International Journal of Health Planning and Management最新文献

The second Trump administration's centrepiece legislation, the modestly-named Big Beautiful Bill, passed by the House of Representatives and going through the Senate at time of writing, offers an opportunity to reflect upon how the U.S. state affects health policy and the prospects for equitable access to affordable healthcare. Is the U.S. still an outlier (by comparison with Europe and much of the world), in that its many of its citizens are either uncovered, poorly covered or tenuously and only temporarily covered by health insurance? The answer is yes. And the chipping away at Obamacare and Medicaid by Trump 2.0 (learning from his failure to repeal Obamacare in 2017) as part of the Big Beautiful Bill, shows us that it is easier for the Right to dismantle progressive social legislation than it is for the Liberal-Left to assemble it. To understand why, and to revisit why the U.S. polity struggles to enact progressive healthcare reform, we have to understand the effect of the U.S. state (i.e. political structure) upon public policy. This article revisits the nature of that state, to depict the underlying causes of ‘American exceptionalism’ which are partly ideological but also more significantly institutional than often realised.

Support workers (SWs) form the largest section of the NHS workforce, and the ongoing NHS workforce crisis underscores the need for their efficient utilisation. This study explored the deployment of imaging SWs within NHS radiology departments in England, forming part of a larger multiphase research project funded by the National Institute for Health Research (NIHR). It involved multi-centre case studies across nine radiology departments, employing a thematic analysis of focus groups and interviews with a range of radiology staff, including SWs themselves (n = 113). Results showed that recruitment of SWs was generally not challenging, however, retention was variable potentially due to limited opportunities for career progression and a lack of role understanding. Deployment strategies varied significantly across sites, which sometimes influenced SW effectiveness and were often selected for service need rather than SW development. Role scope was often unclear and training inconsistent which may exacerbate poor role understanding, and the lack of clear career pathways outside of professional registration conflicted with SWs' strong desire for progression. SWs are highly valued, crucial to operational efficiency and excellent patient care. Efficient deployment of SWs within NHS radiology services is crucial for alleviating workforce shortages and improving service delivery, however, this is impeded by the variability in role definition and deployment practices evidenced in this study. Standardising role titles, responsibilities, and training, and creating clear progression pathways could help to fully harness the capabilities of SWs in healthcare settings. National frameworks offer recommendations for standardisation, but this study suggests implementation remains inconsistent or delayed.

Corruption in health systems diverts resources, erodes trust, and reduces service quality. Traditional oversight methods struggle to detect fraudulent patterns, but Artificial Intelligence (AI) offers new possibilities. AI can analyse large datasets to predict corruption risks and detect irregularities in procurement, insurance claims, and counterfeit medicines. Successful applications include AI-powered tools that flag suspicious transactions, expose bid-rigging in procurement, and identify fraudulent medical billing. AI can also complement other analytical tools to help track counterfeit drug supply chains through image recognition and network analysis. However, AI's impact depends on how it is deployed. Government-led AI initiatives may enhance transparency but risk reinforcing power imbalances or enabling authoritarian control. In contrast, civil society-driven efforts can empower citizens to hold authorities accountable but face challenges like limited data access and misinformation risks. Moreover, AI can also facilitate corruption in the health system through biased algorithms, deepfake propaganda, or manipulated AI-driven decision-making in resource allocation. Maximising AI's anti-corruption potential in healthcare requires investments in skilled personnel and data systems. AI should complement human oversight, with transparent auditing mechanisms to mitigate biases. Integrating blockchain and AI technologies may enhance accountability by securing procurement records and preventing data manipulation. While AI presents significant opportunities, its application to anti-corruption remains a political issue as much as a technological one. Careful governance, ethical and legal safeguards, and balanced implementation will determine whether AI combats corruption or exacerbates abuses.

In 2021, the World Health Organization (WHO) introduced the Health Workforce Support and Safeguards List, updating the 2010 WHO Global Code of Practice on the International Recruitment of Health Personnel. The change introduced a new way of defining what constitutes a country with a critical health worker shortage. The new calculations are based on a combined score of countries' health worker density per 1000 population and the Universal Health Coverage (UHC) service coverage index. It has led to an increase in the number of low- and middle-income countries (LMICs) considered at risk from active recruitment by high income countries (HICs). However, the 2021 WHO Safeguard list review failed to explicitly recognise the main causes of low health worker density in countries on the list. Many included countries are unable or unwilling to invest in their health sectors, which restricts the number of staff that can be hired. These countries experience high unemployment among trained and qualified health workers, despite their high need for health workers. Recent dramatic reductions in international aid and development support, means that LMICs that fail to invest in their health workforce, will face ever greater shortfalls in meeting the basic health needs of their populations. For WHO Safeguard-listed countries establishing bilateral health worker migration agreements, better support is needed to create fair deals that allow them to receive compensation from destination countries for the training costs of their emigrating health workers, which can be used to directly hire additional health workers back home.

The COVID-19 pandemic exposed critical weaknesses in global health governance, prompting the development of the WHO Pandemic Agreement, formally adopted by the World Health Assembly in May 2025. This landmark Agreement seeks to address the shortcomings of the 2005 International Health Regulations by establishing legally binding commitments to enhance pandemic preparedness, equity, and international solidarity. However, the negotiation process revealed deep geopolitical divisions, raising concerns about the Agreement's legitimacy and enforceability. Key provisions include improved surveillance, data sharing, protection for healthcare workers, and equitable access to medical countermeasures. Nevertheless, its effectiveness may be compromised by vague language, unresolved issues, and the absence or abstention of influential states. Implementation is further challenged by political fragmentation, sovereignty concerns, and disparities in national capacities. The Agreement's success will depend on sustained political will, robust accountability mechanisms, and meaningful national adoption. Ongoing debates over the definition of ‘pandemic’ and the WHO's limited enforcement powers underscore the tension between multilateral cooperation and national sovereignty. While the Agreement represents a significant step forward, it is not a panacea. Its promise lies in its potential to catalyse coordinated global action, but only if supported by genuine commitment and adaptive governance. As the world faces future health threats, the Pandemic Agreement must evolve into a practical tool for resilience, equity, and collective security.