Health Systems最新文献

Effective radiotherapy for cancer treatment requires precise and reproducible positioning of patients at linear accelerators. Assistance systems in digitally networked radiotherapy can help involved specialists perform these tasks more efficiently and accurately. This paper analyses patient positioning systems and develops new knowledge by applying the Design Science Research methodology. A systematic literature review ensures the rigour of the research. Furthermore, this article presents the results of an online survey on assistance systems for patient positioning, the derived design requirements and an artefact in the form of a conceptual model of a patient positioning system. Both the systematic literature review and the online survey serve as empirical evidence for the conceptual model. This paper thereby contributes to broadening the academic knowledge on patient positioning in radiotherapy and provides guidance to system designers.

Hospitals play a critical role in ensuring continuous and effective healthcare delivery, especially during crises. However, the COVID-19 pandemic exposed vulnerabilities in hospital systems, prompting a need to enhance resilience-the ability to withstand, absorb, respond to, recover from, and learn from disasters. A systematic literature review, grounded in the resource-based view, identified organizational characteristics, in terms of resources and capabilities, and their synergistic effects that bolster hospital resilience. The results demonstrate that digital technologies impact on anticipation and adaptation abilities, organizational capabilities to reorganize roles, tasks, and spaces enhance adaptability, and Inter-organizational collaborations increase the responsiveness of the hospitals. The study provides substantial theoretical and practical contributions. It expands knowledge of hospital resilience in light of recent disruptive events and promotes integration capabilities as determinants for the majority of resilience dimensions. All organisational and inter-organisational collaboration, cooperation, and coordination are deemed crucial for hospital resilience.

Digital health technologies, powered by digital data, provide an opportunity to improve the efficacy and efficiency of health systems at large. However, little is known about different approaches to the use of data for digital health design, or about their possible relations to system-level dynamics. In this contribution, we identify four existing approaches to the use of data for digital health design, namely the silent, the overt, the data-enabled, and the convergent. After characterising the approaches, we provide real-life examples of each. Furthermore, we compare the approaches in terms of selected desirable characteristics of the design process, highlighting relative advantages and disadvantages. Finally, we reflect on the system-level relevance of the differentiation between the approaches and point towards future research directions. Overall, the contribution provides researchers and practitioners with a broad conceptual framework to examine data-related challenges and opportunities in digital health design.

Healthcare spending in the United States is concentrated on a small percentage of individuals, with 5% of the population accounting for 50% of annual spending. Many patients among the top 5% of spenders have complex health and social needs. Care coordination interventions, often led by a multidisciplinary team consisting of nurses, community health workers and social workers, are one strategy for addressing the challenges facing such patients. Care teams strive to improve health outcomes by forging strong relationships with clients, visiting them on a regular basis, reconciling medications, arranging primary and speciality care visits, and addressing social needs such as housing instability, unemployment and insurance. In this paper, we propose a simulation algorithm that samples longitudinal patient-level encounter histories to estimate the staffing needs for a multidisciplinary care team. Our numerical results illustrate multiple uses of the algorithm for staffing under stationary and non-stationary patient enrollment rates.