This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1328) Health Services Research (410) Health Care Quality and Health Services Research (247) Health Services Research and Health Care Utilization in Older Patients (76) Involvement of Elderly Populations in Health and Health Services Reserch (30) Frailty Detection, Assessment and Prediction (19) Design of Processes and Workflows (86)

Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27499, first published .
Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data

Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data

Identifying Frequent Health Care Users and Care Consumption Patterns: Process Mining of Emergency Medical Services Data

Authors of this article:

Laura Maruster1 Author Orcid Image ;   Durk-Jouke van der Zee1 Author Orcid Image ;   Erik Buskens1, 2 Author Orcid Image
Article Authors Cited by (7) Tweetations (5) Metrics

Journals

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  4. Yang X, Huang W, Zhao W, Zhou X, Shi N, Xia Q. Exploring Acute Pancreatitis Clinical Pathways Using a Novel Process Mining Method. Healthcare 2023;11(18):2529 View
  5. Cyrino C, Castro M, Nunes H, Deodato S, Dell’Acqua M, Juliani C. Factors related to readmissions to the Mobile Emergency Care Service. Escola Anna Nery 2023;27 View
  6. Cyrino C, Castro M, Nunes H, Deodato S, Dell’Acqua M, Juliani C. Fatores relacionados às readmissões ao Serviço de Atendimento Móvel de Urgência. Escola Anna Nery 2023;27 View
  7. Weil L, Zwerwer L, Chu H, Verhoeff M, Jeurissen P, van Munster B. Identifying future high healthcare utilization in patients with multimorbidity – development and internal validation of machine learning prediction models using electronic health record data. Health and Technology 2024;14(3):433 View