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  • Designing a Machine Learning-based System to Augment the Work Processes of Medical Secretaries

    Patrick S. Johansen, Rune M. Jacobsen, Lukas B. L. Bysted, Mikael B. Skov, Eleftherios Papachristos

    Chapter from the book: Loizides, F et al. 2020. Human Computer Interaction and Emerging Technologies: Adjunct Proceedings from the INTERACT 2019 Workshops.

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    Advances in Machine Learning (ML) provide new opportunities for augmenting work practice. In this paper, we explored how an ML-based suggestion system can augment Danish medical secretaries in their daily tasks of handling patient referrals and allocating patients to a hospital ward. Through a user-centred design process, we studied the work context and processes of two medical secretaries. This generated a model of how a medical secretary would assess a visitation suggestion, and furthermore, it provided insights into how a system could fit into the medical secretaries’ daily tasks. We present our system design and discuss how our contribution may be of value to HCI practitioners designing for work augmentation in similar contexts.

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    Johansen, P et al. 2020. Designing a Machine Learning-based System to Augment the Work Processes of Medical Secretaries. In: Loizides, F et al (eds.), Human Computer Interaction and Emerging Technologies. Cardiff: Cardiff University Press. DOI: https://doi.org/10.18573/book3.y

    This is an Open Access chapter distributed under the terms of the Creative Commons Attribution 4.0 license (unless stated otherwise), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. Copyright is retained by the author(s).

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    Published on May 7, 2020