Introduction There is a large and increasing shortage of nursing staff. To alleviate this problem, healthcare systems should prioritize healthcare interventions that improve nurse retention over healthcare interventions that reduce it or leave it unchanged. One way to do so is to evaluate interventions on their anticipated impact on nurse intention-to-stay, which is an important precursor of retention. An overview of available instruments to quantify nurse intention-to-stay is lacking, resulting in researchers re-inventing the wheel. This review aims to fill this gap. Methods A systematic literature search was performed in the databases Medline ALL via Ovid, Embase.com, Web of Science Core Collection, CINAHL Plus, PsycINFO, the Cochrane Central Register of Controlled Trials via Wiley, and Google Scholar (200 highest-ranked references only). The search string consisted of terms and associated synonyms for 1) nursing staff, 2) personnel intent to stay/leave, and 3) surveys. Articles were included when there was a quantitative method mentioned for measuring the intention of nurses to stay or quit nursing and/or their job/position/organization. Information was extracted on the year of publication, study design, study population, number of participants, instrument used for measuring intention-to-stay, and whether the instrument was focused on leaving the job, organization, or profession. In addition, we checked whether the instrument was used to evaluate the (expected or realized) impact of an intervention and if an association was determined between intention-to-stay (measured through the instrument) and retention. The protocol was not registered. Results 967 articles fulfilled our inclusion criteria, most of which were published in recent years. A total of 485 instruments were found. Nine regularly used instruments were identified, differing in their respective popularity over time, their size, the population for which they were developed and the strength of their link to actual retention. Notably, compared with the large body of literature on nurse intention-to-stay generally, the number of studies specifically measuring the impact of an intervention on nurse intention-to-stay is limited (n=20). Most of these intervention studies focused on changes in nurse training/mentorship or mental health support. Discussion & conclusion Many different instruments exist to measure nurses' intention-to-stay. To add to our identified instruments, a comparative study is needed to identify which instrument offers the strongest predictive value for nurse retention. The absence of studies specifically evaluating the impact of interventions on nurses' intention-to-stay creates a critical gap in understanding how health interventions influence retention. Funding Dutch Research Council, 406.XS.04.151.

Current approaches to health system resilience tend to prioritize system-level outcomes (e.g. functionality) while overlooking key underlying social processes, contexts, and power-laden interactions through which resilience is produced. When community resilience is subsumed under health system resilience, without attending to distinct contextual factors, it can lead to fragmented approaches or maladaptive outcomes that misalign with the resilience of communities. Therefore, resilience approaches need to include additional methods that incorporate analyses of power structures and context. We propose intersectionality theory as a methodological lens to investigate the underlying social processes and power dynamics that shape community resilience and health system resilience interactions. An intersectionality approach prompts researchers to distinguish how resilience capacity is derived through the involvement of community actors, their unique intersecting social identities, and their lived experiences. Including an intersectional lens in resilience approaches provides researchers with the tools to identify points of practical constraints that arise at the intersection of communities and health systems, with particular attention on the burdens that are placed on community actors.

Introduction: Mathematical and optimisation models are frequently used to improve hospital planning and capacity management. However, the resulting model-derived solutions are rarely evaluated for their adoption within the real-world context of a hospital. Objectives: In this study, we share our experience of an interdisciplinary collaboration between operations research/management science and implementation science, as one way of bridging the gap between technically sound solutions and their practical, sustainable use in healthcare. Methodology: We applied implementation science prospectively to anticipate adoption implications at the design stage of a scheduling tool. Specifically, we used the Consolidated Framework for Implementation Research (CFIR) to identify anticipated barriers and facilitators for adopting a mathematically optimised surgery blueprint schedule within a children’s hospital. Results: Identified anticipated facilitators included strong staff motivation to improve schedules, as well as positive perceptions of an objectively designed mathematical scheduling tool. Barriers included resistance to change among some staff and the demand for more evidence of the schedule’s benefits prior to implementation. We identified a strong culture of retaining autonomy in scheduling decisions, as well as operational adjustments made to current scheduling tools. Practical implications: Applying CFIR prospectively demonstrated how implementation science frameworks could provide a structured way to anticipate adoption challenges and align technical solutions with organisational realities.

Purpose (stating the main purposes and research question): Anthropogenic resource use contributes to pollution, violent conflict over scarce resources, loss of biodiversity, and diminished quality of life for humans. Moreover, the “safe” amount of carbon dioxide—350 parts per million—has been exceeded. The health care industry is responsible for 4–5% of total world emissions,[i] which is similar to the global food sector.[ii] Health care carbon emissions come from health care infrastructures, supply chains and health care delivery. Increasingly, health care delivery is reliant on technologies which require the use of artificial intelligence to provide supportive care, such as triage algorithms, electronic patient records, and robotics.[iii] While these technological innovations have advanced health care significantly, they also contribute to the negative effects on the environment, among others, through carbon emissions. The environmental impacts of artificial intelligence (AI) in health care—in particular—are understudied. This research seeks to fill this gap. Methods: Our team ran an exploratory search in Scopus and PubMed to identify studies that integrate environmental sustainability, artificial intelligence, and health. Results: Our research initially yielded 735 studies. 77 of these studies focused on an environmental concern of a health technology or AI-application in a health care setting, but most of the articles in this subset addressed lowering energy consumption of a specific technology, such as a sensor or monitoring technology. Conclusions: While there have been studies looking at AI in health care; sustainability in AI; and sustainability in health care, little attention has been paid to the interface between all three. [i] Karliner, J., Slotterback, S., Boyd, R., Ashby, B., & Steele, K. 2019. Health Care’s Climate Footprint: How the Health Sector Contributes to the Global Climate Crisis and Opportunities for Action Healthcare Without HarmARUP; September. [ii] Pichler, P. P., Jaccard, I. S., Weisz, U., & Weisz, H. 2019 International Comparison of Health Care Carbon Footprints, Environmental Research Letters 14, no. 6: 064004. [iii] Khaliq, Abdul, Ali Waqas, Qasim Ali Nisar, Shahbaz Haider, and Zunaina Asghar. 2022. Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective. Technology in Society 68: 101807.

Introduction: The 2021 European floods in Germany, Belgium, and the Netherlands significantly impacted healthcare. With climate change increasing flood risks, healthcare preparedness is essential. Floods affect healthcare directly and indirectly by disrupting patient access, damaging infrastructure and impeding care continuity. Our interdisciplinary research in the Netherlands systematically assesses flood impacts on healthcare, optimises disaster preparedness, patient logistics, and continuity and explores crisis governance, incorporating lessons from coronavirus disease-2019 (COVID-19). Methods: Our multi-sited, interdisciplinary project titled “Pandemic lessons for flood disaster preparedness” includes literature reviews on: (i) the (in) direct impacts of floods on healthcare, (ii) disaster decision-making strategies and (iii) patient logistics during crises. Empirically, ethnographic methods (interviews, focus groups, document analyses, and observations) will: (a) assess hospital flood preparedness, (b) explore decision-making and crisis management strategies and (c) analyse the dynamics of health system governance during floods. Data from these sources and flood scenarios will inform models on healthcare impacts and decision-making, culminating in a simulation game for research and training. Discussion: This study offers a comprehensive, interdisciplinary approach to understanding and improving healthcare system preparedness for floods. By integrating diverse fields such as healthcare governance, disaster risk management, logistics and hydraulic engineering, we provide a unique lens on resilience. A key strength is the incorporation of lessons from the COVID-19 pandemic, allowing us to draw parallels between pandemic response and flood preparedness. In addition, our simulation game serves as a robust tool for translating knowledge into practice. However, the study’s reliance on collaboration with busy healthcare and disaster response professionals may limit engagement. Moreover, the absence of direct public and patient involvement in the research design, though partially mitigated by engaging representative organizations, presents a potential limitation. Lastly, the challenge of obtaining real-time data from flood events could introduce recall bias, but triangulation of various data sources aims to address this issue. Despite these challenges, the study’s integration of long-term data from recent floods and focus on healthcare-specific crisis governance provides valuable insights for improving disaster preparedness.

Background: Real-time audiovisual communication between healthcare providers (HCP) at different hospitals (TeleNeonatology) can improve neonatal outcomes, address capacity challenges, and reduce emotional burden on parents. Despite its potential, TeleNeonatology has yet to be widely implemented in routine clinical care, partly due to non-optimal integration into care pathways and working routines. To provide insights for further adoption, this study presents the evaluation of a pilot in the Netherlands. Methods: A prospective hybrid type III effectiveness-implementation study was conducted in 2024. During the pilot, a TeleNeo program facilitated both acute and elective communication between Erasmus MC NICU-level IV and Amphia NICU-level II. The TeleNeo program was developed and continuously improved during the pilot using co-creation with HCP and parents to enable embedding in care pathways and working routines. A mixed-methods approach was used for evaluation. The primary outcome was a validated 21-item usability questionnaire with five-points Likert Scale questions for parents (n = 50) and HCP (n = 85). Implementation determinants were evaluated with semi-structured interviews and surveys. Effectiveness was measured via parent reported experiences, and clinical outcomes length-of-stay and transfer rate. Results: Twelve months of implementation led to 99 consultations for 50 patients and families, including 33 acute patients, possibly in need of an acute transfer. Evaluation showed high feasibility and adoption. Usability was high among parents (n = 26, median score 5 [interquartile rage: 4–5]) and HCP (n = 48, median score 5 [interquartile range 4–5]). Parents valued rapid expert availability, involvement in transfer decisions, and experienced shared care between the NICUs. HCP observed quick and approachable communication, quicker medical decisions, improved quality of care, and smoother transitions between NICUs. Nurses were able to be more pro-active. In 18% (6/33) of acute cases transfers were perceived to be prevented. HCP highlighted TeleNeo’s influence on the local teams’ autonomy, communication styles, and financial aspects as important barriers in interviews (n = 12) and questionnaires (n = 65). Conclusions: Pilot implementation showed high feasibility of our TeleNeo program, enabling shared care at the optimal location for our patients. Our findings will guide a robust strategy for implementation in the Southwest of the Netherlands, enhancing neonatal care, parental satisfaction and nursing experience.

Background: Health systems worldwide struggled to obtain sufficient personal protective equipment (PPE) and ventilators during the COVID-19 pandemic due to global supply chain disruptions. Our study’s aim was to create a proof-of-concept model that would simulate the effects of supply strategies under various scenarios, to ultimately help decision-makers decide on alternative supply strategies for future similar health system related crises. Methods: We developed a system dynamics model that linked a disease transmission model structure (susceptible, exposed, infectious, recovered (SEIR)) with a model for the availability of critical supplies in hospitals; thereby connecting care demand (patients’ critical care in hospitals), with care supply (available critical equipment and supplies). To inform the model structure, we used data on critical decisions and events taking place surrounding purchase, supply, and availability of PPE and ventilators during the first phase of the COVID-19 pandemic within the English national health system. We used exploratory modelling and analysis to assess the effects of uncertainties on different supply strategies in the English health system under different scenarios. Strategies analysed were: (i) purchasing from the world market or (ii) through direct tender, (iii) stockpiling, (iv) domestic production, (v) supporting innovative supply strategies, or (vi) loaning ventilators from the private sector. Results: We found through our exploratory analysis that a long-lasting shortage in PPE and ventilators is likely to be apparent in various scenarios. When considering the worst-case scenario, our proof-of-concept model shows that purchasing PPE and ventilators from the world market or through direct tender have the greatest influence on reducing supply shortages, compared to producing domestically or through supporting innovative supply strategies. However, these supply strategies are affected most by delays in their shipment time or set-up. Conclusion: We demonstrated that using a system dynamics and exploratory modelling approach can be helpful in identifying the purchasing and supply chain strategies that contribute to the preparedness and responsiveness of health systems during crises. Our results suggest that to improve health systems’ resilience during pandemics or similar resource-constrained situations, purchasing and supply chain decision-makers can develop crisis frameworks that propose a plan of action and consequently accelerate and improve procurement processes and other governance processes during health-related crises; implement diverse supplier frameworks; and (re)consider stockpiling. This proof-of-concept model demonstrates the importance of including critical supply chain strategies as part of the preparedness and response activities to contribute to health system resilience.

BACKGROUND: Prompted by recent shocks and stresses to health systems globally, various studies have emerged on health system resilience. Our aim is to describe how health system resilience is operationalised within empirical studies and previous reviews. We compare these to the core conceptualisations and characteristics of resilience in a broader set of domains (specifically, engineering, socio-ecological, organisational and community resilience concepts), and trace the different schools, concepts and applications of resilience across the health literature. METHODS: We searched the Pubmed database for concepts related to 'resilience' and 'health systems'. Two separate analyses were conducted for included studies: a total of n = 87 empirical studies on health system resilience were characterised according to part of health systems covered, type of threat, resilience phase, resilience paradigm, and approaches to building resilience; and a total of n = 30 reviews received full-text review and characterised according to type of review, resilience concepts identified in the review, and theoretical framework or underlying resilience conceptualisation. RESULTS: The intersection of health and resilience clearly has gained importance in the academic discourse with most papers published since 2018 in a variety of journals and in response to external threats, or in reference to more frequent hospital crisis management. Most studies focus on either resilience of health systems generally (and thereby responding to an external shock or stress), or on resilience within hospitals (and thereby to regular shocks and operations). Less attention has been given to community-based and primary care, whether formal or informal. While most publications do not make the research paradigm explicit, 'resilience engineering' is the most prominent one, followed by 'community resilience' and 'organisational resilience'. The social-ecological systems roots of resilience find the least application, confirming our findings of the limited application of the concept of transformation in the health resilience literature. CONCLUSIONS: Our review shows that the field is fragmented, especially in the use of resilience paradigms and approaches from non-health resilience domains, and the health system settings in which these are used. This fragmentation and siloed approach can be problematic given the connections within and between the complex and adaptive health systems, ranging from community actors to local, regional, or national public health organisations to secondary care. Without a comprehensive definition and framework that captures these interdependencies, operationalising, measuring and improving resilience remains challenging.