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.

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.

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

Background: Home care is facing increasing demand due to an aging population. Several challenges have been identified in the provision of home care, such as the need for support and tailoring support to individual needs. Goal-oriented interventions, such as reablement, may provide a solution to some of these challenges. The reablement approach targets adaptation to disease and relearning of everyday life skills and has been found to improve health-related quality of life while reducing service use.Objective: The objective of this study is to characterize home care system variables (elements) and their relationships (connections) relevant to home care staff workload, home care user needs and satisfaction, and the reablement approach. This is to examine the effects of improvement and interventions, such as the person-centered reablement approach, on the delivery of home care services, workload, work-related stress, home care user experience, and other organizational factors. The main focus was on Swedish home care and tax-funded universal welfare systems.Methods: The study used a mixed methods approach where a causal loop diagram was developed grounded in participatory methods with academic health care science research experts in nursing, occupational therapy, aging, and the reablement approach. The approach was supplemented with theoretical models and the scientific literature. The developed model was verified by the same group of experts and empirical evidence. Finally, the model was analyzed qualitatively and through simulation methods.Results: The final causal loop diagram included elements and connections across the categories: stress, home care staff, home care user, organization, social support network of the home care user, and societal level. The model was able to qualitatively describe observed intervention outcomes from the literature. The analysis suggested elements to target for improvement and the potential impact of relevant studied interventions. For example, the elements "workload" and "distress" were important determinants of home care staff health, provision, and quality of care.Conclusions: The developed model may be of value for informing hypothesis formulation, study design, and discourse within the context of improvement in home care. Further work will include a broader group of stakeholders to reduce the risk of bias. Translation into a quantitative model will be explored.

Background: Routine therapeutic drug monitoring (TDM) relies heavily on measuring trough drug concentrations. Trough concentrations are affected not only by drug bioavailability and clearance, but also by various patient and disease factors and the volume of distribution. This often makes interpreting differences in drug exposure from trough data challenging. This study aimed to combine the advantages of top-down analysis of therapeutic drug monitoring data with bottom-up physiologically-based pharmacokinetic (PBPK) modeling to investigate the effect of declining renal function in chronic kidney disease (CKD) on the nonrenal intrinsic metabolic clearance (CLint) of tacrolimus as a case example.

Methods: Data on biochemistry, demographics, and kidney function, along with 1167 tacrolimus trough concentrations for 40 renal transplant patients, were collected from the Salford Royal Hospital's database. A reduced PBPK model was developed to estimate CLint for each patient. Personalized unbound fractions, blood-to-plasma ratios, and drug affinities for various tissues were used as priors to estimate the apparent volume of distribution. Kidney function based on the estimated glomerular filtration rate (eGFR) was assessed as a covariate for CLint using the stochastic approximation of expectation and maximization method.

Results: At baseline, the median (interquartile range) eGFR was 45 (34.5-55.5) mL/min/1.73 m2. A significant but weak correlation was observed between tacrolimus CLint and eGFR (r = 0.2, P < 0.001). The CLint declined gradually (up to 36%) with CKD progression. Tacrolimus CLint did not differ significantly between stable and failing transplant patients.

Conclusions: Kidney function deterioration in CKD can affect nonrenal CLint for drugs that undergo extensive hepatic metabolism, such as tacrolimus, with critical implications in clinical practice. This study demonstrates the advantages of combining prior system information (via PBPK) to investigate covariate effects in sparse real-world datasets.