An Empirical Analysis of Predictors for Workload Estimation in Healthcare

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

doi:10.1007/978-3-030-50371-0_22

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 proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Computational Science - ICCS 2020
Subtitle of host publication	20th International Conference Amsterdam, The Netherlands, June 3-5, 2020,
Editors	Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, Joãs Teixeira
Publisher	Springer
Pages	304-311
Number of pages	8
Volume	LNCS 12137
Edition	Part 1
ISBN (Electronic)	9783030503710
ISBN (Print)	9783030503703
DOIs	https://doi.org/10.1007/978-3-030-50371-0_22
Publication status	Published - 19 Jun 2020
Event	20th International Conference on Computational Science - Cancelled due to COVID-19 was due to take place in Amsterdam Duration: 3 Jun 2020 → 5 Jun 2020 Conference number: 20 https://www.iccs-meeting.org/iccs2020/

Publication series

Name	Theoretical Computer Science and General Issues
Publisher	Springer International Publishing
Number	Part 1
Volume	LNCS 12137
ISSN (Print)	0302-9743
ISSN (Electronic)	1161-3349

Conference

Conference	20th International Conference on Computational Science
Abbreviated title	ICCS 2020
Period	3/06/20 → 5/06/20
Internet address	https://www.iccs-meeting.org/iccs2020/

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10.1007/978-3-030-50371-0_22Licence: Unspecified

AAMAccepted author manuscript, 237 KBLicence: Unspecified

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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

Gatta, Roberto ; Vallati, Mauro ; Pirola, Ilenia et al. / An Empirical Analysis of Predictors for Workload Estimation in Healthcare. Computational Science - ICCS 2020: 20th International Conference Amsterdam, The Netherlands, June 3-5, 2020, . editor / Valeria V. Krzhizhanovskaya ; Gábor Závodszky ; Michael H. Lees ; Jack J. Dongarra ; Peter M. A. Sloot ; Sérgio Brissos ; Joãs Teixeira. Vol. LNCS 12137 Part 1. ed. Springer, 2020. pp. 304-311 (Theoretical Computer Science and General Issues; Part 1).

@inproceedings{7a2521159f374cbca21fd37f8ff70158,

title = "An Empirical Analysis of Predictors for Workload Estimation in Healthcare",

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{\textquoteright}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.",

keywords = "Workload Estimation, Machine Learning, Predictors",

author = "Roberto Gatta and Mauro Vallati and Ilenia Pirola and Jacopo Lenkowicz and Luca Tagliaferri and Carlo Cappelli and Maurizio Castellano",

year = "2020",

month = jun,

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number = "Part 1",

pages = "304--311",

editor = "Krzhizhanovskaya, {Valeria V.} and G{\'a}bor Z{\'a}vodszky and Lees, {Michael H.} and Dongarra, {Jack J.} and Sloot, {Peter M. A.} and S{\'e}rgio Brissos and Jo{\~a}s Teixeira",

booktitle = "Computational Science - ICCS 2020",

address = "Germany",

edition = "Part 1",

note = "20th International Conference on Computational Science, ICCS 2020 ; Conference date: 03-06-2020 Through 05-06-2020",

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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 VV Krzhizhanovskaya, G Závodszky, MH Lees, JJ Dongarra, PMA Sloot, S Brissos & J Teixeira (eds), Computational Science - ICCS 2020: 20th International Conference Amsterdam, The Netherlands, June 3-5, 2020, . Part 1 edn, vol. LNCS 12137, Theoretical Computer Science and General Issues, no. Part 1, vol. LNCS 12137, Springer, pp. 304-311, 20th International Conference on Computational Science, 3/06/20. https://doi.org/10.1007/978-3-030-50371-0_22

An Empirical Analysis of Predictors for Workload Estimation in Healthcare. / Gatta, Roberto; Vallati, Mauro; Pirola, Ilenia et al.

Computational Science - ICCS 2020: 20th International Conference Amsterdam, The Netherlands, June 3-5, 2020, . ed. / Valeria V. Krzhizhanovskaya; Gábor Závodszky; Michael H. Lees; Jack J. Dongarra; Peter M. A. Sloot; Sérgio Brissos; Joãs Teixeira. Vol. LNCS 12137 Part 1. ed. Springer, 2020. p. 304-311 (Theoretical Computer Science and General Issues; Vol. LNCS 12137, No. Part 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Empirical Analysis of Predictors for Workload Estimation in Healthcare

AU - Gatta, Roberto

AU - Vallati, Mauro

AU - Pirola, Ilenia

AU - Lenkowicz, Jacopo

AU - Tagliaferri, Luca

AU - Cappelli, Carlo

AU - Castellano, Maurizio

N1 - Conference code: 20

PY - 2020/6/19

Y1 - 2020/6/19

N2 - 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.

AB - 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.

KW - Workload Estimation

KW - Machine Learning

KW - Predictors

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SN - 9783030503703

VL - LNCS 12137

T3 - Theoretical Computer Science and General Issues

SP - 304

EP - 311

BT - Computational Science - ICCS 2020

A2 - Krzhizhanovskaya, Valeria V.

A2 - Závodszky, Gábor

A2 - Lees, Michael H.

A2 - Dongarra, Jack J.

A2 - Sloot, Peter M. A.

A2 - Brissos, Sérgio

A2 - Teixeira, Joãs

PB - Springer

T2 - 20th International Conference on Computational Science

Y2 - 3 June 2020 through 5 June 2020

ER -

Gatta R, Vallati M, Pirola I, Lenkowicz J, Tagliaferri L, Cappelli C et al. An Empirical Analysis of Predictors for Workload Estimation in Healthcare. In Krzhizhanovskaya VV, Závodszky G, Lees MH, Dongarra JJ, Sloot PMA, Brissos S, Teixeira J, editors, Computational Science - ICCS 2020: 20th International Conference Amsterdam, The Netherlands, June 3-5, 2020, . Part 1 ed. Vol. LNCS 12137. Springer. 2020. p. 304-311. (Theoretical Computer Science and General Issues; Part 1). Epub 2020 Jun 15. doi: 10.1007/978-3-030-50371-0_22

An Empirical Analysis of Predictors for Workload Estimation in Healthcare

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