International Journal of Health Planning and Management最新文献

This study aims to optimise operating room scheduling and improve hospital operational efficiency by predicting surgery durations using machine learning algorithms. Traditional methods often face challenges in accuracy, while machine learning models offer superior predictive performance. Using real-world operating room data from a large general hospital in China, the study compares various machine learning algorithms and selects the XGBoost model as the most effective predictive framework. Three types of models were developed: an all-inclusive model, a department-specific model, and a doctor-specific model. The department-specific model demonstrated the highest accuracy, outperforming the others. The results were applied to the hospital's surgical centre scheduling process, significantly enhancing operating room resource utilisation. The study highlights the importance of data preprocessing and feature selection in improving prediction accuracy. Overall, machine learning-based surgery duration prediction can effectively address challenges in surgical scheduling and provide data-driven support for hospital operational management.

Prioritisation in public health is complex, as decision-makers must allocate limited resources across diverse health concerns, risk factors, and target groups. In Denmark, municipalities play a central role under the Danish Health Act §119, which mandates health promotion and preventive services. However, municipalities face challenges in balancing competing needs without clear guidance. This qualitative study explores how municipalities navigate public health prioritisation, through in-depth interviews with 21 health directors and managers from 15 municipalities, selected for demographic, socio-economic, and geographical diversity. Data were analysed using systematic text condensation (STC). The findings highlight the following six themes which are central to understanding how municipalities prioritise within health promotion and prevention: (1) Economic considerations: Prioritising short-term, patient-focused services under budget constraints, (2) Public health data: Prioritising services based on locally identified health challenges, (3) Legal frameworks: Focusing on mandated services amid ambiguous legislation, (4) Evidence and evaluation: Selecting interventions with perceived effectiveness, (5) Political context: Aligning health priorities with agendas and ideological acceptability, and (6) Demographic and geographic conditions: Adapting services to local realities. We find that municipalities adopt a pragmatic, consensus-driven approach, but prioritisation processes vary, leading to disparities in service provision. Using Kingdon's Multiple Streams Framework, we discuss how municipalities frame health issues, select policy solutions, and navigate political contexts. The study highlights the need for clearer national frameworks to promote equitable public health access.

Sovereign Wealth Funds (SWFs), managing over USD 13 trillion globally, are increasingly active in healthcare. Yet research often treats them as uniform, overlooking how mandates may shape strategies. This study explores whether typologies, savings, stabilisation, and development suggest distinct approaches to healthcare investment. Using Agency Theory and a multiple-case design, six funds were analysed across diverse contexts: Mubadala (UAE), Temasek (Singapore in India), GIC (Singapore in Malaysia), ADQ (UAE in Egypt), the New Zealand Superannuation Fund (NZSF), and the Nigeria Sovereign Investment Authority (NSIA). Findings indicate preliminary patterns. Stabilisation funds pursue conservative, infrastructure-heavy strategies to preserve liquidity. Savings funds deploy patient capital adaptively, emphasising innovation in strong systems and scalable delivery in moderate ones. Development funds align with state priorities, focussing on politically salient infrastructure even in moderate governance settings. Together, these patterns offer exploratory propositions that provide a foundation for further testing. The study contributes by extending Agency Theory to show how agency challenges differ by fund type, and by offering policymakers and healthcare leaders a framework to anticipate SWF behaviour and structure partnerships. While limited to six cases, with single-case representation for stabilisation and development funds, the framework provides a basis for future testing and for examining health outcomes such as access, innovation, and resilience. Recognising SWF heterogeneity is essential to aligning sovereign capital with sustainable healthcare.