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

doi:10.18573/book3.y

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

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

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Abstract

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.

Originalsprog	Engelsk
Titel	Human Computer Interaction and Emerging Technologies : Workshop Proceedings from the INTERACT 2019 Workshops
Forlag	Cardiff University Press
Publikationsdato	2020
Sider	191-196
ISBN (Elektronisk)	978-1-911653-09-7
DOI	https://doi.org/10.18573/book3.y
Status	Udgivet - 2020
Begivenhed	INTERACT 2019: The 17th IFIP TC.13 International Conference on Human-Computer Interaction - Paphos, Cypern Varighed: 2 sep. 2019 → 6 sep. 2019 Konferencens nummer: 17 http://interact2019.org

Konference

Konference	INTERACT 2019
Nummer	17
Land/Område	Cypern
By	Paphos
Periode	02/09/2019 → 06/09/2019
Internetadresse	http://interact2019.org

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10.18573/book3.yLicens: CC BY 4.0

human-computer-interaction-and-emerging-technologies-26-designing-a-machine-learning-based-system-to-augme.Forlagets udgivne version, 176 KBLicens: CC BY 4.0

https://koriinkpen.github.io/Interact2019_AI-HCI/Skov-et-al_InteractWorkshop_AugmentWorkProcesses_final.pdf

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Citationsformater

Johansen, P. S., Jacobsen, R. M., Bysted, L. B. L., Skov, M. B., & Papachristos, E. (2020). Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries. I Human Computer Interaction and Emerging Technologies: Workshop Proceedings from the INTERACT 2019 Workshops (s. 191-196). Cardiff University Press. https://doi.org/10.18573/book3.y

@inproceedings{5ae5b19d8cbc467298b6db985f0fbb96,

title = "Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries",

abstract = "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{\textquoteright} 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.",

keywords = "Work Augmentation, Human-AI Interaction, Medical Domain",

author = "Johansen, {Patrick Skov} and Jacobsen, {Rune M{\o}berg} and Bysted, {Lukas Bj{\o}rn Leer} and Skov, {Mikael B.} and Eleftherios Papachristos",

year = "2020",

doi = "10.18573/book3.y",

language = "English",

pages = "191--196",

booktitle = "Human Computer Interaction and Emerging Technologies",

publisher = "Cardiff University Press",

note = "INTERACT 2019 : The 17th IFIP TC.13 International Conference on Human-Computer Interaction, INTERACT 2019 ; Conference date: 02-09-2019 Through 06-09-2019",

url = "http://interact2019.org",

}

Johansen, PS, Jacobsen, RM, Bysted, LBL, Skov, MB & Papachristos, E 2020, Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries. i Human Computer Interaction and Emerging Technologies: Workshop Proceedings from the INTERACT 2019 Workshops. Cardiff University Press, s. 191-196, INTERACT 2019, Paphos, Cypern, 02/09/2019. https://doi.org/10.18573/book3.y

Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries. / Johansen, Patrick Skov; Jacobsen, Rune Møberg; Bysted, Lukas Bjørn Leer et al.
Human Computer Interaction and Emerging Technologies: Workshop Proceedings from the INTERACT 2019 Workshops. Cardiff University Press, 2020. s. 191-196.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

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AU - Papachristos, Eleftherios

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N2 - 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.

AB - 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.

KW - Work Augmentation

KW - Human-AI Interaction

KW - Medical Domain

U2 - 10.18573/book3.y

DO - 10.18573/book3.y

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PB - Cardiff University Press

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ER -

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

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