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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.
Öppna denna publikation i ny flik eller fönster >>Diagnosing an overcrowded emergency department from its Electronic Health Records
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2024 (Engelska)Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikel-id 9955Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Nature Research, 2024
Nationell ämneskategori
Medicin och hälsovetenskap
Identifikatorer
urn:nbn:se:kth:diva-346372 (URN)10.1038/s41598-024-60888-9 (DOI)001225890200015 ()38688997 (PubMedID)2-s2.0-85191754138 (Scopus ID)
Anmärkning

QC 20240617

Tillgänglig från: 2024-05-14 Skapad: 2024-05-14 Senast uppdaterad: 2024-06-17Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>A Comparative Analysis between Real-World Data and Clinical Trials to Evaluate Differences in Outcomes for SCLC Patients
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2023 (Engelska)Ingår i: Journal of Thoracic Oncology, ISSN 1556-0864, E-ISSN 1556-1380, Vol. 18, nr 11, s. S697-S697Artikel i tidskrift, Meeting abstract (Övrigt vetenskapligt) Published
Ort, förlag, år, upplaga, sidor
ELSEVIER SCIENCE INC, 2023
Nyckelord
small cell lung cancer, real-world evidence, platinum doublet chemotherapy
Nationell ämneskategori
Cancer och onkologi
Identifikatorer
urn:nbn:se:kth:diva-342734 (URN)001098831602137 ()
Anmärkning

QC 20240216

Tillgänglig från: 2024-02-16 Skapad: 2024-02-16 Senast uppdaterad: 2024-02-16Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis
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2023 (Engelska)Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 302, s. 18-22Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
IOS Press, 2023
Nyckelord
oncology, Process mining, Real-world Data, small cell lung cancer, treatment decision
Nationell ämneskategori
Cancer och onkologi
Forskningsämne
Tillämpad matematik och beräkningsmatematik
Identifikatorer
urn:nbn:se:kth:diva-329927 (URN)10.3233/SHTI230056 (DOI)001071432900004 ()37203601 (PubMedID)2-s2.0-85159759671 (Scopus ID)
Konferens
The 33rd Medical Informatics Europe Conference, MIE2023, Gothenburg, Sweden. May 22-25, 2023.
Anmärkning

QC 20230628

Tillgänglig från: 2023-06-26 Skapad: 2023-06-26 Senast uppdaterad: 2023-11-07Bibliografiskt granskad
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)
Öppna denna publikation i ny flik eller fönster >>A Data-Driven Discrete Event Simulation Model to Improve Emergency Department Logistics
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2022 (Engelska)Ingår i: Proceedings of the 2022 Winter Simulation Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2022Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2022
Nationell ämneskategori
Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi
Identifikatorer
urn:nbn:se:kth:diva-330232 (URN)10.1109/wsc57314.2022.10015465 (DOI)000991872900062 ()2-s2.0-85147456598 (Scopus ID)
Konferens
Winter Simulation Conference, WSC 2022, Singapore, December 11-14, 2022
Anmärkning

QC 20230628

Tillgänglig från: 2023-06-28 Skapad: 2023-06-28 Senast uppdaterad: 2023-09-01Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>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 (Engelska)Ingår i: Clinical and Translational Science, ISSN 1752-8054, E-ISSN 1752-8062, Vol. 15, nr 10, s. 2437-2447Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Wiley, 2022
Nyckelord
C reactive protein; carboplatin; cisplatin; etoposide; irinotecan; lactate dehydrogenase; platinum complex
Nationell ämneskategori
Medicin och hälsovetenskap Cancer och onkologi
Identifikatorer
urn:nbn:se:kth:diva-321440 (URN)10.1111/cts.13371 (DOI)000832654100001 ()35856401 (PubMedID)2-s2.0-85135121261 (Scopus ID)
Forskningsfinansiär
Cancerfonden, CAN2021/1469 Pj01Cancerfonden, CAN 2018/597KTH
Anmärkning

QC 20221115

Tillgänglig från: 2022-11-14 Skapad: 2022-11-14 Senast uppdaterad: 2022-11-16Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-3398-2296

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