Division of labor between humans and algorithms in healthcare: the case of surgery duration predictions

  • For many healthcare applications a collaboration of humans and algorithms has been shown to be superior to pure automation in terms of performance. However, the healthcare sector is characterized by shortages in personnel, which can lead to an excessive workload for the employees and thus makes automation highly beneficial to reduce human workload. In our paper, we consider a combination of different work modes and evaluate whether humans have to be involved in every instance of a task or whether they can be replaced by an AI for some instances. We analyze the potential of segmenting tasks based on who is involved in their completion: Either an AI or a human complete the task individually, or they complete the task together. Considering the case of surgery duration predictions and using a dataset from a university hospital, we observe that human effort could be decreased while maintaining a high prediction performance.

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Metadaten
Author:Dominik David Walzner, Laura Maria Poreschack, Andreas Fuegener, Sebastian SchiffelsORCiDGND, Christof Denz
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/118779
URL:https://aisel.aisnet.org/icis2023/ishealthcare/ishealthcare/9/
ISBN:978-1-958200-07-0OPAC
Parent Title (English):Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies - ICIS 2023 Proceedings: International Conference on Information Systems, December 10-13, 2023, Hyderabad, India
Publisher:AIS Electronic Library (AISeL)
Place of publication:New York, NY
Editor:Souren Paul, Suprateek Sarker, Virpi Kristiina Tuunainen
Type:Conference Proceeding
Language:English
Date of Publication (online):2025/01/31
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2025/02/01
First Page:9
Institutes:Wirtschaftswissenschaftliche Fakultät
Wirtschaftswissenschaftliche Fakultät / Institut für Betriebswirtschaftslehre
Wirtschaftswissenschaftliche Fakultät / Institut für Betriebswirtschaftslehre / Professur für Digital Health & Medical Decision Making
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit

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