Hospitals are complex systems, and the flow of patients is dynamic and nonlinear in such systems. Network representation allows flow algorithms to observe bottlenecks as candidates for optimisation. To model the dynamic behaviour of the patient flow, we need to consider the variability in arrival rates and service times (length of stay). Previously proposed dynamic flow algorithms mainly focused on arrival and departure rates, inflow and outflow, edges' and vertices' capacity, and routing, with applications mainly in transportation and telecommunication. In hospitals, bottlenecks that emerge from the patients' flow are a result of the vertices (wards) behaviour defined by capacity (beds), number of servers (staff), service time variability, and edges (care pathways) distribution probability. We offer a modified flow algorithm that takes a hospital network, iterates over the patients' arrival rates, and measures the flow with respect to vertices' capacities, servers, service time variability, edge capacity, and distribution probability. The result is a dynamic residual graph to measure the bottlenecks' persistency and severity, identify the root causes of bottlenecks, and wards' dynamic nonlinear behaviour. The algorithm provides a quick holistic view of hospital performance and the analysis of the edges and vertices' behaviour over time.

The degree of connectivity among hospital wards and the dynamic nonlinear flow of patients cause bottlenecks to begin in non-priority wards, accumulate within the paths, distribute throughout the hospital, and emerge as overflow in crucial wards. This requires a network-based modeling approach to address the bottlenecks caused by second- and third-order wards and to significantly influence the overall and emergent performance of multiple wards. Understanding the relative merits of different network modeling and analysis approaches in this complex environment is often challenging and requires a holistic strategy to identify persistent bottlenecks and provide evidence-based scenarios. This article introduces a novel hybrid modeling approach that integrates network analysis algorithms and agent-based network simulation of patient flow over a complete hospital network. Through network analysis, such as structural hole and flow algorithms, the approach identifies common persistent bottlenecks from the flow and structural perspectives, while percolation and perturbation analyses measure the performance improvement of wards based on variations in patient flow, and the simulations enable the investigation of scenarios. The results indicate the wards and patient types that can contribute to improving the hospital’s performance. The proposed approach facilitates holistic, dynamic modeling of hospitals, irrespective of their network scale, and enables the identification of bottleneck sources and their associated paths, contributing to a comprehensive assessment of the system’s performance.

Research data management has become an inevitable part of research, and both funding agencies and publishers nowadays require open and reusable data. This article focuses on one of the most prominent initiatives promoting reusability of research data - the FAIR guiding principles - nowadays widely accepted as the new standard for research data management. Through semi-structured interviews, we investigated researchers' experiences of practicing FAIR research data management within the context of a multi-stakeholder project within the field of health research funded by the European Commission. Our analysis showed that the informants' experiences of practicing FAIR research data management differed largely depending on which scientific tradition they belonged to; something that previous studies have attributed to shortcomings in the current infrastructure, lack of resources and persistent cultures around data sharing in the wider scientific community. Drawing on previous work presented within the field of Critical Data Studies (CDS), we argue that our findings point to a more fundamental problem; namely the failure to recognize that the FAIR framework is built on a positivist conceptualization of data. We argue that if FAIR is to have any chance of succeeding in its ambitions to be as inclusive and all-encompassing as it wants to be, these insights need to be taken more into account and provide some potential pathways.

Background: Medical waste should be collected, classified, and transported to the treatment plant within 48 h. If it is not disposed of in time, it will cause cross-infection, increasing the risk of disease transmission and environmental pollution. How to reasonably plan transportation routes to ensure that the medical waste can be transported to the treatment plant in time is very important.

Objective: There are usually two modes of transportation, the fastest speed and shortest path, how to reasonably plan the transportation scheme so that medical waste can be transported to the treatment plant for disposal in the specified time is the main purpose of this article.

Methods: The multi-agent modeling method is adopted. AnyLogic simulation software is used to model the transportation routes of 118 Grade III hospitals and 2 treatment plants in Beijing under the two transportation modes of fastest speed and shortest path.

Results: Based on the traffic index in Beijing, the speed range of 20 km/h–32 km/h is set up and divided into 4 parts and 24 levels with 0.5 km/h as the unit, and the 24 levels of medical waste transportation data set is formed. The key speed nodes of 21 km/h, 24 km/h and 29.5 km/h are identified.

Conclusions: The medical waste transportation model and transport data set formed in this paper have enriched the theory and data basis of medical waste transportation management. The key speed nodes of transportation model selection have important practical significance for the transportation management decision of medical waste in big cities.

Many important systems, both natural and artificial, are complex in nature, and models and simulations are one of the main instruments to study them. In this paper, we present an approach where a complex social system is represented at a high level of abstraction as a network, thereby addressing several challenges such as quantification, intervention, adaptation and validation. The network represents the factors that influence the mental health and wellbeing in children and young people. In this article, we present an approach that links a system dynamics simulation to simulate the network and ranking algorithms to measure the vertices' behaviors. The network is enhanced by adding edge strengths in the form of correlations between vertices (established through literature). Such an approach allows us to exploit the network structure to qualify and quantify the vertices of the network, to overlay different processes over the network topology, to add and remove new vertices, and therefore interact dynamically. This in turn allows for the qualification of vertices' importance and network resilience. System dynamics simulation allows for policy analysis, where different scenarios are analyzed by stimulating a set of vertices and the effect over the network is observed. This approach allows for an abstract, flexible, yet comprehensive way of analyzing a complex social network at any scale.

Emergency department overcrowding is a complex problem that persists globally. Data of visits constitute an opportunity to understand its dynamics. However, the gap between the collected information and the real-life clinical processes, and the lack of a whole-system perspective, still constitute a relevant limitation. An analytical pipeline was developed to analyse one-year of production data following the patients that came from the ED (n = 49,938) at Uppsala University Hospital (Uppsala, Sweden) by involving clinical experts in all the steps of the analysis. The key internal issues to the ED were the high volume of generic or non-specific diagnoses from non-urgent visits, and the delayed decision regarding hospital admission caused by several imaging assessments and lack of hospital beds. Furthermore, the external pressure of high frequent re-visits of geriatric, psychiatric, and patients with unspecified diagnoses dramatically contributed to the overcrowding. Our work demonstrates that through analysis of production data of the ED patient flow and participation of clinical experts in the pipeline, it was possible to identify systemic issues and directions for solutions. A critical factor was to take a whole systems perspective, as it opened the scope to the boundary effects of inflow and outflow in the whole healthcare system.

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem holistically. This paper introduces a novel approach to analyse healthcare production data, treating the length of stay of patients, and the follow up decision regarding discharge or admission to the hospital as a time-to-event analysis problem. Our methodology employs traditional survival estimators and machine learning models, and Shapley additive explanations values to interpret the model outcomes. The most relevant features influencing length of stay were whether the patient received a scan at the ED, emergency room urgent visit, age, triage level, and the medical alarm unit category. The clinical insights derived from the explanation of the models holds promise for increase understanding of the overcrowding from the data. Our work demonstrates that a time-to-event approach to the over- crowding serves as a valuable initial to uncover crucial insights for further investigation and policy design.

The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on reproducing control arm outcomes by matching real-world patient cohorts to clinical trial baseline populations. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. In this article, we propose a novel approach that aims to explore and examine these discrepancies by concomitantly investigating the impact of selection criteria and operations on the measurements of outcomes from the patient data. We tested the approach on a dataset consisting of small-cell lung cancer patients receiving platinum-based chemotherapy regimens from a real-world data cohort (n = 223) and six clinical trial control arms (n = 1224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in both patient populations and operational conditions (e.g., frequency of assessments, and censoring), for which further investigation is required. Discovering and accounting for confounders, including hidden effects of differences in operations related to the treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trials and real-world data. Continued development of the method presented here to systematically explore and account for these differences could pave the way for transferring learning across clinical studies and developing mutual translation between the real-world and clinical trials to inform clinical study design.

Background: The implementation of a data-driven approach within the health care system happens in a rapid pace; including in the eldercare sector. Within Swedish eldercare, data-driven health approach is not yet widely implemented. In the specific context of long-term care for older adults, quality of care is as much determined by how social care is being performed as it is by what kind medical care that is provided. In particular, relational aspects have been proven to have a crucial influence on the experience of quality of care for the actors involved. Drawing on ethnographic material collected at a Swedish nursing home, this paper explores in what way the relational aspects of care could potentially become affected by the increased use of a data-driven health approach. Methods: An ethnographic approach was adopted in order to investigate the daily care work at a long-term care facility as it unfolded. Fieldwork was conducted at a somatic ward in a Swedish long-term care facility over 4 months (86 h in total), utilizing the methods of participant observation, informal interviews and document analysis. The material was analyzed iteratively throughout the entire research process adopting thematic analysis. Results: Viewing our ethnographic material through an observational lense problematising the policy discourse around data-driven health approach, two propositions were developed. First, we propose that relational knowledge risk becoming less influential in shaping everyday care, when moving to a data-driven health approach. Second, we propose that quality of care risk becoming more directed on quality of medical care at the expense of quality of life. Conclusion: While the implementation of data-driven health approach within long-term care for older adults is not yet widespread, the general development within health care points towards a situation in which this will become reality. Our study highlights the importance of taking the relational aspects of care into consideration, both during the planning and implementation phase of this process. By doing this, the introduction of a data-driven health approach could serve to heighten the quality of care in a way which supports both quality of medical care and quality of life.