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  • Title: Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.
    Author: Marino MR, Trunfio TA, Ponsiglione AM, Amato F, Improta G.
    Journal: Sci Rep; 2024 Aug 22; 14(1):19513. PubMed ID: 39174595.
    Abstract:
    A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, without any evaluation by a healthcare provider (Leave Without Being Seen, LWBS). This has an impact on the hospital in terms of missing income from lost opportunities to offer treatment and, in general, of negative outcomes from the ED process. Consequently, healthcare managers must be able to forecast and control patients who leave the ED without being evaluated in advance. This study is a retrospective analysis of patients registered at the ED of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno (Italy) during the years 2014-2021. The goal was firstly to analyze factors that lead to patients abandoning the ED without being examined, taking into account the features related to patient characteristics such as age, gender, arrival mode, triage color, day of week of arrival, time of arrival, waiting time for take-over and year. These factors were used as process measures to perform a correlation analysis with the LWBS status. Then, Machine Learning (ML) techniques are exploited to develop and compare several LWBS prediction algorithms, with the purpose of providing a useful support model for the administration and management of EDs in the healthcare institutions. During the examined period, 688,870 patients were registered and 39188 (5.68%) left without being seen. Of the total LWBS patients, 59.6% were male and 40.4% were female. Moreover, from the statistical analysis emerged that the parameter that most influence the abandonment rate is the waiting time for take-over. The final ML classification model achieved an Area Under the Curve (AUC) of 0.97, indicating high performance in estimating LWBS for the years considered in this study. Various patient and ED process characteristics are related to patients who LWBS. The possibility of predicting LWBS rates in advance could be a valid tool quickly identifying and addressing "bottlenecks" in the hospital organization, thereby improving efficiency.
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