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The Ability of Emergency Medical Service Staff to Predict Emergency Department Disposition: A Prospective Study
Abdulrhman Alghamdi1,2, Abdullah Alshibani1,2, Meshary Binhotan1,2
1Emergency Medical Services Department, College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Purpose:
Paramedics' decision to notify receiving hospitals and transport patients to an appropriate healthcare facility is based on the Prediction of Intensive Care Unit (ICU) and Hospital Admissions guide. This study aimed to assess the paramedics' gestalt on both ward and ICU admission.
Patients And Methods:
A prospective study was conducted at King Abdulaziz Medical City between September 2021 and March 2022. Paramedics were asked several questions related to the prediction of the patient's hospital outcome, including emergency department (ED) discharge or hospital admission (ICU or ward). Additional data, such as the time of the ambulance's arrival and the staff years of experience, were collected. The categorical characteristics are presented by frequency and percentage for each category.
Results:
This study included 251 paramedics and 251 patients. The average age of the patients was 62 years. Of the patients, 32 (12.7%) were trauma, and 219 (87.3%) were non-trauma patients. Two-thirds of the patients (n=171, 68.1%) were predicted to be admitted to the hospital, and 80 (31.8%) of the EMS staff indicated that the patient do not need a hospital or an ambulance. The sensitivity, specificity, PPV, and NPV of the emergency medical service (EMS) staffs' gestalt for patient admission to the hospital were, respectively (77%), (33%), (16%), and (90%). Further analysis was reported to defend the EMS staffs' gestalt based on the level of EMS staff and the nature of the emergency (medical vs trauma), are reported.
Conclusion:
Our study reports a low level of accurately predicting patient admission to the hospital, including the ICU. The results of this study have important implications for enhancing the accuracy of EMS staff predictive ability and ensuring that patients receive appropriate care promptly.
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