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

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › 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.

Original language	English
Title of host publication	Human Computer Interaction and Emerging Technologies : Workshop Proceedings from the INTERACT 2019 Workshops
Publisher	Cardiff University Press
Publication date	2020
Pages	191-196
ISBN (Electronic)	978-1-911653-09-7
DOIs	https://doi.org/10.18573/book3.y
Publication status	Published - 2020
Event	INTERACT 2019: The 17th IFIP TC.13 International Conference on Human-Computer Interaction - Paphos, Cyprus Duration: 2 Sept 2019 → 6 Sept 2019 Conference number: 17 http://interact2019.org

Conference

Conference	INTERACT 2019
Number	17
Country/Territory	Cyprus
City	Paphos
Period	02/09/2019 → 06/09/2019
Internet address	http://interact2019.org

Keywords

Work Augmentation
Human-AI Interaction
Medical Domain

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10.18573/book3.yLicence: CC BY 4.0

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

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

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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. In Human Computer Interaction and Emerging Technologies: Workshop Proceedings from the INTERACT 2019 Workshops (pp. 191-196). Cardiff University Press. https://doi.org/10.18573/book3.y

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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. in Human Computer Interaction and Emerging Technologies: Workshop Proceedings from the INTERACT 2019 Workshops. Cardiff University Press, pp. 191-196, INTERACT 2019, Paphos, Cyprus, 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. p. 191-196.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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AU - Johansen, Patrick Skov

AU - Jacobsen, Rune Møberg

AU - Bysted, Lukas Bjørn Leer

AU - Skov, Mikael B.

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

M3 - Article in proceeding

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EP - 196

BT - Human Computer Interaction and Emerging Technologies

PB - Cardiff University Press

T2 - INTERACT 2019

Y2 - 2 September 2019 through 6 September 2019

ER -

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

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