improve efficiency. Although the legislative architecture was largely in place by end-2023, core operational pillars are still unfinished: the quality-indicator decree was only adopted in mid-2025, with first availability for categorisation in 2027; staffing-norm legislation has yet to enter the legislative process; minimum-volume thresholds have been repeatedly revised downward; and no provider has so far been excluded from the network on volume grounds. Policy Implications: The roll-out has been affected by political interference, repeated amendments, delays and politically driven re-categorisations, illustrating the gap between a legalistic design and its political execution. Policy implications stress the importance of aligned payment systems, sustained stakeholder engagement and systematic evaluation against the reform’s stated objectives. Outcomes for efficiency, waiting times and treatable mortality will be the subject of future quantitative research as administrative data become available.
Artificial intelligence (AI) is increasingly used in health care to improve the quality of care, support clinical decision-making, and increase efficiency. As AI systems are integrated into healthcare workflows, they can also become involved in how work is managed, including directing tasks, coordinating workflows, monitoring activity, and evaluating performance. These managerial uses of digital and AI systems are commonly referred to as forms of algorithmic management. Such uses may contribute to the intended benefits of AI, but may also make work more standardised, measurable, and monitorable, potentially increasing managerial control, reducing professional autonomy, or intensifying work. Whether these consequences arise depends on how these systems are implemented and governed. Realising AI’s potential in health care therefore requires explicit choices about how AI is used within healthcare organisations and how gains from automation are allocated, so that benefits for care are maximised while potential harms are limited.
Background Volunteers can add value to healthcare by reducing professional staff workload or improving patients’ quality of life. Assessing the value of volunteers in healthcare could aid decision makers regarding investments in volunteer programmes. However, no comprehensive overview exists that systematically compares methods to quantify the value of volunteers. Objective To provide an overview of the different methods used to quantify the value of healthcare volunteers Methods CINAHL, MedLine and Embase were searched in April 2024 for articles that quantitatively evaluate the value of volunteers in healthcare. Results In total, 69 relevant full text articles were included. Most articles report on volunteers’ value for patients by measuring clinical outcomes or patient satisfaction. Economic evaluations from the perspective of providers frequently use an opportunity cost or replacement cost approach. The intrinsic value for volunteers is also often reported, e.g. by measuring their motivation or increase in knowledge. Few articles quantify the value for staff, family members of patients or society as a whole. Conclusions This review shows that a variety of methods can be used to quantify the value of volunteers, and from a range of perspectives: patients, family members of patients, volunteers, staff, healthcare providers and society. To gain a comprehensive understanding of the value of volunteers, methods from different perspectives should be considered as value may differ per perspective.
Background Value-based payment (VBP) schemes have emerged as alternatives or complements to traditional payment models, to realign financial incentives toward quality, efficiency, and patient-centered outcomes. Despite their rapid expansion, no recent review synthesizes both design features and reported impacts. Objective To provide an overview of recent VBP schemes, highlighting policy objectives, design parameters, and reported impacts, without focusing on specific countries or care settings. Methods A scoping review was conducted following JBI methodology. Searches in Medline (via PubMed) and Web of Science identified English-language peer-reviewed articles, systematic or scoping reviews published between January 2015 and July 2024. The review focused on large-scale VBP schemes implemented after 2010 or active between 2010 and 2024. Studies were included if they described a VBP scheme and/or evaluated its impact using quantitative evaluation methods. Gray literature supplemented information on identified models. Results We identified 61 VBP schemes across 15 countries: 38 pay-for-performance, 18 bundled payment, and 5 shared savings. Common objectives included improving cost efficiency (40%) and care coordination (38%). One-third targeted multiple care settings, and about half focused on multiple conditions. Among the 41 VBP schemes that underwent impact evaluation, positive effects were most frequently observed for clinical process (n = 14), followed by efficiency (n = 11), patient outcomes (n = 11). Conclusion Although VBP schemes hold considerable promise, the ability of these programs to deliver desired outcomes depends heavily on their specific design and the context in which they are implemented. Continuous evaluation and iterative refinement are essential to ensure these programs achieve measurable improvements in quality, efficiency, and patient experience.
Background Well-being in the healthcare sector is of substantial policy relevance, given persistent labour shortages and the central role of subjective well-being in worker productivity and retention. Objective We investigate changes in healthcare workers’ life satisfaction since the onset of the pandemic relative to workers in other essential and non-essential sectors, and examine the role of pandemic severity and subsequent policy responses in explaining these differences. Methods We use thirteen waves of quarterly panel data from the COME-HERE dataset, which collects information on respondents in five European countries from April 2020 to August 2024, and estimate linear regressions via OLS. Results The life satisfaction of healthcare workers is always higher than that of other essential workers and non-essential workers. During the pandemic, the pandemic death rate had a negative effect on life satisfaction that was three times larger for healthcare workers. At the same time, lockdown stringency reduced the life satisfaction of workers in every sector other than healthcare. Conclusions As pandemic death rates and lockdown stringency mostly moved hand-in-hand, the higher life satisfaction of healthcare workers persisted throughout and after the pandemic possibly reflecting the meaningful nature of their jobs. At the same time, the negative impact of pandemic severity on workers’ well-being is especially pronounced among healthcare workers, highlighting the importance of supporting their well-being during public health crises to sustain healthcare system performance.
