–This event is organised in conjunction with the International Conference on Business Process Management (BPM 2018) in Sydney on September 10, 2018–
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 discipline 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 such as education, finances, and especially healthcare.
The International Workshop on Process-Oriented Data Science for Healthcare 2018 (PODS4H18) 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. 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 workshop is an initiative of the Process-Oriented Data Science for Healthcare Alliance.
Sponsors & Partners
Submitted works should involve the analysis, management, or improvement of processes using recorded data in the healthcare domain. Non process-centric approaches will fall out of scope. The workshop aims at both more theoretical contributions such as new techniques and algorithms, and more applied contributions such as methodologies and case studies.
The topics of interest include, but are not limited to:
- 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
Two types of submissions are accepted:
Regular: Submitted papers will be evaluated on the basis of significance, originality, technical quality, and potential to generate relevant discussion. Submissions must use the Springer LNCS/LNBIP format. Submissions must be in English and must not exceed 12 pages (including figures, bibliography and appendices). Each paper should clarify the relation of the paper with the workshop main topics, clearly state the problem being addressed, the goal of the work, the results achieved, and the relation to other work. Papers should be submitted electronically as a self-contained PDF file via the Easychair submission system (https://easychair.org/conferences/?conf=pods4h). Submissions must be original contributions that have not been published previously, nor already submitted to other conferences or journals in parallel with this workshop. Accepted papers will be published by Springer as a post-workshop proceedings volume in the series Lecture Notes in Business Information Processing (LNBIP). At least one author of each accepted paper must register and participate in the workshop. The best papers will be invited to submit an extended version on a fast-track at a JCR journal Special Issue (see Journal Special Issue section).
Success Cases: Success cases are short accessible communications describing a PODS4H experience performed by a group or a company in a specific scenario, along with some analysis that provides insights and learning for the future (examples available here). Submissions must be in English and must not exceed 2 pages. The format is free (reference material available here), but the document must include a highly visible ‘International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) – Success Case’ reference. Papers should be submitted electronically as a self-contained PDF file via the submission system (https://easychair.org/conferences/?conf=pods4h). At least one author of each accepted paper must register and participate in the workshop. Accepted cases that qualify will be published online by the IEEE Task Force on Process Mining as part of their Case Studies series.
Journal Special Issue
The best Regular papers will be invited to submit an extended version on a fast-track at the Special Issue on Process-Oriented Data Science of the International Journal of Environmental Research and Public Health (IJERPH). IJERPH is indexed in the Journal Citation Reports – Thomson Reuters index, with a 5-Year Impact Factor of 2.54. The expanded workshop paper must fulfil the following requirements: (1) the paper should be expanded to the size of a journal research article; (2) the workshop paper should be cited and noted on the first page of the paper; (3) if the authors do not hold the copyright of the published workshop paper, authors should seek the appropriate permission from the copyright holder; (4) authors are asked to disclose that it is workshop paper in their cover letter and include a statement on what has been changed compared to the original workshop paper. Notice that IJERPH is an open-access journal, and it is the authors responsibility to cover the Article Processing Charges (http://www.mdpi.com/journal/ijerph/apc) if the paper is finally accepted.
Abstract submission deadline (Regular):
18 May 2018 (with the paper submission)
Paper submission deadline (Regular):
25 May 2018 1 June 2018
Notification of acceptance (Regular): 29 June 2018
Camera ready (Regular): 13 July 2018
Success Case submission deadline: 9 July 2018
Success Case notification of acceptance: 20 July 2018
Workshop day: 10 September 2018
*Deadlines correspond to anywhere on earth (‘AoE’ or ‘UTC-12′)
It is a full-day workshop. The program includes the presentation of the academic work, and a round-table meeting session to discuss the current state of the discipline and future community efforts.
- Jorge Munoz-Gama, Pontificia Universidad Catòlica de Chile (Chile)
- Carlos Fernandez-Llatas, Universitat Politècnica de Valencia (Spain)
- Niels Martin, Hasselt University (Belgium)
- Owen Johnson, University of Leeds (United Kingdom)
The workshop is an initiative of the Process-Oriented Data Science for Healthcare Alliance. The goal of this international alliance is to promote the research, development, education and understanding of process-oriented data science in healthcare. For more information about the activities and its members visit pods4h.com/alliance
- Robert Andrews, Queensland University of Technology
- Joos Buijs, Eindhoven University of Technology
- Andrea Burattin, Technical University of Denmark
- Dr. Daniel Capurro, Pontificia Universidad Católica de Chile
- Josep Carmona, Universitat Politècnica de Catalunya
- Carlos Fernandez-Llatas, Universitat Politècnica de Valencia
- Dr. René de la Fuente, Pontificia Universidad Católica de Chile
- Roberto Gatta, Università Cattolica del Sacro Cuore
- Zhengxing Huang, Zhejiang University
- Owen Johnson, University of Leeds
- Felix Mannhardt, SINTEF
- Ronny Mans, VitalHealth Software
- Niels Martin, Hasselt University
- Renata Medeiros de Carvalho, Eindhoven University of Technology
- Jorge Munoz-Gama, Pontificia Universidad Católica de Chile
- Ricardo Quintano, Pontifícia Universidade Católica do Rio de Janeiro / Philips Research
- David Riaño, Universitat Rovira i Virgili
- Eric Rojas, Pontificia Universidad Católica de Chile
- Lucia Sacchi, University of Pavia
- Fernando Seoane, Karolinska Institutet
- Marcos Sepúlveda, Pontificia Universidad Católica de Chile
- Minseok Song, Pohang University of Science and Technology
- Vicente Traver, Universitat Politècnica de Valencia
- Wil van der Aalst, RWTH Aachen University
- Rob Vanwersch, Maastricht University Medical Center
- Chuck Webster, EHR Workflow Inc.