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

Patrick Skov Johansen, Rune Møberg Jacobsen, Lukas Bjørn Leer Bysted, Mikael B. Skov, Eleftherios Papachristos

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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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 usercentred 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.
Original languageEnglish
Title of host publicationHuman Computer Interaction and Emerging Technologies : Workshop Proceedings from the INTERACT 2019 Workshops
PublisherCardiff University Press
Publication date2020
ISBN (Electronic)978-1-911653-09-7
Publication statusPublished - 2020
EventINTERACT 2019: The 17th IFIP TC.13 International Conference on Human-Computer Interaction - Paphos, Cyprus
Duration: 2 Sep 20196 Sep 2019
Conference number: 17


ConferenceINTERACT 2019
Internet address

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