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

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

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 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 publicationAdjunct Proceedings of the 17th IFIP Conference on Human-Computer Interaction (Interact’19)
PublisherSpringer
Publication date2019
Publication statusPublished - 2019

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Learning systems
Human computer interaction

Cite this

Johansen, P. S., Jacobsen, R. M., Bysted, L. B. L., Skov, M. B., & Papachristos, E. (2019). Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries. In Adjunct Proceedings of the 17th IFIP Conference on Human-Computer Interaction (Interact’19) Springer.
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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 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.",
author = "Johansen, {Patrick Skov} and Jacobsen, {Rune M{\o}berg} and Bysted, {Lukas Bj{\o}rn Leer} and Skov, {Mikael B.} and Eleftherios Papachristos",
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booktitle = "Adjunct Proceedings of the 17th IFIP Conference on Human-Computer Interaction (Interact’19)",
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Johansen, PS, Jacobsen, RM, Bysted, LBL, Skov, MB & Papachristos, E 2019, Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries. in Adjunct Proceedings of the 17th IFIP Conference on Human-Computer Interaction (Interact’19). Springer.

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 proceedingArticle in proceedingResearchpeer-review

TY - GEN

T1 - Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries

AU - Johansen, Patrick Skov

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AU - Bysted, Lukas Bjørn Leer

AU - Skov, Mikael B.

AU - Papachristos, Eleftherios

PY - 2019

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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

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Johansen PS, Jacobsen RM, Bysted LBL, Skov MB, Papachristos E. Designing a Machine Learning-Based System to Augment the Work Processes of Medical Secretaries. In Adjunct Proceedings of the 17th IFIP Conference on Human-Computer Interaction (Interact’19). Springer. 2019