An Empirical Analysis of Predictors for Workload Estimation in Healthcare

Roberto Gatta, Mauro Vallati, Ilenia Pirola, Jacopo Lenkowicz, Luca Tagliaferri, Carlo Cappelli, Maurizio Castellano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The limited availability of resources makes the resource allocation strategy a pivotal aspect for every clinical department. Allocation is usually done on the basis of a workload estimation, which is performed by human experts. Experts have to dedicate a significant amount of time to the workload estimation, and the usefulness of estimations depends on the expert’s ability to understand very different conditions and situations. Machine learning-based predictors can help in reduce the burden on human experts, and can provide some guarantees at least in terms of repeatability of the delivered performance. However, it is unclear how good their estimations would be, compared to those of experts. In this paper we address this question by exploiting 6 algorithms for estimating the workload of future activities of a real-world department. Results suggest that this is a promising avenue for future investigations aimed to optimising the use of resources of clinical departments.
Original languageEnglish
Title of host publicationComputational Science - ICCS 2020
Subtitle of host publication20th International Conference Amsterdam, The Netherlands, June 3-5, 2020,
EditorsValeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, Joãs Teixeira
PublisherSpringer
Pages304-311
Number of pages8
VolumeLNCS 12137
EditionPart 1
ISBN (Electronic)9783030503710
ISBN (Print)9783030503703
DOIs
Publication statusPublished - 19 Jun 2020
Event20th International Conference on Computational Science - Cancelled due to COVID-19 was due to take place in Amsterdam
Duration: 3 Jun 20205 Jun 2020
Conference number: 20
https://www.iccs-meeting.org/iccs2020/

Publication series

NameTheoretical Computer Science and General Issues
PublisherSpringer International Publishing
NumberPart 1
VolumeLNCS 12137
ISSN (Print)0302-9743
ISSN (Electronic)1161-3349

Conference

Conference20th International Conference on Computational Science
Abbreviated titleICCS 2020
Period3/06/205/06/20
Internet address

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  • Cite this

    Gatta, R., Vallati, M., Pirola, I., Lenkowicz, J., Tagliaferri, L., Cappelli, C., & Castellano, M. (2020). An Empirical Analysis of Predictors for Workload Estimation in Healthcare. In V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos, & J. Teixeira (Eds.), Computational Science - ICCS 2020: 20th International Conference Amsterdam, The Netherlands, June 3-5, 2020, (Part 1 ed., Vol. LNCS 12137, pp. 304-311). (Theoretical Computer Science and General Issues; Vol. LNCS 12137, No. Part 1). Springer. https://doi.org/10.1007/978-3-030-50371-0_22