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  1. Pubblicazioni

Evaluating the Dispatching Policies for a Regional Network of Emergency Departments Exploiting Health Care Big Data

Contributo in Atti di convegno
Data di Pubblicazione:
2018
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.
Tipologia CRIS:
04A-Conference paper in volume
Elenco autori:
Aringhieri, Roberto; Dell’Anna, Davide; Duma, Davide; Sonnessa, Michele
Autori di Ateneo:
ARINGHIERI Roberto
Link alla scheda completa:
https://iris.unito.it/handle/2318/1655211
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/1655211/379034/2018-LCNS-MOD-postPrint.pdf
Titolo del libro:
MOD 2017: Machine Learning, Optimization, and Big Data
Pubblicato in:
LECTURE NOTES IN COMPUTER SCIENCE
Journal
LECTURE NOTES IN COMPUTER SCIENCE
Series
  • Dati Generali

Dati Generali

URL

https://link.springer.com/chapter/10.1007%2F978-3-319-72926-8_46
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