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Building Process-Oriented Data Science Solutions for Real-World Healthcare

Author

Listed:
  • Carlos Fernandez-Llatas

    (SABIEN—Institute of Information and Communication Technologies (ITACA), Universitat Politecnica de Valencia, Camino de Vera S/N, 46022 Valencia, Spain
    Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 171 77 Stockholm, Sweden)

  • Niels Martin

    (Research Group Business Informatics, Hasselt University, Martelarenlaan 42, 3500 Hasselt, Belgium
    Research Foundation Flanders (FWO), Egmontstraat 5, 1000 Brussels, Belgium)

  • Owen Johnson

    (School of Computing, Faculty of Engineering, University of Leeds, Leeds LS2 9JT, UK)

  • Marcos Sepulveda

    (Department of Computer Science, School of Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago 7820436, Chile)

  • Emmanuel Helm

    (School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Softwarepark 11, 4332 Hagenberg, Austria)

  • Jorge Munoz-Gama

    (Department of Computer Science, School of Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Santiago 7820436, Chile)

Abstract

The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.

Suggested Citation

  • Carlos Fernandez-Llatas & Niels Martin & Owen Johnson & Marcos Sepulveda & Emmanuel Helm & Jorge Munoz-Gama, 2022. "Building Process-Oriented Data Science Solutions for Real-World Healthcare," IJERPH, MDPI, vol. 19(14), pages 1-5, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8427-:d:859527
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    References listed on IDEAS

    as
    1. Kraus, Sascha & Schiavone, Francesco & Pluzhnikova, Anna & Invernizzi, Anna Chiara, 2021. "Digital transformation in healthcare: Analyzing the current state-of-research," Journal of Business Research, Elsevier, vol. 123(C), pages 557-567.
    2. Maria Laura Ruiu, 2020. "Mismanagement of Covid-19: lessons learned from Italy," Journal of Risk Research, Taylor & Francis Journals, vol. 23(7-8), pages 1007-1020, August.
    3. Sung S Park & Deborah S Carr, 2021. "Caregivers’ Mental Health and Somatic Symptoms During COVID-19," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 76(4), pages 235-240.
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