Abstract
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 language | English |
|---|---|
| Title of host publication | Adjunct Proceedings of the 17th IFIP Conference on Human-Computer Interaction (Interact’19) |
| Publisher | Springer |
| Publication date | 2019 |
| Publication status | Published - 2019 |
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Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries. / Johansen, Patrick Skov; Jacobsen, Rune Møberg; Bysted, Lukas Bjørn Leer; Skov, Mikael B.; Papachristos, Eleftherios.
Adjunct Proceedings of the 17th IFIP Conference on Human-Computer Interaction (Interact’19). Springer, 2019.Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review
TY - GEN
T1 - Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries
AU - Johansen, Patrick Skov
AU - Jacobsen, Rune Møberg
AU - Bysted, Lukas Bjørn Leer
AU - Skov, Mikael B.
AU - Papachristos, Eleftherios
PY - 2019
Y1 - 2019
N2 - Advances in Machine Learning (ML) provide new opportunities foraugmenting 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 medicalsecretaries. This generated a model of how a medical secretary would assess avisitation suggestion, and furthermore, it provided insights into how a systemcould fit into the medical secretaries’ daily tasks. We present our system designand discuss how our contribution may be of value to HCI practitioners designingfor work augmentation in similar contexts.
AB - Advances in Machine Learning (ML) provide new opportunities foraugmenting 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 medicalsecretaries. This generated a model of how a medical secretary would assess avisitation suggestion, and furthermore, it provided insights into how a systemcould fit into the medical secretaries’ daily tasks. We present our system designand discuss how our contribution may be of value to HCI practitioners designingfor work augmentation in similar contexts.
M3 - Article in proceeding
BT - Adjunct Proceedings of the 17th IFIP Conference on Human-Computer Interaction (Interact’19)
PB - Springer
ER -