Background: A prolonged length of stay in an emergency department is related to lower quality of care and adverse outcomes, which are often linked with overcrowding.
Objective: Examine the influence of demographic factors on prolonged length of stay in the emergency department.
Methods: This study used a cross-sectional design. It used secondary data for all patients admitted during the specific duration at the emergency department of a governmental hospital in Saudi Arabia. The independent variables were gender, age, disposition status, shift time, and clinical acuity (CTAS) level while the dependent variable was prolonged length of stay.
Results: The study shows that 30% of patients stay at the emergency department for four hours or more. The results also show a significant association between demographic factors which are age, gender, disposition status, shift time, clinical acuity (CTAS) level and prolonged length of stay in an emergency department. Based on the results males are more likely to stay in the emergency department than females (OR = 1.20; 95% CI = 1.04 to 1.38). Patients aged 60 and older are less likely to stay in the emergency department than patients aged 29 or smaller (OR = 0.58; 95% CI = 0.39 to 0.84). According to disposition status discharged patients after examination stays in the emergency department more than admitted patients after the examination (OR = 2.78; 95% CI = 1.67 to 4.99). Patients who come to the night shift are less likely to stay in the emergency department than patients who come in the morning shift (OR = 0.67; 95% CI = 0.56 to 0.81). Patients who are classified in level three of CTAS are less likely to stay in the emergency department than patients who are classified in level one (OR = 0.28; 95% CI = 0.88 to 0.023).
Conclusion: Demographic factors such as age, gender, shift time, disposition status and clinical acuity (CTAS) were important factors that needed to be considered to reduce the length of stay of patients in the emergency department. it is possible to formulate a machine learning model to predict the anticipated length of stay in the hospital for each patient. This prediction with an accepted margin of uncertainty will help the clinicians to communicate the evidence-based anticipated length of stay with the patient's caregivers. In addition, hospital managers need to provide the emergency department with enough staff and materials to reduce the length of stay of patients.
Copyright: © 2024 Alnahari, A’aqoulah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.