P151 The role of the nurse in a resuscitation team: Looking for the determinants of efficiency and quality efficiency

Kipourgos, Georgios; Albani, Eleni; Bakalis, Nikolaos; Tzenalis, Anastasios

doi:10.1016/s0300-9572(22)00561-5

Cited by 1 publication

(1 citation statement)

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“…While all the previous works are retrospective studies, one of the research goals is to develop systems to fully automatise triage, such as the i-TRIAGE system Kipourgos et al 42 It is a decision support system able to triage patients automatically. Similarly, in Joseph et al, 37 the authors presented an algorithm for a more granular triaging of patients.…”

Section: Prediction Of Triage Levelsmentioning

confidence: 99%

On the computational approaches for supporting triage systems

Defilippo,

Bertucci,

Zurzolo

et al. 2023

Interdisciplinary Medicine

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Triage procedure is used in Emergency Departments (ED) to manage the patient's treatment and prioritise care access. This is a largely resourceconsuming phase and relevant to reduce risk and optimise resource management. Moreover, the presence of patients in the ED (both in treatment rooms and in waiting rooms after triage) may increase the patients' time of stay, thus creating problems for critical patients and for healthcare process management. Moreover, it has been proved that a large fraction of ED incoming patients do not require emergency treatments and might be treated in ambulatory or by family doctors. In such cases, the triage wastes resources and time. In addition, the decision of a low priority or no ED necessity is relevant considering that underestimating treatment necessity may cause errors in patient treatments. Improving triage related decisions is a relevant task.It has been shown that computational methods such as machine learning (ML) may support triage by providing better stratification of patients as well as better results in terms of outcome. We here present a literature review discussing some recent approaches to predict the severity of patients and in particular, we present recent approaches based on ML. We use PRISMA methodology to include works in our analysis. Finally, the future directions of research and open problems are highlighted. K E Y W O R D Sliterature review, machine learning, triage systems | INTRODUCTIONPatients go to Emergency Departments (EDs) to access the clinical care of hospitals. In Italy, access to ED is universal and accessible to all the people staying in Italy, without any restriction, as stated in the Constitution of the Italian Republic. Managing queues in ED is crucial for guaranteeing the best care to all people, thus minimising clinical risk, efficiency and, lastly, the cost for the healthcare system. [1][2][3] This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Prediction Of Triage Levelsmentioning

confidence: 99%