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Process-Oriented Data Science for Healthcare 2019 (PODS4H19)

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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: closed (9 August 2020) | Viewed by 57242

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Dr. Jorge Munoz-Gama

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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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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
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Dr. Niels Martin

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Research Group Business Informatics, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium
Interests: process simulation; process mining; process modelling; healthcare processes; healthcare facility design
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Dr. Owen Johnson

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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
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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

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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 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

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Published Papers (12 papers)

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16 pages, 2973 KiB

Open AccessArticle

Using a Multi-Level Process Comparison for Process Change Analysis in Cancer Pathways

by Angelina Prima Kurniati, Ciarán McInerney, Kieran Zucker, Geoff Hall, David Hogg and Owen Johnson

Int. J. Environ. Res. Public Health 2020, 17(19), 7210; https://doi.org/10.3390/ijerph17197210 - 1 Oct 2020

Cited by 13 | Viewed by 3441

Abstract

The area of process change over time is a particular concern in healthcare, where patterns of care emerge and evolve in response to individual patient needs. We propose a structured approach to analyse process change over time that is suitable for the complex domain of healthcare. Our approach applies a qualitative process comparison at three levels of abstraction: a holistic perspective (process model), a middle-level perspective (trace), and a fine-grained detail (activity). Our aim was to detect change points, localise and characterise the change, and unravel/understand the process evolution. We illustrate the approach using a case study of cancer pathways in Leeds where we found evidence of change points identified at multiple levels. In this paper, we extend our study by analysing the miners used in process discovery and providing a deeper analysis of the activity of investigation in trace and activity levels. In the experiment, we show that this qualitative approach provides a useful understanding of process change over time. Examining change at three levels provides confirmatory evidence of process change where perspectives agree, while contradictory evidence can lead to focused discussions with domain experts. This approach should be of interest to others dealing with processes that undergo complex change over time. Full article

(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))

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16 pages, 1985 KiB

Open AccessArticle

Mapping the Patient’s Journey in Healthcare through Process Mining

by Michael Arias, Eric Rojas, Santiago Aguirre, Felipe Cornejo, Jorge Munoz-Gama, Marcos Sepúlveda and Daniel Capurro

Int. J. Environ. Res. Public Health 2020, 17(18), 6586; https://doi.org/10.3390/ijerph17186586 - 10 Sep 2020

Cited by 33 | Viewed by 12611

Abstract

Nowadays, assessing and improving customer experience has become a priority, and has emerged as a key differentiator for business and organizations worldwide. A customer journey (CJ) is a strategic tool, a map of the steps customers follow when engaging with a company or organization to obtain a product or service. The increase of the need to obtain knowledge about customers’ perceptions and feelings when interacting with participants, touchpoints, and channels through different stages of the customer life cycle. This study aims to describe the application of process mining techniques in healthcare as a tool to asses customer journeys. The appropriateness of the approach presented is illustrated through a case study of a key healthcare process. Results depict how a healthcare process can be mapped through the CJ components, and its analysis can serve to understand and improve the patient’s experience. Full article

(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))

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20 pages, 1197 KiB

Open AccessArticle

Process Mining-Supported Emergency Room Process Performance Indicators

by Minsu Cho, Minseok Song, Junhyun Park, Seok-Ran Yeom, Il-Jae Wang and Byung-Kwan Choi

Int. J. Environ. Res. Public Health 2020, 17(17), 6290; https://doi.org/10.3390/ijerph17176290 - 28 Aug 2020

Cited by 19 | Viewed by 4272

Abstract

Emergency room processes are often exposed to the risk of unexpected factors, and process management based on performance measurements is required due to its connectivity to the quality of care. Regarding this, there have been several attempts to propose a method to analyze the emergency room processes. This paper proposes a framework for process performance indicators utilized in emergency rooms. Based on the devil’s quadrangle, i.e., time, cost, quality, and flexibility, the paper suggests multiple process performance indicators that can be analyzed using clinical event logs and verify them with a thorough discussion with clinical experts in the emergency department. A case study is conducted with the real-life clinical data collected from a tertiary hospital in Korea to validate the proposed method. The case study demonstrated that the proposed indicators are well applied using the clinical data, and the framework is capable of understanding emergency room processes’ performance. Full article

(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))

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23 pages, 1711 KiB

Open AccessArticle

Automatic Process Comparison for Subpopulations: Application in Cancer Care

by Francesca Marazza, Faiza Allah Bukhsh, Jeroen Geerdink, Onno Vijlbrief, Shreyasi Pathak, Maurice van Keulen and Christin Seifert

Int. J. Environ. Res. Public Health 2020, 17(16), 5707; https://doi.org/10.3390/ijerph17165707 - 7 Aug 2020

Cited by 11 | Viewed by 3335

Abstract

Processes in organisations, such as hospitals, may deviate from the intended standard processes, due to unforeseeable events and the complexity of the organisation. For hospitals, the knowledge of actual patient streams for patient populations (e.g., severe or non-severe cases) is important for quality control and improvement. Process discovery from event data in electronic health records can shed light on the patient flows, but their comparison for different populations is cumbersome and time-consuming. In this paper, we present an approach for the automatic comparison of process models that were extracted from events in electronic health records. Concretely, we propose comparing processes for different patient populations by cross-log conformance checking, and standard graph similarity measures obtained from the directed graph underlying the process model. We perform a user study with 20 participants in order to obtain a ground truth for similarity of process models. We evaluate our approach on two data sets, the publicly available MIMIC database with the focus on different cancer patients in intensive care, and a database on breast cancer patients from a Dutch hospital. In our experiments, we found average fitness to be a good indicator for visual similarity in the ZGT use case, while the average precision and graph edit distance are strongly correlated with visual impression for cancer process models on MIMIC. These results are a call for further research and evaluation for determining which similarity or combination of similarities is needed in which type of process model comparison. Full article

(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))

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12 pages, 1503 KiB

Open AccessArticle

Delphi Method to Achieve Clinical Consensus for a BPMN Representation of the Central Venous Access Placement for Training Purposes

by Rene de la Fuente, Ricardo Fuentes, Jorge Munoz-Gama, Jorge Dagnino and Marcos Sepúlveda

Int. J. Environ. Res. Public Health 2020, 17(11), 3889; https://doi.org/10.3390/ijerph17113889 - 30 May 2020

Cited by 8 | Viewed by 3318

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 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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15 pages, 714 KiB

Open AccessArticle

Process-Oriented Instrument and Taxonomy for Teaching Surgical Procedures in Medical Training: The Ultrasound-Guided Insertion of Central Venous Catheter

by Victor Galvez, Rene de la Fuente, Cesar Meneses, Luis Leiva, Gonzalo Fagalde, Valeria Herskovic, Ricardo Fuentes, Jorge Munoz-Gama and Marcos Sepúlveda

Int. J. Environ. Res. Public Health 2020, 17(11), 3849; https://doi.org/10.3390/ijerph17113849 - 29 May 2020

Cited by 2 | Viewed by 3084

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 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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22 pages, 3800 KiB

Open AccessArticle

A Comparative Process Mining Analysis of Road Trauma Patient Pathways

by Robert Andrews, Moe T. Wynn, Kirsten Vallmuur, Arthur H. M. ter Hofstede and Emma Bosley

Int. J. Environ. Res. Public Health 2020, 17(10), 3426; https://doi.org/10.3390/ijerph17103426 - 14 May 2020

Cited by 18 | Viewed by 3930

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 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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28 pages, 970 KiB

Open AccessArticle

Privacy-Preserving Process Mining in Healthcare

by Anastasiia Pika, Moe T. Wynn, Stephanus Budiono, Arthur H.M. ter Hofstede, Wil M.P. van der Aalst and Hajo A. Reijers

Int. J. Environ. Res. Public Health 2020, 17(5), 1612; https://doi.org/10.3390/ijerph17051612 - 2 Mar 2020

Cited by 63 | Viewed by 8082

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 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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12 pages, 389 KiB

Open AccessArticle

Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare

by Emmanuel Helm, Anna M. Lin, David Baumgartner, Alvin C. Lin and Josef Küng

Int. J. Environ. Res. Public Health 2020, 17(4), 1348; https://doi.org/10.3390/ijerph17041348 - 19 Feb 2020

Cited by 11 | Viewed by 4484

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 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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1 pages, 227 KiB

Open AccessReply

Reply to “On Clinical Utility and Systematic Reporting in Case Studies of Healthcare Process Mining. Comment on: 10.3390/ijerph17041348 ‘Towards the Use of Standardised Terms in Clinical Case Studies for Process Mining in Healthcare’ ”

by Emmanuel Helm, Anna M. Lin, David Baumgartner, Alvin C. Lin and Josef Küng

Int. J. Environ. Res. Public Health 2020, 17(22), 8583; https://doi.org/10.3390/ijerph17228583 - 19 Nov 2020

Viewed by 2792

Abstract

Many thanks to Dr. Mordaunt for his thoughtful Comment, which we were delighted to read with great interest[...] Full article

(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))

4 pages, 259 KiB

Open AccessComment

On Clinical Utility and Systematic Reporting in Case Studies of Healthcare Process Mining. Comment on: 10.3390/ijerph17041348 “Towards the Use of Standardised Terms in Clinical Case Studies for Process Mining in Healthcare”

by Dylan A. Mordaunt

Int. J. Environ. Res. Public Health 2020, 17(22), 8298; https://doi.org/10.3390/ijerph17228298 - 10 Nov 2020

Viewed by 1940

Abstract

Recently in Environmental Research and Public Health, Helm and colleagues reported on a systematic review of healthcare process mining (HPM) case reports, focusing on the reporting of technical and clinical aspects and discussing standardisation terms in future HCM reports utilising existing ontologies [...] Read more.

(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))

19 pages, 622 KiB

Open AccessPerspective

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

by Roberto Gatta, Mauro Vallati, Carlos Fernandez-Llatas, Antonio Martinez-Millana, Stefania Orini, Lucia Sacchi, Jacopo Lenkowicz, Mar Marcos, Jorge Munoz-Gama, Michel A. Cuendet, Berardino de Bari, Luis Marco-Ruiz, Alessandro Stefanini, Zoe Valero-Ramon, Olivier Michielin, Tomas Lapinskas, Antanas Montvila, Niels Martin, Erica Tavazzi and Maurizio Castellano

Int. J. Environ. Res. Public Health 2020, 17(18), 6616; https://doi.org/10.3390/ijerph17186616 - 11 Sep 2020

Cited by 13 | Viewed by 4847

Abstract

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community? Full article

(This article belongs to the Special Issue Process-Oriented Data Science for Healthcare 2019 (PODS4H19))

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