A Conceptual Framework to Support Discovering of Patients' Pathways as Operational Process Charts

Araghi, Sina Namaki; Fontaili, Franck; Lamine, Elyes; Salatgé, Nicolas; Lesbegueries, Julien; Pouyade, Sebastien Rebiere; Tancerel, Ludovic; Bénaben, Frédérick

doi:10.1109/aiccsa.2018.8612822

Cited by 8 publications

(9 citation statements)

References 12 publications

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“…R.IO‐DIAG is an open‐source software developed to assist users in the implementation of PM tasks that follow the DIAG approach. DIAG is a PM methodology for the analysis of patient pathways obtained from RTLS data (Araghi, Fontaili, et al, 2018). R.IO‐DIAG allows users to perform process discovery, conformance checking, and process enhancement from event logs corresponding to location data of patients.…”

Section: Literature Discussionmentioning

confidence: 99%

“…Automated (Abo-Hamad, 2017; Alharbi et al, 2018;Alvarez et al, 2018;Amantea et al, 2020;Antonelli & Bruno, 2015;Araghi et al, 2020;Araghi, Fontaili, et al, 2018;Baker et al, 2017;Bouarfa & Dankelman, 2012;Caron et al, 2011;Caron et al, 2014;B. Chen et al, 2021;Chiudinelli et al, 2020;Cho et al, 2014;Dagliati et al, 2014;De Oliveira et al, 2020;De Weerdt et al, 2012;Duma & Aringhieri, 2020;Fei et al, 2010;Fernandez-Llatas et al, 2015;Fern andez-Llatas et al, 2010;Fern andez-Llatas, Garcia-Gomez, et al, 2011;Furniss et al, 2016;Garg & Agarwal, 2016;A.…”

Section: Discovery Approach Referencesunclassified

“…Locations are determined by means of wireless technologies as radio frequency identification (RFID) tags, integrated in patient identification bands or staff cards, and antennas which are deployed in the hospital's departments. RTLS data have been used for PM to collect patients' movements (Araghi et al, 2019(Araghi et al, , 2020Araghi, Fontaili, et al, 2018;Araghi, Fontanili, et al, 2018;Fern andez-Llatas et al, 2013;Martinez-Millana et al, 2019), to mine the workflow of medical equipments (Liu et al, 2014), to study a surgical process (Fernandez-Llatas et al, 2015), and to enhance event logs extracted from a HIS (Fernandez-Llatas et al, 2021;Martin, 2018).…”

Section: Healthcare Datamentioning

confidence: 99%

“…The same author (Fernandez-Llatas et al, 2015) tested both the heuristic miner and PALIA for mining patient pathways from RLTS data collected in a surgical ward. Miclo et al (2015) and Araghi, Fontaili, et al (2018) used the fuzzy miner, and the heuristic miner, respectively, on RTLS data to mine human movements. Prodel et al (2015) used an integer linear programming approach to discover the care process at a macroscopic scale from a large-size database.…”

Section: Discovery Approach Referencesmentioning

confidence: 99%

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Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

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Process mining (PM) is a well-known research area that includes techniques, methodologies, and tools for analyzing processes in a variety of application domains. In the case of healthcare, processes are characterized by high variability in terms of activities, duration, and involved resources (e.g., physicians, nurses, administrators, machineries, etc.). Besides, the multitude of diseases that the patients housed in healthcare facilities suffer from makes medical contexts highly heterogeneous. As a result, understanding and analyzing healthcare processes are certainly not trivial tasks, and administrators and doctors look for tools and methods that can concretely support them in improving the healthcare services they are involved in. In this context, PM has been increasingly used for a wide range of applications as reported in some recent reviews. However, these reviews mainly focus on discussion on applications related to the clinical pathways, while a systematic review of all possible applications is absent. In this article, we selected 172 papers published in the last 10 years, that present applications of PM in the healthcare domain. The objective of this study is to help and guide researchers interested in the medical field to understand the main PM applications in the healthcare, but also to suggest new ways to develop promising and not yet fully investigated applications. Moreover, our study could be of interest for practitioners who are considering applications of PM, who can identify and choose PM algorithms, techniques, tools, methodologies, and approaches, toward what have been the experiences of success.

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Section: Literature Discussionmentioning

confidence: 99%

Section: Discovery Approach Referencesunclassified

Section: Healthcare Datamentioning

confidence: 99%

Section: Discovery Approach Referencesmentioning

confidence: 99%

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Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

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“…(ii) a scale-adaptive method for refining the location of activities within outdoor and indoor environments. Cited research works location event log preparation and interpretation (Senderovich et al, 2016;Muzammal et al, 2018;Wan et al, 2017;Namaki Araghi et al, 2018a) Use of location data for extraction of knowledge (Hwang and Jang, 2017;Sztyler et al, 2016;Ertek et al, 2017;Mazimpaka and Timpf, 2016), (Rojas et al, 2017a;Zheng, 2015a;Tanuja and Govindarajulu, 2017;Ramos et al, 2017;Garaeva et al, 2017;Bao and Wang, 2017), (Zhenjiang et al, 2017;Aryal and Sujing Wang, 2017;Tanuja and Govindarajulu, 2016;Feng Ling et al, 2016), (Lamr and Skrbek, 2016;Blank et al, 2016;Fernandez-Llatas et al, 2015;Zheng, 2015b), (Liao et al, 2015;Tang et al, 2015;Miclo et al, 2015;Jin et al, 2015;Martinez-Millana et al, 2019), (Dogan et al, 2019;Namaki Araghi et al, 2019;Namaki Araghi et al, 2018a;Namaki Araghi et al, 2018b;Araghi et al, 2018) Figure 3: Analysis of the literature of process mining, indoor localization systems, and data mining relevant to the approaches in which the location data were used.…”

Section: Preparation and Interpretation Of Location Datamentioning

confidence: 99%

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Araghi,

Fontanili,

Lamine

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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The application of indoor localization and process mining emerges as an intriguing tool for the researchers to address the structural issues related to the patient pathways inside healthcare organizations. However, there is a major gap in the literature. This is related to the lack of enough attention to the interpretation of location data. Therefore, as a contribution, this article presents the DIAG meta-model and relevant location data interpretation rules. This model-driven approach has been realized in the context of the R-IOSUITE application and it supports the further analyses by the process mining methods.

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