Hospitals operate in increasingly complex and dynamically uncertain environments. To understand how hospital organizations can cope with such profound uncertainty, this article presents a multiple case study of five hospitals during the COVID-19 crisis in a heavily hit region of the Netherlands. We find that hospitals make adaptations in five key categories, namely: reorganization, decision-making, human resources, material resources, and planning. These adaptations offer insights into the core capabilities needed by hospitals to cope with dynamic uncertainty. Our findings highlight the need for hospitals to become more flexible without sacrificing efficiency. Organizations can accomplish this by building in more sensing and seizing capabilities to be better prepared for and respond to environmental change. Furthermore, transforming capabilities allow organizations to be more resilient and responsive in the face of ongoing uncertainty. We make recommendations on how hospitals can build these capabilities and address the core challenges they face in this pursuit.

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Maintaining hospital workers’ psychological health is essential for hospitals’ capacities to sustain organizational functioning during the COVID-19 pandemic. Workers’ personal resilience can be an important factor in preserving psychological health, but how this exactly works in high stakes situations, such as the COVID-19 pandemic, requires further exploration. Similarly, the role of team social climate as contributor to individual psychological health seems obvious, but how it exactly prevents workers from developing depressive complaints in prolonged crises remains under investigated. The present paper therefore applies conservation of resources theory to study the relationships between resilience, team social climate, and depressive complaints, specifically focusing on worries about infections as an important explanatory mechanism. Based on questionnaire data of 1126 workers from five hospitals in the Netherlands during the second peak of the pandemic, this paper estimates a moderated-mediation model. This model shows that personal resilience negatively relates to depressive complaints (β = −0.99, p < 0.001, 95%CI = −1.45–−0.53), partially as personal resilience is negatively associated with worries about infections (β = −0.42, p < 0.001, 95%CI = −0.50–−0.33) which in turn are positively related to depressive complaints (β = 0.75, p < 0.001, 95% CI = 0.31–1.19). Additionally, team social climate is associated with a lower effect of worries about being infected and infecting others on depressive complaints (β = −0.88, p = 0.03, 95% CI = −1.68–−0.09). These findings suggest that resilience can be an important individual level resource in preventing depressive complaints. Moreover, the findings imply that hospitals have an important responsibility to maintain a good team social climate to shield workers from infection related worries building up to depressive complaints.

Background: Surveillance of recent HIV infections (RHI) using an avidity assay has been implemented at Dutch sexual health centres (SHC) since 2014, but data on RHI diagnosed at other test locations is lacking.Setting: Implementation of the avidity assay in HIV treatment clinics for the purpose of studying RHI among HIV patients tested at different test locations.Methods: We retrospectively tested leftover specimens from newly diagnosed HIV patients in care in 2013–2015 in Amsterdam. Avidity Index (AI) values ≤0.80 indicated recent infection (acquired ≤6 months prior to diagnosis), and AI &gt; 0.80 indicated established infection (acquired &gt;6 months prior to diagnosis). An algorithm for RHI was applied to correct for false recency. Recency based on this algorithm was compared with recency based on epidemiological data only. Multivariable logistic regression analysis was used to identify factors associated with RHI among men who have sex with men (MSM).Results: We tested 447 specimens with avidity; 72% from MSM. Proportions of RHI were 20% among MSM and 10% among heterosexuals. SHC showed highest proportions of RHI (27%), followed by GPs (15%), hospitals (5%), and other/unknown locations (11%) (p &lt; 0.001). Test location was the only factor associated with RHI among MSM. A higher proportion of RHI was found based on epidemiological data compared to avidity testing combined with the RHI algorithm.Conclusion: SHC identify more RHI infections compared to other test locations, as they serve high-risk populations and offer frequent HIV testing. Using avidity-testing for surveillance purposes may help targeting prevention programs, but the assay lacks robustness and its added value may decline with improved, repeat HIV testing and data collection.