The emergency department (ED) is the access point for urgent cases within the health facility. In recent years, however, several factors have led to a massive use of ED as a privileged access method to assistance even for patients who do not need timely treatment. This flow of so-called "non-urgent"cases generates pressure on the ED resources, leading to a considerable increase in waiting times which, in turn, generates an increase in patients who leave the ED before being visited from medical doctors. In this work, we investigate some of the factors that may lead to the decision to leave the ED before the first visit. Data were collected at the hospital A.O.R.N. "A. Cardarelli"of Naples (Italy) and then analyzed through traditional statistical tools and more advanced machine learning algorithms.

Analysis of voluntary departures from the emergency department of the hospital AORN "a. Cardarelli" / Ponsiglione, A. M.; Majolo, M.; Longo, G.; Russo, G.; Triassi, M.; Raiola, E.; Improta, G.. - (2021), pp. 1-4. (Intervento presentato al convegno 2021 International Symposium on Biomedical Engineering and Computational Biology, BECB 2021 tenutosi a chn nel 2021) [10.1145/3502060.3503630].

Analysis of voluntary departures from the emergency department of the hospital AORN "a. Cardarelli"

Ponsiglione A. M.;Triassi M.;Raiola E.;Improta G.
2021

Abstract

The emergency department (ED) is the access point for urgent cases within the health facility. In recent years, however, several factors have led to a massive use of ED as a privileged access method to assistance even for patients who do not need timely treatment. This flow of so-called "non-urgent"cases generates pressure on the ED resources, leading to a considerable increase in waiting times which, in turn, generates an increase in patients who leave the ED before being visited from medical doctors. In this work, we investigate some of the factors that may lead to the decision to leave the ED before the first visit. Data were collected at the hospital A.O.R.N. "A. Cardarelli"of Naples (Italy) and then analyzed through traditional statistical tools and more advanced machine learning algorithms.
2021
9781450384117
Analysis of voluntary departures from the emergency department of the hospital AORN "a. Cardarelli" / Ponsiglione, A. M.; Majolo, M.; Longo, G.; Russo, G.; Triassi, M.; Raiola, E.; Improta, G.. - (2021), pp. 1-4. (Intervento presentato al convegno 2021 International Symposium on Biomedical Engineering and Computational Biology, BECB 2021 tenutosi a chn nel 2021) [10.1145/3502060.3503630].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/879484
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