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Publications (5 of 5) Show all publications
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)38688997 (PubMedID)2-s2.0-85191754138 (Scopus ID)
Note

QC 20240515

Available from: 2024-05-14 Created: 2024-05-14 Last updated: 2024-05-15Bibliographically 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
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
Abourraja, M. N., Marzano, L., Raghothama, J., Boodaghian Asl, A., Darwich, A. S., Meijer, S., . . . Falk, N. (2022). A Data-Driven Discrete Event Simulation Model to Improve Emergency Department Logistics. In: Proceedings of the 2022 Winter Simulation Conference: . Paper presented at Winter Simulation Conference, WSC 2022, Singapore, December 11-14, 2022. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Data-Driven Discrete Event Simulation Model to Improve Emergency Department Logistics
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2022 (English)In: Proceedings of the 2022 Winter Simulation Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper, Published paper (Refereed)
Abstract [en]

Demands for health care are becoming overwhelming for healthcare systems around the world regarding theavailability of resources, particularly, in emergency departments (EDs) that are continuously open and mustserve immediately any patient who comes in. Efficient management of EDs and their resources is requiredmore than ever. This could be achieved either by optimizing resource utilization or by the improvement ofhospital layout. This paper investigates, through data-driven simulation alternative designs of workflowsand layouts to operate the ED of the Uppsala University Hospital in Sweden. Results are analyzed tounderstand the requirements across the hospital for reduced waiting times in the ED. The main observationrevealed that introducing a new ward dedicated to patients having complex diagnoses with a capacity ofless than 20 beds leads to lower waiting times. Furthermore, the use of data-mining was of great help inreducing the efforts of building the simulation model.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:kth:diva-330232 (URN)10.1109/wsc57314.2022.10015465 (DOI)000991872900062 ()2-s2.0-85147456598 (Scopus ID)
Conference
Winter Simulation Conference, WSC 2022, Singapore, December 11-14, 2022
Note

QC 20230628

Available from: 2023-06-28 Created: 2023-06-28 Last updated: 2023-09-01Bibliographically approved
Marzano, L., Darwich, A. S., Tendler, S., Dan, A., Lewensohn, R., De Petris, L., . . . Meijer, S. (2022). A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study. Clinical and Translational Science, 15(10), 2437-2447
Open this publication in new window or tab >>A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study
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2022 (English)In: Clinical and Translational Science, ISSN 1752-8054, E-ISSN 1752-8062, Vol. 15, no 10, p. 2437-2447Article in journal (Refereed) Published
Abstract [en]

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans’ Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA–IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.

Place, publisher, year, edition, pages
Wiley, 2022
Keywords
C reactive protein; carboplatin; cisplatin; etoposide; irinotecan; lactate dehydrogenase; platinum complex
National Category
Medical and Health Sciences Cancer and Oncology
Identifiers
urn:nbn:se:kth:diva-321440 (URN)10.1111/cts.13371 (DOI)000832654100001 ()35856401 (PubMedID)2-s2.0-85135121261 (Scopus ID)
Funder
Swedish Cancer Society, CAN2021/1469 Pj01Swedish Cancer Society, CAN 2018/597KTH Royal Institute of Technology
Note

QC 20221115

Available from: 2022-11-14 Created: 2022-11-14 Last updated: 2022-11-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3398-2296

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