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Process-Oriented Data Science for Healthcare 2018 (PODS4H18)

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (15 April 2019) | Viewed by 29248

Special Issue Editors

Department of Computer Science, School of Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Chile
Interests: process mining; process oriented data science; process analysis in healthcare; process analysis in education
Special Issues, Collections and Topics in MDPI journals
1. SABIEN-ITACA Institute, Universitat Politecnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain
2. Department of Clinical Science, Intervention and Technology(CLINTEC), Karolinska Institutet, 171 77 Stockholm, Sweden
Interests: healthcare; health informatics; process mining; internet of things; chronic diseases
Special Issues, Collections and Topics in MDPI journals
Research Group Business Informatics, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium
Interests: process simulation; process mining; process modelling; healthcare processes; healthcare facility design
Special Issues, Collections and Topics in MDPI journals
School of Computing, Faculty of Engineering, University of Leeds, 7.19 E C Stoner Building, Leeds LS2 9JT, UK
Interests: process analytics; electronic health record (EHR) systems; health informatics; AI and implementation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world’s most valuable resource is no longer oil, but data. The ultimate goal of data science techniques is not to collect more data, but to extract knowledge and insights from existing data in various forms. For analyzing and improving processes, event data is the main source of information. In recent years, a new research area has emerged combining traditional process analysis and data-centric analysis: Process-Oriented Data Science (PODS). The interdisciplinary nature of this new research area has resulted in its application for analyzing processes in different domains, especially healthcare.

This Special Issue aims at providing a high-quality forum for interdisciplinary researchers and practitioners (both data/process analysts and medical audience) to exchange research findings and ideas on healthcare process analysis techniques and practices. Process-Oriented Data Science for Healthcare (PODS4H) research includes a wide range of topics from process mining techniques adapted for healthcare processes, to practical issues on implementing PODS methodologies in healthcare centers’ analysis units.

This Special Issue includes the extended versions of the accepted articles in the ‘International Workshop on Process-Oriented Data Science 2018’, presenting novel research that demonstrates the potential of PODS approaches for analyzing the way healthcare is delivered.

Dr. Jorge Munoz-Gama
Dr. Carlos Fernandez-Llatas
Dr. Niels Martin
Dr. Owen Johnson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Process Mining in Healthcare
  • Process Discovery and Data-aided Process Modeling in Healthcare
  • Conformance Checking and Compliance Analysis of Healthcare Processes
  • Data-aided Process Enhancement and Repair
  • Healthcare Process Prediction and Recommendation
  • Healthcare Process Simulation
  • Healthcare Process Optimization
  • Process-Aware Hospital Information Systems Analysis and Data Extraction
  • Interfaces for PODS4H
  • Disease-driven PODS4H
  • Methodologies and Best Practices for PODS4H
  • Case Studies and Application of PODS4H
  • WACI (Wild And Crazy Ideas) for PODS4H

Published Papers (6 papers)

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Research

21 pages, 16624 KiB  
Article
Process-Oriented Feedback through Process Mining for Surgical Procedures in Medical Training: The Ultrasound-Guided Central Venous Catheter Placement Case
by Ricardo Lira, Juan Salas-Morales, Luis Leiva, Rene de la Fuente, Ricardo Fuentes, Alejandro Delfino, Claudia Hurtado Nazal, Marcos Sepúlveda, Michael Arias, Valeria Herskovic and Jorge Munoz-Gama
Int. J. Environ. Res. Public Health 2019, 16(11), 1877; https://doi.org/10.3390/ijerph16111877 - 28 May 2019
Cited by 16 | Viewed by 4414
Abstract
Developing high levels of competence in the execution of surgical procedures through training is a key factor for obtaining good clinical results in healthcare. To improve the effectiveness of the training, it is advisable to provide feedback to each student tailored to how [...] Read more.
Developing high levels of competence in the execution of surgical procedures through training is a key factor for obtaining good clinical results in healthcare. To improve the effectiveness of the training, it is advisable to provide feedback to each student tailored to how the student has performed the procedure on each occasion. Current state-of-the-art feedback is based on Checklists and Global Rating Scales, which indicate whether all process steps have been carried out and the quality of each execution step. However, there is a process perspective that is not captured successfully by these instruments, e.g., steps performed, but in an undesired order, group of activities that are repeated an unnecessary number of times, or an excessive transition time between two consecutive steps. In this research, we propose a novel use of process mining techniques to effectively identify desired and undesired process patterns regarding rework, the order in which activities are performed, and time performance, in order to complement the tailored feedback for surgical procedures using a process perspective. The proposed approach was applied to analyze a real case of ultrasound-guided Central Venous Catheter placement training. It was quantitatively and qualitatively validated that the students who participated in the training program perceived the process-oriented feedback they received as favorable for their learning. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2018 (PODS4H18))
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22 pages, 2874 KiB  
Article
Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case
by Gema Ibanez-Sanchez, Carlos Fernandez-Llatas, Antonio Martinez-Millana, Angeles Celda, Jesus Mandingorra, Lucia Aparici-Tortajada, Zoe Valero-Ramon, Jorge Munoz-Gama, Marcos Sepúlveda, Eric Rojas, Víctor Gálvez, Daniel Capurro and Vicente Traver
Int. J. Environ. Res. Public Health 2019, 16(10), 1783; https://doi.org/10.3390/ijerph16101783 - 20 May 2019
Cited by 33 | Viewed by 6578
Abstract
The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of [...] Read more.
The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2018 (PODS4H18))
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20 pages, 1324 KiB  
Article
Performance Analysis of Emergency Room Episodes Through Process Mining
by Eric Rojas, Andres Cifuentes, Andrea Burattin, Jorge Munoz-Gama, Marcos Sepúlveda and Daniel Capurro
Int. J. Environ. Res. Public Health 2019, 16(7), 1274; https://doi.org/10.3390/ijerph16071274 - 10 Apr 2019
Cited by 24 | Viewed by 3827
Abstract
The performance analysis of Emergency Room episodes is aimed at providing decision makers with knowledge that allows them to decrease waiting times, reduce patient congestion, and improve the quality of care provided. In this case study, Process Mining is used to determine which [...] Read more.
The performance analysis of Emergency Room episodes is aimed at providing decision makers with knowledge that allows them to decrease waiting times, reduce patient congestion, and improve the quality of care provided. In this case study, Process Mining is used to determine which activities, sub-processes, interactions, and characteristics of episodes explain why some episodes have a longer duration. The employed method and the results obtained are described in detail to serve as a guide for future performance analysis in this domain. It was discovered that the main cause of the increment in the episode duration is the occurrence of a loop between the Examination and Treatment sub-processes. It was also found out that as the episode severity increases, the number of repetitions of the Examination–Treatment loop increases as well. Moreover, the episodes in which this loop is more common are those that lead to Hospitalization as discharge destination. These findings might help to reduce the occurrence of this loop, in turn lowering the episode duration and, consequently, providing faster attention to more patients. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2018 (PODS4H18))
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25 pages, 2004 KiB  
Article
Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland
by Robert Andrews, Moe T. Wynn, Kirsten Vallmuur, Arthur H. M. ter Hofstede, Emma Bosley, Mark Elcock and Stephen Rashford
Int. J. Environ. Res. Public Health 2019, 16(7), 1138; https://doi.org/10.3390/ijerph16071138 - 29 Mar 2019
Cited by 29 | Viewed by 4683
Abstract
While noting the importance of data quality, existing process mining methodologies (i) do not provide details on how to assess the quality of event data (ii) do not consider how the identification of data quality issues can be exploited in the planning, data [...] Read more.
While noting the importance of data quality, existing process mining methodologies (i) do not provide details on how to assess the quality of event data (ii) do not consider how the identification of data quality issues can be exploited in the planning, data extraction and log building phases of any process mining analysis, (iii) do not highlight potential impacts of poor quality data on different types of process analyses. As our key contribution, we develop a process-centric, data quality-driven approach to preparing for a process mining analysis which can be applied to any existing process mining methodology. Our approach, adapted from elements of the well known CRISP-DM data mining methodology, includes conceptual data modeling, quality assessment at both attribute and event level, and trial discovery and conformance to develop understanding of system processes and data properties to inform data extraction. We illustrate our approach in a case study involving the Queensland Ambulance Service (QAS) and Retrieval Services Queensland (RSQ). We describe the detailed preparation for a process mining analysis of retrieval and transport processes (ground and aero-medical) for road-trauma patients in Queensland. Sample datasets obtained from QAS and RSQ are utilised to show how quality metrics, data models and exploratory process mining analyses can be used to (i) identify data quality issues, (ii) anticipate and explain certain observable features in process mining analyses, (iii) distinguish between systemic and occasional quality issues, and (iv) reason about the mechanisms by which identified quality issues may have arisen in the event log. We contend that this knowledge can be used to guide the data extraction and pre-processing stages of a process mining case study to properly align the data with the case study research questions. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2018 (PODS4H18))
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14 pages, 865 KiB  
Article
Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process
by Antonio Martinez-Millana, Aroa Lizondo, Roberto Gatta, Salvador Vera, Vicente Traver Salcedo and Carlos Fernandez-Llatas
Int. J. Environ. Res. Public Health 2019, 16(2), 199; https://doi.org/10.3390/ijerph16020199 - 11 Jan 2019
Cited by 21 | Viewed by 5326
Abstract
The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to [...] Read more.
The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n = 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2018 (PODS4H18))
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14 pages, 806 KiB  
Article
Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance
by Christoph Rinner, Emmanuel Helm, Reinhold Dunkl, Harald Kittler and Stefanie Rinderle-Ma
Int. J. Environ. Res. Public Health 2018, 15(12), 2809; https://doi.org/10.3390/ijerph15122809 - 10 Dec 2018
Cited by 16 | Viewed by 3894
Abstract
Background: Process mining is a relatively new discipline that helps to discover and analyze actual process executions based on log data. In this paper we apply conformance checking techniques to the process of surveillance of melanoma patients. This process consists of recurring events [...] Read more.
Background: Process mining is a relatively new discipline that helps to discover and analyze actual process executions based on log data. In this paper we apply conformance checking techniques to the process of surveillance of melanoma patients. This process consists of recurring events with time constraints between the events. Objectives: The goal of this work is to show how existing clinical data collected during melanoma surveillance can be prepared and pre-processed to be reused for process mining. Methods: We describe an approach based on time boxing to create process models from medical guidelines and the corresponding event logs from clinical data of patient visits. Results: Event logs were extracted for 1023 patients starting melanoma surveillance at the Department of Dermatology at the Medical University of Vienna between January 2010 and June 2017. Conformance checking techniques available in the ProM framework and explorative applied process mining techniques were applied. Conclusions: The presented time boxing enables the direct use of existing process mining frameworks like ProM to perform process-oriented analysis also with respect to time constraints between events. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2018 (PODS4H18))
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