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Meijer, Sebastiaan, ProfessorORCID iD iconorcid.org/0000-0003-1126-3781
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Publications (10 of 198) Show all publications
Boodaghian Asl, A., Raghothama, J., Darwich, A. S. & Meijer, S. (2024). A hybrid modeling approach to simulate complex systems and classify behaviors. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, 13(1), Article ID 9.
Open this publication in new window or tab >>A hybrid modeling approach to simulate complex systems and classify behaviors
2024 (English)In: NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, ISSN 2192-6662, Vol. 13, no 1, article id 9Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Simulation and modeling, Ranking algorithm, Complex systems, Mental wellbeing
National Category
Computer Sciences Other Health Sciences
Identifiers
urn:nbn:se:kth:diva-345028 (URN)10.1007/s13721-024-00446-5 (DOI)001186407300001 ()2-s2.0-85188097928 (Scopus ID)
Note

QC 20240408

Available from: 2024-04-08 Created: 2024-04-08 Last updated: 2025-01-22Bibliographically approved
Marzano, L., Darwich, A. S., Raghothama, J., Sven, L., Falk, N., Bodeby, P. & Meijer, S. (2024). Diagnosing an overcrowded emergency department from its Electronic Health Records. Scientific Reports, 14(1), Article ID 9955.
Open this publication in new window or tab >>Diagnosing an overcrowded emergency department from its Electronic Health Records
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2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 9955Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Nature Research, 2024
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:kth:diva-346372 (URN)10.1038/s41598-024-60888-9 (DOI)001225890200015 ()38688997 (PubMedID)2-s2.0-85191754138 (Scopus ID)
Note

QC 20240617

Available from: 2024-05-14 Created: 2024-05-14 Last updated: 2024-12-10Bibliographically approved
Haraldsson, T., Marzano, L., Krishna, H., Lethval, S., Falk, N., Bodeby, P., . . . Darwich, A. S. (2024). Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach. Studies in Health Technology and Informatics, 316, 678-682
Open this publication in new window or tab >>Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach
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2024 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 316, p. 678-682Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IOS Press, 2024
Keywords
Emergency Department, Explainable Artificial Intelligence (XAI), Healthcare Systems, Machine Learning, real-world data, Survival analysis
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:kth:diva-353489 (URN)10.3233/SHTI240505 (DOI)39176833 (PubMedID)2-s2.0-85202007643 (Scopus ID)
Note

QC 20240924

Available from: 2024-09-19 Created: 2024-09-19 Last updated: 2024-09-24Bibliographically approved
Marzano, L., Darwich, A. S., Dan, A., Tendler, S., Lewensohn, R., De Petris, L., . . . Meijer, S. (2024). Exploring the discrepancies between clinical trials and real-world data: A small-cell lung cancer study. Clinical and Translational Science, 17(8), Article ID e13909.
Open this publication in new window or tab >>Exploring the discrepancies between clinical trials and real-world data: A small-cell lung cancer study
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2024 (English)In: Clinical and Translational Science, ISSN 1752-8054, E-ISSN 1752-8062, Vol. 17, no 8, article id e13909Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Wiley, 2024
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-351885 (URN)10.1111/cts.13909 (DOI)001285427300001 ()39113428 (PubMedID)2-s2.0-85200661148 (Scopus ID)
Note

QC 20240829

Available from: 2024-08-19 Created: 2024-08-19 Last updated: 2024-12-10Bibliographically approved
Hellstrand, M., Kornevs, M., Raghothama, J. & Meijer, S. (2024). Tensions between real-world practices and the digitalization paradigm for data-driven services in eldercare: observations from an ethnographic study in Sweden. BMC Geriatrics, 24(1), Article ID 98.
Open this publication in new window or tab >>Tensions between real-world practices and the digitalization paradigm for data-driven services in eldercare: observations from an ethnographic study in Sweden
2024 (English)In: BMC Geriatrics, E-ISSN 1471-2318, Vol. 24, no 1, article id 98Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Data driven health, Ethnography, Long term care, Quality of care, Relational care
National Category
Nursing Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:kth:diva-342826 (URN)10.1186/s12877-024-04693-z (DOI)38273237 (PubMedID)2-s2.0-85182986445 (Scopus ID)
Note

QC 20240201

Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-08-28Bibliographically approved
Marzano, L., Dan, A., Tendler, S., Darwich, A. S., Raghothama, J., De Petris, L., . . . Meijer, S. (2023). A Comparative Analysis between Real-World Data and Clinical Trials to Evaluate Differences in Outcomes for SCLC Patients. Journal of Thoracic Oncology, 18(11), S697-S697
Open this publication in new window or tab >>A Comparative Analysis between Real-World Data and Clinical Trials to Evaluate Differences in Outcomes for SCLC Patients
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2023 (English)In: Journal of Thoracic Oncology, ISSN 1556-0864, E-ISSN 1556-1380, Vol. 18, no 11, p. S697-S697Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC, 2023
Keywords
small cell lung cancer, real-world evidence, platinum doublet chemotherapy
National Category
Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-342734 (URN)001098831602137 ()
Note

QC 20240216

Available from: 2024-02-16 Created: 2024-02-16 Last updated: 2024-02-16Bibliographically approved
Abourraja, M. N., Rouky, N., Kornevs, M., Meijer, S. & Kringos, N. (2023). A simulation-based decision support framework devoted to Ro-Ro terminals: Design, implementation and evaluation. Computers & industrial engineering, 180, 109248, Article ID 109248.
Open this publication in new window or tab >>A simulation-based decision support framework devoted to Ro-Ro terminals: Design, implementation and evaluation
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2023 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 180, p. 109248-, article id 109248Article in journal (Refereed) Published
Abstract [en]

As a type of intermodal terminal, pure Ro-Ro terminals are one of the most important logistic hubs in the supply chain for rolling freight stored in containerized and wheeled steel boxes. These large-scale systems are highly complex, with nonlinear and hard-to-predict behavior evolving in a stochastic environment. Consequently, making decisions about any problem thereof is no mean feat, particularly for terminal planners. To assist them in decision-making, a pool of relevant models and tools have been developed over the years in the literature. Nevertheless, models that are oriented toward specific objectives dominate, and generic ones are rare. This paper tries to fill this gap and proposes a generic framework to be used as a factory to create specific decision support models based on simulation for pure Ro-Ro terminals. This framework is formulated following two artifacts: (1) the known classification of key performance indicators combined with the typical functional and physical organization of pure Ro-Ro terminals; (2) the three main arteries of harbor systems, namely flows, decisions and operations. Then a scalable way of making decisions based on a flexible form of the cost function weighted according to a set of coefficients is integrated. These designed coefficients allow decision-makers a wide flexibly in choosing how the best solutions are determined. An application of this framework is illustrated through a real case study, where the weights are estimated using an expert-profiling based approach then pushed into the OptQuest optimizer to be calibrated before analyzing the results. These results are aggregated, then expressed as scores on a scale of 0 to 1. This is to help terminal planners to easily identify the worst and best planning scenarios as well as the relationships and compatibilities between the involved handling rules to suggest different alternatives for managing operations.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Ro-Ro terminal, Terminal planning, Discrete event simulation, Decision support framework, Conceptual modeling, Generic design
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-328412 (URN)10.1016/j.cie.2023.109248 (DOI)000988759100001 ()2-s2.0-85153223415 (Scopus ID)
Note

QC 20230613

Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2023-06-13Bibliographically approved
Marzano, L., Meijer, S., Dan, A., Tendler, S., De Petris, L., Lewensohn, R., . . . Darwich, A. S. (2023). Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis. Paper presented at The 33rd Medical Informatics Europe Conference, MIE2023, Gothenburg, Sweden. May 22-25, 2023.. Studies in Health Technology and Informatics, 302, 18-22
Open this publication in new window or tab >>Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis
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2023 (English)In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 302, p. 18-22Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IOS Press, 2023
Keywords
oncology, Process mining, Real-world Data, small cell lung cancer, treatment decision
National Category
Cancer and Oncology
Research subject
Applied and Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-329927 (URN)10.3233/SHTI230056 (DOI)001071432900004 ()37203601 (PubMedID)2-s2.0-85159759671 (Scopus ID)
Conference
The 33rd Medical Informatics Europe Conference, MIE2023, Gothenburg, Sweden. May 22-25, 2023.
Note

QC 20230628

Available from: 2023-06-26 Created: 2023-06-26 Last updated: 2023-11-07Bibliographically approved
Darwich, A. S., Bostroem, A.-M., Guidetti, S., Raghothama, J. & Meijer, S. (2023). Investigating the Connections Between Delivery of Care, Reablement, Workload, and Organizational Factors in Home Care Services: Mixed Methods Study. JMIR Human Factors, 10, Article ID e42283.
Open this publication in new window or tab >>Investigating the Connections Between Delivery of Care, Reablement, Workload, and Organizational Factors in Home Care Services: Mixed Methods Study
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2023 (English)In: JMIR Human Factors, E-ISSN 2292-9495, Vol. 10, article id e42283Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
JMIR Publications Inc., 2023
National Category
Nursing
Identifiers
urn:nbn:se:kth:diva-333252 (URN)10.2196/42283 (DOI)001025960000001 ()37389904 (PubMedID)2-s2.0-85165985349 (Scopus ID)
Note

QC 20230731

Available from: 2023-07-31 Created: 2023-07-31 Last updated: 2024-08-28Bibliographically approved
Wang, Q., Liu, H., Ore, F., Wang, L., Baalsrud Hauge, J. & Meijer, S. (2023). Multi-actor perspectives on human robotic collaboration implementation in the heavy automotive manufacturing industry- A Swedish case study. Technology in society, 72, 102165, Article ID 102165.
Open this publication in new window or tab >>Multi-actor perspectives on human robotic collaboration implementation in the heavy automotive manufacturing industry- A Swedish case study
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2023 (English)In: Technology in society, ISSN 0160-791X, E-ISSN 1879-3274, Vol. 72, p. 102165-, article id 102165Article in journal (Refereed) Published
Abstract [en]

Implementing an industrial collaborative robot for Human-Robot Collaboration (HRC) in the automotive manufacturing industry is an emerging technology-driven solution aiming to increase production efficiency and reduce the human operator's ergonomic load. Successful implementation of innovative technology depends on technical feasibility and on the acceptance by the affected actors. Many studies exist that focus on the technical aspects of HRC, however, research that focuses on understanding the multi-actor concerns of HRC adoption is rare. In an effort to support the successful adoption of industrial collaborative robots, this study aims to un-derstand the concerns of the various actors who work at the operational and management levels influencing future HRC adoption in the heavy automotive manufacturing industry.A literature review was conducted to understand the HRC implementation challenges and the methods used to investigate multi-actor involvement in advance of, and during, the implementation stage. After reviewing existing studies, the actor analysis method was selected to present the actors' perceptions using the action, factor, and goal (AFG) list to understand different actors' opinions of HRC adoption, using a Swedish heavy vehicle manufacturing company case study.The case study results showed that the actors from the same organization had different concerns but mostly positive expectations for future HRC adoption. The actors' perception map shows the details pertaining to Ac-tions, Concerns, and Goals as well as the logical flow between these elements in regards to HRC future adoption. The involvement of different actor groups prior to new solution implementation contributes to a holistic view of potential implementation influences and challenges in the organization. Actor analysis can provide a set of analysis processes that comply with multi-actor perceptions to understand future adoption challenges from different perspectives. In the next step, safety-related issues and under-development standardization are the key challenges of HRC implementation.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Human-robot collaboration, Actor analysis, Heavy automotive manufacturing
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:kth:diva-322793 (URN)10.1016/j.techsoc.2022.102165 (DOI)000890884300011 ()2-s2.0-85141925971 (Scopus ID)
Note

QC 20230207

Available from: 2023-02-07 Created: 2023-02-07 Last updated: 2023-02-07Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-1126-3781

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