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Development and validation of the short form of the Later Life Workplace Index:a study across 10 countries
No full textCost-Effectiveness of Empagliflozin in Patients with Chronic Heart Failure Irrespective of Left-Ventricle Ejection Fraction in the Netherlands
No full textOBJECTIVE: Clinical trials have demonstrated the efficacy of the sodium-glucose cotransporter-2 inhibitor (SGLT2i) empagliflozin in patients suffering from heart failure (HF), regardless of whether their left-ventricle ejection fraction (LVEF) is reduced (HFrEF), mildly reduced (HFmrEF), or preserved (HFpEF). This study aims to assess the cost-effectiveness of empagliflozin when added to standard of care (SoC), consisting of lifestyle changes, medications, and surgery or devices, compared to SoC alone in patients with chronic HF irrespective of LVEF in the Netherlands.METHODS: A Markov model was developed to simulate patient outcomes over a lifetime horizon, incorporating data from the EMPEROR-Reduced and EMPEROR-Preserved trials. Key outcomes included incremental cost-effectiveness ratios (ICERs) expressed in costs per quality-adjusted life-year (QALY) gained, life expectancy, and hospitalization rates. Probabilistic and one-way sensitivity analyses were conducted to assess the robustness of the results.RESULTS: The analysis revealed that treatment with empagliflozin plus SoC resulted in higher life expectancy (6.58 vs. 6.47 years for HFrEF; 7.78 vs. 7.69 years for HFmrEF/HFpEF) and a lower incidence of HF hospitalizations compared to SoC alone. The ICERs were €8515/QALY for HFrEF and €9807/QALY for HFmrEF/HFpEF, both below the willingness-to-pay threshold of €50,000/QALY, indicating cost-effectiveness. Sensitivity analyses confirmed the robustness of the results, indicating there is a high probability (97% for HFrEF and 98% for HFmrEF/HFpEF) that empagliflozin plus SoC is cost-effective.CONCLUSION: Empagliflozin, when added to SoC, is a cost-effective treatment option for patients irrespective of LVEF in the Netherlands.</p
Anthropomorphic AI:Sociopathic AI Features as Predictors of Career Opportunities and Threats Perceived by Pilots in Commercial Aviation
No full textThe Utility of an Attention-Based Performance Validity Test in a Sample of Austrian Early Retirement Claimants
No full textWODC-onderzoek. Openbare registers, de toegankelijkheid daarvan en het gegevensbeschermingsrecht
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The role of agentic disposition and orientation on the career competencies of graduating students:a four-country study
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Only practical knowledge or knowing the algorithm?:Notions and necessities of explainable artificial intelligence in long-term care
No full textThe number of older adults living independently at home is expanding, which is often said to bring the need for more technological assistance. Dutch policy aims to allow older adults to remain living at home as long as possible. In such policies, the use of technologies supports older adults to perform daily practices. Artificial intelligence (AI), as part of these technologies, has the potential to improve personalized care and ageing in place, both at home and in residential care settings. However, the internal machineries of AI systems often remain hidden as a black box for the users, which can include caregivers or older adults. Interest in explainable AI (XAI) originates from this ‘black boxing’, as a technique to assist users in understanding the underlying logic of the decision-making process, and in identifying mistakes, transforming the opaque black box into an interpretable twin ‘white box’. Current research is mostly located in the technical domains, and it remains unknown how various stakeholders see XAI. To fill this gap, we conducted 21 scenario-based interviews with professionals to investigated XAI in three long-term care contexts: company, care management and care provision. We draw on the theory of the co-constitution of ageing and AI to reach our aim of understanding ‘what is XAI’ in the different contexts, and the enactments of XAI in their practices. Participants express different notions and necessities of XAI, varying from knowing algorithms towards practical understanding. The needed level of explainability is divers in the different contexts of care. As a follow-up, we recommend to include older adults and perform research into the enactment of XAI in practice, and the form or degree of XAI needed and for whom
Big Data Analytics-Driven AI Capabilities and Their Influence on Innovation Ambidexterity:A Hospital Service Performance Study
No full textThe emergence of big data analytics (BDA) and artificial intelligence(AI) offers hospital departments new opportunities to enhance service performance and the quality of care. However, the specific role of BDA-driven AI capabilities in driving innovation ambidexterity, the ability to simultaneously explore new ideas and exploit existing ones, remains unclear. Thus, our work investigates how BDA-driven AI capabilities influence innovation ambidexterityand, in turn, enhance service performance at the departmental level. In doing so,we use survey data from 118 Dutch hospital departments and analyze it usingpartial least squares structural equation modeling (PLS-SEM). We find thatBDA-driven AI capabilities have a positive impact on the innovation ambidexterity of hospital departments, which subsequently enhances service performance. Supplementary analysis using two-step qualitative comparative analysis(QCA) confirms our main findings and offers additional insights on the uniqueroles of BDA-AI capabilities and innovation ambidexterity in driving service performance. The results are also confirmed through robustness checks using alternative measures of ambidexterity and alternative methods of analysis. This research adds to the extant literature by offering valuable insights into how hospitaldepartments could drive service performance while leveraging their BDA-drivenAI capabilities that foster innovation ambidexterity