Overcrowding analysis in emergency department through indexes: a single center study

Abstract

Introduction Overcrowding in the Emergency Department (ED) is one of the major issues that must be addressed in order to improve the services provided in emergency circumstances and to optimize their quality. As a result, in order to help the patients and professionals engaged, hospital organizations must implement remedial and preventative measures. Overcrowding has a number of consequences, including inadequate treatment and longer hospital stays; as a result, mortality and the average duration of stay in critical care units both rise. In the literature, a number of indicators have been used to measure ED congestion. EDWIN, NEDOCS and READI scales are considered the most efficient ones, each of which is based on different parameters regarding the patient management in the ED. Methods In this work, EDWIN Index and NEDOCS Index have been calculated every hour for a month period from February 9th to March 9th, 2020 and for a month period from March 10th to April 9th, 2020. The choice of the period is related to the date of the establishment of the lockdown in Italy due to the spread of Coronavirus; in fact on 9 March 2020 the Italian government issued the first decree regarding the urgent provisions in relation to the COVID-19 emergency. Besides, the Pearson correlation coefficient has been used to evaluate how much the EDWIN and NEDOCS indexes are linearly dependent. Results EDWIN index follows a trend consistent with the situation of the first lockdown period in Italy, defined by extreme limitations imposed by Covid-19 pandemic. The 8:00–20:00 time frame was the most congested, with peak values between 8:00 and 12:00. on the contrary, in NEDOCS index doesn’t show a trend similar to the EDWIN one, resulting less reliable. The Pearson correlation coefficient between the two scales is 0,317. Conclusion In this study, the EDWIN Index and the NEDOCS Index were compared and correlated in order to assess their efficacy, applying them to the case study of the Emergency Department of “San Giovanni di Dio e Ruggi d’Aragona” University Hospital during the Covid-19 pandemic. The EDWIN scale turned out to be the most realistic model in relation to the actual crowding of the ED subject of our study. Besides, the two scales didn’t show a significant correlation value.

Health Status of Nonemergency Patients in the Emergency Department Using the EQ-5D

“…It is related to the hospital's capacity, staf, patient discharge, community resources, and other factors. Te number of ED visits has been increasing year by year, and the patient's medical needs can exceed the emergency capacity, causing ED crowding to be a phenomenon of widespread concern globally [2][3][4]. A study has reported that 32.7% of ED visits were for nonemergency health issues [5].…”

Prediction of Hospital Length Stay for Patients Undergoing Mastectomy

“…Researchers and practitioners are focused on developing models and methodologies for estimating LOS [5,6], and models have been proposed to decrease LOS for other surgeries [7][8][9][10][11][12]. The implementations presented actually adopt all those data analysis strategies already successfully used in the health sector for the analysis of biomedical data [13][14][15] but also from a management point of view for the analysis of hospital resources [16][17][18] and pathways [19,20]. The main aim of the proposed work is to design and develop the most suitable ML model able to estimate the LOS for patients undergoing mastectomy in order to optimize the healthcare management process.…”