Special Issue "Process-Oriented Data Science for Healthcare 2019 (PODS4H19)"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences & Services".

Deadline for manuscript submissions: 9 August 2020.

Special Issue Editors

Dr. Jorge Munoz-Gama
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Guest Editor
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
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Dr. Carlos Fernandez-Llatas
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Guest Editor
SABIEN-ITACA Institute, Universitat Politecnica de Valencia, Camino de Vera S/N Valencia 46022, Spain
Interests: Healthcare, Health Informatics, Process Mining, Internet Of Things, Chronic Diseases
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Dr. Niels Martin
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Guest Editor
Hasselt University, Research group Business Informatics
Interests: Process simulation, Process mining, Process modelling, Healthcare processes, Healthcare facility design
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Dr. Owen Johnson
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Guest Editor
School of Computing, Faculty of Engineering, University of Leeds, 7.19 E C Stoner Building
Interests: Process analytics; Electronic Health Record (EHR) systems; Health informatics strategy and implementation
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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. Event data is the main source of information for the analysis and improvement of processes. 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 the analysis of 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 a 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 the analysis units of healthcare centres.

This Special Issue includes the extended versions of accepted articles from the ‘International Workshop on Process-Oriented Data Science 2019’, presenting novel research that demonstrates the potential of PODS approaches in analysing 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 2300 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 Modelling in Healthcare
  • Conformance Checking and Compliance Analysis of Healthcare Processes
  • Data-Aided Process Enhancement and Repair
  • Healthcare Process Prediction and Recommendations
  • 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 (5 papers)

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Research

Open AccessArticle
Delphi Method to Achieve Clinical Consensus for a BPMN Representation of the Central Venous Access Placement for Training Purposes
Int. J. Environ. Res. Public Health 2020, 17(11), 3889; https://doi.org/10.3390/ijerph17113889 - 30 May 2020
Abstract
Proper teaching of the technical skills necessary to perform a medical procedure begins with its breakdown into its constituent steps. Currently available methodologies require substantial resources and their results may be biased. Therefore, it is difficult to generate the necessary breakdown capable of [...] Read more.
Proper teaching of the technical skills necessary to perform a medical procedure begins with its breakdown into its constituent steps. Currently available methodologies require substantial resources and their results may be biased. Therefore, it is difficult to generate the necessary breakdown capable of supporting a procedural curriculum. The aim of our work was to breakdown the steps required for ultrasound guided Central Venous Catheter (CVC) placement and represent this procedure graphically using the standard BPMN notation. Methods: We performed the first breakdown based on the activities defined in validated evaluation checklists, which were then graphically represented in BPMN. In order to establish clinical consensus, we used the Delphi method by conducting an online survey in which experts were asked to score the suitability of the proposed activities and eventually propose new activities. Results: Surveys were answered by 13 experts from three medical specialties and eight different institutions in two rounds. The final model included 28 activities proposed in the initial model and four new activities proposed by the experts; seven activities from the initial model were excluded. Conclusions: The proposed methodology proved to be simple and effective, generating a graphic representation to represent activities, decision points, and alternative paths. This approach is complementary to more classical representations for the development of a solid knowledge base that allows the standardization of medical procedures for teaching purposes. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))
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Open AccessArticle
Process-Oriented Instrument and Taxonomy for Teaching Surgical Procedures in Medical Training: The Ultrasound-Guided Insertion of Central Venous Catheter
Int. J. Environ. Res. Public Health 2020, 17(11), 3849; https://doi.org/10.3390/ijerph17113849 - 29 May 2020
Abstract
Procedural training is relevant for physicians who perform surgical procedures. In the medical education field, instructors who teach surgical procedures need to understand how their students are learning to give them feedback and assess them objectively. The sequence of steps of surgical procedures [...] Read more.
Procedural training is relevant for physicians who perform surgical procedures. In the medical education field, instructors who teach surgical procedures need to understand how their students are learning to give them feedback and assess them objectively. The sequence of steps of surgical procedures is an aspect rarely considered in medical education, and state-of-the-art tools for giving feedback and assessing students do not focus on this perspective. Process Mining can help to include this perspective in this field since it has recently been used successfully in some applications. However, these previous developments are more centred on students than on instructors. This paper presents the use of Process Mining to fill this gap, generating a taxonomy of activities and a process-oriented instrument. We evaluated both tools with instructors who teach central venous catheter insertion. The results show that the instructors found both tools useful to provide objective feedback and objective assessment. We concluded that the instructors understood the information provided by the instrument since it provides helpful information to understand students’ performance regarding the sequence of steps followed. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))
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Open AccessArticle
A Comparative Process Mining Analysis of Road Trauma Patient Pathways
Int. J. Environ. Res. Public Health 2020, 17(10), 3426; https://doi.org/10.3390/ijerph17103426 - 14 May 2020
Abstract
In this paper we report on key findings and lessons from a process mining case study conducted to analyse transport pathways discovered across the time-critical phase of pre-hospital care for persons involved in road traffic crashes in Queensland (Australia). In this study, a [...] Read more.
In this paper we report on key findings and lessons from a process mining case study conducted to analyse transport pathways discovered across the time-critical phase of pre-hospital care for persons involved in road traffic crashes in Queensland (Australia). In this study, a case is defined as being an individual patient’s journey from roadside to definitive care. We describe challenges in constructing an event log from source data provided by emergency services and hospitals, including record linkage (no standard patient identifier), and constructing a unified view of response, retrieval, transport and pre-hospital care from interleaving processes of the individual service providers. We analyse three separate cohorts of patients according to their degree of interaction with Queensland Health’s hospital system (C1: no transport required, C2: transported but no Queensland Health hospital, C3: transported and hospitalisation). Variant analysis and subsequent process modelling show high levels of variance in each cohort resulting from a combination of data collection, data linkage and actual differences in process execution. For Cohort 3, automated process modelling generated ’spaghetti’ models. Expert-guided editing resulted in readable models with acceptable fitness, which were used for process analysis. We also conduct a comparative performance analysis of transport segment based on hospital ‘remoteness’. With regard to the field of process mining, we reach various conclusions including (i) in a complex domain, the current crop of automated process algorithms do not generate readable models, however, (ii) such models provide a starting point for expert-guided editing of models (where the tool allows) which can yield models that have acceptable quality and are readable by domain experts, (iii) process improvement opportunities were largely suggested by domain experts (after reviewing analysis results) rather than being directly derived by process mining tools, meaning that the field needs to become more prescriptive (automated derivation of improvement opportunities). Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))
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Open AccessArticle
Privacy-Preserving Process Mining in Healthcare
Int. J. Environ. Res. Public Health 2020, 17(5), 1612; https://doi.org/10.3390/ijerph17051612 - 02 Mar 2020
Cited by 1
Abstract
Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information [...] Read more.
Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))
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Open AccessArticle
Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare
Int. J. Environ. Res. Public Health 2020, 17(4), 1348; https://doi.org/10.3390/ijerph17041348 - 19 Feb 2020
Abstract
Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several [...] Read more.
Process mining can provide greater insight into medical treatment processes and organizational processes in healthcare. To enhance comparability between processes, the quality of the labelled-data is essential. A literature review of the clinical case studies by Rojas et al. in 2016 identified several common aspects for comparison, which include methodologies, algorithms or techniques, medical fields, and healthcare specialty. However, clinical aspects are not reported in a uniform way and do not follow a standard clinical coding scheme. Further, technical aspects such as details of the event log data are not always described. In this paper, we identified 38 clinically-relevant case studies of process mining in healthcare published from 2016 to 2018 that described the tools, algorithms and techniques utilized, and details on the event log data. We then correlated the clinical aspects of patient encounter environment, clinical specialty and medical diagnoses using the standard clinical coding schemes SNOMED CT and ICD-10. The potential outcomes of adopting a standard approach for describing event log data and classifying medical terminology using standard clinical coding schemes are further discussed. A checklist template for the reporting of case studies is provided in the Appendix A to the article. Full article
(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))
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