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

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Abstrakt

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.
OriginalsprogEngelsk
TitelHuman Computer Interaction and Emerging Technologies : Workshop Proceedings from the INTERACT 2019 Workshops
ForlagCardiff University Press
Publikationsdato2020
Sider191-196
ISBN (Elektronisk)978-1-911653-09-7
DOI
StatusUdgivet - 2020
BegivenhedINTERACT 2019: The 17th IFIP TC.13 International Conference on Human-Computer Interaction - Paphos, Cypern
Varighed: 2 sep. 20196 sep. 2019
Konferencens nummer: 17
http://interact2019.org

Konference

KonferenceINTERACT 2019
Nummer17
LandCypern
ByPaphos
Periode02/09/201906/09/2019
Internetadresse

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