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Special Issue "Process-Oriented Data Science for Healthcare 2018 (PODS4H18)"

Special Issue Editors

Guest Editor
Dr. Jorge Munoz-Gama

Department of Computer Science, School of Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Chile
Website | E-Mail
Interests: Process Mining; Process Oriented Data Science; Process Analysis in Healthcare; Process Analysis in Education
Guest Editor
Dr. Carlos Fernandez-Llatas

SABIEN-ITACA Institute, Universitat Politecnica de Valencia, Camino de Vera S/N Valencia 46022, Spain
Website | E-Mail
Interests: Healthcare, Health Informatics; Process Mining; Internet Of Things; Chronic Diseases
Guest Editor
Dr. Niels Martin

Hasselt University, Research group Business Informatics
Website | E-Mail
Interests: Process simulation; Process mining; Process modelling; Healthcare processes; Healthcare facility design
Guest Editor
Dr. Owen Johnson

School of Computing, Faculty of Engineering, University of Leeds, 7.19 E C Stoner Building, Leeds, UK
Website | E-Mail
Interests: Process analytics; Electronic Health Record (EHR) systems; Health informatics strategy and implementation

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 papers will be 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 semimonthly 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 1800 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 (2 papers)

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Research

Open AccessArticle Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process
Int. J. Environ. Res. Public Health 2019, 16(2), 199; https://doi.org/10.3390/ijerph16020199
Received: 22 November 2018 / Revised: 7 January 2019 / Accepted: 9 January 2019 / Published: 11 January 2019
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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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Open AccessArticle Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance
Int. J. Environ. Res. Public Health 2018, 15(12), 2809; https://doi.org/10.3390/ijerph15122809
Received: 25 October 2018 / Revised: 4 December 2018 / Accepted: 7 December 2018 / Published: 10 December 2018
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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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Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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