International Big Data Centre in Emergency Medicine

Institute of Sciences in Emergency Medicine - International Big Data Centre in Emergency Medicine

This observational study aims to use electronic health records to build an International Big Data Centre in Emergency Medicine, within the Institute of Sciences in Emergency Medicine (ISEM) at the Guangdong Provincial People's Hospital. The main questions it seeks to answer are not limited to the following:

• Identify the relationship between Emergency Department Length of Stay (EDLOS), Mortality, and Adverse Events (AE)
• Identify the risk factors associated with high mortality and AE rate among patients who experience prolonged EDLOS
• Other research questions related to emergency medicine, such as building prediction and cluster models for acute diseases

The relationship between the emergency department length-of-stay (EDLOS), mortality, and adverse events (AE) rate remains unclear and underestimated. EDLOS is the time elapsed between the time of arrival and the time of the release of a patient admitted to ED. Mortality is defined as short-term (14 hours, 48 hours, 7 days, 15 days) and 30 days death of patients managed in the ED discharged or admitted. An AE is an event that can result whether from medical mismanagement or happen by accident and causes physical and mental harm to the patient during the stay and remains after leaving. To separate the mismanagement from accidental causes i.e., compliant management but the unfortunate situation or persons, this study will refer to indicators that are objectively measurable and can be compared at different times throughout the patient's stay in the ED.

The hypothesis is that a prolonged stay of the patients in the ED especially without diagnosis or decision of admission is associated with increased mortality and AEs. This project will test this hypothesis through a prospective international multicenter study in contributing to whether a prolonged EDLOS is associated with an increased rate of adverse outcomes.

Inclusion Criteria:

• Every patient who ever visited Emergency Department from 2011-2022

Exclusion Criteria:

• Patients with missing demographic (age, gender, etc.) and triage data