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.

Concentration-dependent binding to red blood cells is a characteristic of several drugs, complicating the understanding of how pathophysiological factors influence drug behavior. This study utilized user-friendly, physiologically-based pharmacokinetic (PBPK) models to compare concentration-dependent and independent blood-to-plasma drug concentration ratios (B/P), using tacrolimus as a case study. Two models were developed and validated for tacrolimus using clinical data from healthy volunteers; Model 1 accounted for saturable blood binding, and Model 2 used a constant B/P level. The differences between the two models based on the two binding assumptions were also studied across clinically relevant hematocrit (HCT) and dose levels. For intravenous (IV) infusions, varying HCT from 15 to 45% resulted in a predicted difference in the area under the concentration-time curve (AUC) of 6–9% for total drug concentration in blood and 37–39% for unbound drug concentration in plasma. Increasing IV doses increased the predicted differences in blood AUC. For oral dosing to steady state, predicted differences in trough concentrations ranged between 50% and 130%, peak concentrations (78–284%), and AUC (up to 125%) according to HCT, dose, and biological medium, e.g., trough differences ranged from 50% (blood, 5 mg) to 130% (plasma, 10 mg). A hypothetical scenario of tacrolimus dose levels increasing above clinically relevant doses revealed a reducing difference in outcomes between the two binding assumptions. Although PBPK models ignoring concentration-dependent binding may adequately fit observed data, they can necessitate compensatory adjustments in disposition parameters, limiting their ability to predict clinical scenarios beyond the model’s original development settings.

Purpose Medical technology manufacturers and innovators have raised the need for more transparency on health technology assessment (HTA) and evidence requirements. The aim of this project was to develop and evaluate an evidence generation tool, a process map to support evidence generation in the context of Swedish HTA. This was facilitated by mapping the Swedish HTA system and processes.

Methods The project undertook a mixed methods approach. Data collection and analysis was carried out to inform mapping of the HTA system and its processes, and evidence generation. A series of discussions were carried out with experts to refine the material. An expert workshop was organised to gather wider input on the current state of evidence generation for medical devices in Sweden. A proof-of-concept usability study was carried out to evaluate the evidence generation tool. The material was developed into a website.

Results Here we present an analysis of the Swedish HTA system and processes, and hurdles for medical device evidence generation. A tool was developed, evaluated, and presented on a dedicated website to support evidence generation for medical devices in the context of Swedish HTA.

Conclusions Evidence evaluation needs to be adapted for medical devices through wider and more informed collaboration between industry, assessors, patient representatives, and other stakeholder groups. This may increase the likelihood of more conclusive HTA. Further, improving the knowledge among companies and researchers on the HTA process may lead to more efficient clinical evidence generation. Further dissemination of the evidence generation tool may facilitate this.

Background: Socioeconomic and environmental factors influence youth mental well-being. Promoting mental well-being is essential to support youths' development toward adulthood with good mental health. Different Stockholm municipalities have adopted strategies to promote youth well-being. However, contextualizing and perceiving goals and mechanisms at the local municipal level is difficult. Thus, comparing or tracking their conception, purpose, and characteristics has been challenging. Objective: We aimed to use data visualizations developed from a fusion of data sources to facilitate stakeholder conversations on promoting youth mental well-being within a municipality. We strive to demonstrate our methodology of using data visualizations as "boundary objects," which are cognitive artifacts that bridge knowledge from various domains to elicit understanding from specialized and siloed parts of a health delivery system. Methods: Stakeholders from the municipalities of Liding & ouml; and Nyn & auml;shamn participated in the study. A total of 15 workshops were conducted: 6 with only Liding & ouml; participants, 6 with only Nyn & auml;shamn participants, and 3 with mixed participants. The sessions were conducted via Microsoft Teams or as physical sessions in Swedish and lasted between 60 and 90 minutes. Interactions were recorded with consent from participants. Recordings were transcribed using Amberscript software. We used matrix factorization with Kullback-Leibler divergence to extract 1000 features and created 10 topic clusters with 20 top words. We used the identified words and phrases to backtrack within the transcripts and to identify dialogues where they were used. We summarized participants' interactions across all the workshops to identify factors or strategies discussed for youth well-being. Results: Participants noted that these sessions allowed them to contextualize their local observations from municipalities relative to the status of other municipalities in the national statistics. They indicated that they conceptualized well-being differently in their respective municipalities and between different professional backgrounds, and the sources of stress for youth differed. They noted the differences in the strategy and data collected for tracking youth well-being. Promotion of sports was a common strategy, while options for leisure activities differed between municipalities and professions. Conclusions: Based on our observations and analysis of the transcripts from participatory workshops, we observed that the data-driven visualizations helped stakeholders from different departments of Liding & ouml; and Nyn & auml;shamn municipalities to identify and bridge knowledge gaps caused by data silos. Participants noted proposals to modify future surveys and identified that this approach to visualizations would help them to share knowledge and maintain a long-term and sustainable collaboration across departments.

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.

Efficacy to biologics in rheumatoid arthritis (RA) patients is variable and is likely influenced by each patient’s circulating drug levels. Using modelling and simulation, the aim of this study was to investigate whether adalimumab and etanercept biosimilar dosing intervals can be altered to achieve therapeutic drug levels at a faster/similar time compared to the recommended interval. RA patients starting subcutaneous Amgevita or Benepali (adalimumab and etanercept biosimilars, respectively) were recruited and underwent sparse serum sampling for drug concentrations. Drug levels were measured using commercially available kits. Pharmacokinetic data were analysed using a population approach (popPK) and potential covariates were investigated in models. Models were compared using goodness-of-fit criteria. Final models were selected and used to simulate alternative dosing intervals. Ten RA patients starting the adalimumab biosimilar and six patients starting the etanercept biosimilar were recruited. One-compartment PK models were used to describe the popPK models for both drugs; no significant covariates were found. Typical individual parameter estimates were used to simulate altered dosing intervals for both drugs. A simulation of dosing the etanercept biosimilar at a lower rate of every 10 days reached steady-state concentrations earlier than the usual dosing rate of every 7 days. Simulations of altered dosing intervals could form the basis for future personalised dosing studies, potentially saving costs whilst increasing efficacy.