Literature DB >> 35104641

Process mining for healthcare: Characteristics and challenges.

Jorge Munoz-Gama1, Niels Martin2, Carlos Fernandez-Llatas3, Owen A Johnson4, Marcos Sepúlveda1, Emmanuel Helm5, Victor Galvez-Yanjari1, Eric Rojas1, Antonio Martinez-Millana6, Davide Aloini7, Ilaria Angela Amantea8, Robert Andrews9, Michael Arias10, Iris Beerepoot11, Elisabetta Benevento7, Andrea Burattin12, Daniel Capurro13, Josep Carmona14, Marco Comuzzi15, Benjamin Dalmas16, Rene de la Fuente1, Chiara Di Francescomarino17, Claudio Di Ciccio18, Roberto Gatta19, Chiara Ghidini17, Fernanda Gonzalez-Lopez1, Gema Ibanez-Sanchez6, Hilda B Klasky20, Angelina Prima Kurniati21, Xixi Lu11, Felix Mannhardt22, Ronny Mans23, Mar Marcos24, Renata Medeiros de Carvalho22, Marco Pegoraro25, Simon K Poon26, Luise Pufahl27, Hajo A Reijers28, Simon Remy29, Stefanie Rinderle-Ma30, Lucia Sacchi31, Fernando Seoane32, Minseok Song33, Alessandro Stefanini7, Emilio Sulis34, Arthur H M Ter Hofstede9, Pieter J Toussaint35, Vicente Traver6, Zoe Valero-Ramon6, Inge van de Weerd11, Wil M P van der Aalst25, Rob Vanwersch22, Mathias Weske29, Moe Thandar Wynn9, Francesca Zerbato36.   

Abstract

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
Copyright © 2022 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Healthcare; Process mining

Mesh:

Year:  2022        PMID: 35104641     DOI: 10.1016/j.jbi.2022.103994

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  4 in total

1.  A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.

Authors:  Arianna Dagliati; Roberto Gatta; Alberto Malovini; Valentina Tibollo; Lucia Sacchi; Fidelia Cascini; Luca Chiovato; Riccardo Bellazzi
Journal:  Front Public Health       Date:  2022-05-23

Review 2.  Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis.

Authors:  Alberto Eugenio Tozzi; Francesco Fabozzi; Megan Eckley; Ileana Croci; Vito Andrea Dell'Anna; Erica Colantonio; Angela Mastronuzzi
Journal:  Front Oncol       Date:  2022-05-31       Impact factor: 5.738

3.  How do I update my model? On the resilience of Predictive Process Monitoring models to change.

Authors:  Williams Rizzi; Chiara Di Francescomarino; Chiara Ghidini; Fabrizio Maria Maggi
Journal:  Knowl Inf Syst       Date:  2022-03-21       Impact factor: 2.531

4.  Building Process-Oriented Data Science Solutions for Real-World Healthcare.

Authors:  Carlos Fernandez-Llatas; Niels Martin; Owen Johnson; Marcos Sepulveda; Emmanuel Helm; Jorge Munoz-Gama
Journal:  Int J Environ Res Public Health       Date:  2022-07-10       Impact factor: 4.614

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.