Evaluating the dispatching policies for a regional network of emergency departments exploiting health care big data

Roberto Aringhieri; Davide Dell’Anna; Davide Duma; Michele Sonnessa

doi:10.1007/978-3-319-72926-8_46

Evaluating the dispatching policies for a regional network of emergency departments exploiting health care big data

Roberto Aringhieri^*, Davide Dell’Anna, Davide Duma, Michele Sonnessa

^*Corresponding author for this work

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

Abstract

The Emergency Department (ED) is responsible to provide medical and surgical care to patients arriving at the hospital in need of immediate care. At the regional level, the EDs system can be seen as a network of EDs cooperating to maximise the outputs (number of patients served, average waiting time,..) and outcomes in terms of the provided care quality. In this paper we discuss how quantitative analysis based on health care big data can provide a tool to evaluate the dispatching policies for the network of emergency departments operating in Piedmont, Italy: the basic idea is to exploit clusters of EDs in such a way to fairly distribute the workload. Further, we discuss how big data can enable a novel methodological approach to the health system analysis.

Original language	English
Title of host publication	Machine Learning, Optimization, and Big Data - Third International Conference, MOD 2017, Revised Selected Papers
Publisher	Springer
Pages	549-561
Number of pages	13
Volume	10710 LNCS
ISBN (Print)	9783319729251
DOIs	https://doi.org/10.1007/978-3-319-72926-8_46
Publication status	Published - 2018
Event	3rd International Conference on Machine Learning, Optimization, and Big Data, MOD 2017 - Volterra, Italy Duration: 14 Sept 2017 → 17 Sept 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10710 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	3rd International Conference on Machine Learning, Optimization, and Big Data, MOD 2017
Country/Territory	Italy
City	Volterra
Period	14/09/17 → 17/09/17

Keywords

Big data
Emergency care pathway
Health systems

Access to Document

10.1007/978-3-319-72926-8_46

aringhieriFinal published version, 356 KBLicence: Taverne

Cite this

Aringhieri, R., Dell’Anna, D., Duma, D., & Sonnessa, M. (2018). Evaluating the dispatching policies for a regional network of emergency departments exploiting health care big data. In Machine Learning, Optimization, and Big Data - Third International Conference, MOD 2017, Revised Selected Papers (Vol. 10710 LNCS, pp. 549-561). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10710 LNCS). Springer. https://doi.org/10.1007/978-3-319-72926-8_46

Aringhieri, Roberto ; Dell’Anna, Davide ; Duma, Davide et al. / Evaluating the dispatching policies for a regional network of emergency departments exploiting health care big data. Machine Learning, Optimization, and Big Data - Third International Conference, MOD 2017, Revised Selected Papers. Vol. 10710 LNCS Springer, 2018. pp. 549-561 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The Emergency Department (ED) is responsible to provide medical and surgical care to patients arriving at the hospital in need of immediate care. At the regional level, the EDs system can be seen as a network of EDs cooperating to maximise the outputs (number of patients served, average waiting time,..) and outcomes in terms of the provided care quality. In this paper we discuss how quantitative analysis based on health care big data can provide a tool to evaluate the dispatching policies for the network of emergency departments operating in Piedmont, Italy: the basic idea is to exploit clusters of EDs in such a way to fairly distribute the workload. Further, we discuss how big data can enable a novel methodological approach to the health system analysis.",

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Aringhieri, R, Dell’Anna, D, Duma, D & Sonnessa, M 2018, Evaluating the dispatching policies for a regional network of emergency departments exploiting health care big data. in Machine Learning, Optimization, and Big Data - Third International Conference, MOD 2017, Revised Selected Papers. vol. 10710 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10710 LNCS, Springer, pp. 549-561, 3rd International Conference on Machine Learning, Optimization, and Big Data, MOD 2017, Volterra, Italy, 14/09/17. https://doi.org/10.1007/978-3-319-72926-8_46

Evaluating the dispatching policies for a regional network of emergency departments exploiting health care big data. / Aringhieri, Roberto; Dell’Anna, Davide; Duma, Davide et al.
Machine Learning, Optimization, and Big Data - Third International Conference, MOD 2017, Revised Selected Papers. Vol. 10710 LNCS Springer, 2018. p. 549-561 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10710 LNCS).

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

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AB - The Emergency Department (ED) is responsible to provide medical and surgical care to patients arriving at the hospital in need of immediate care. At the regional level, the EDs system can be seen as a network of EDs cooperating to maximise the outputs (number of patients served, average waiting time,..) and outcomes in terms of the provided care quality. In this paper we discuss how quantitative analysis based on health care big data can provide a tool to evaluate the dispatching policies for the network of emergency departments operating in Piedmont, Italy: the basic idea is to exploit clusters of EDs in such a way to fairly distribute the workload. Further, we discuss how big data can enable a novel methodological approach to the health system analysis.

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Aringhieri R, Dell’Anna D, Duma D, Sonnessa M. Evaluating the dispatching policies for a regional network of emergency departments exploiting health care big data. In Machine Learning, Optimization, and Big Data - Third International Conference, MOD 2017, Revised Selected Papers. Vol. 10710 LNCS. Springer. 2018. p. 549-561. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-72926-8_46

Evaluating the dispatching policies for a regional network of emergency departments exploiting health care big data

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Keywords

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