OBJECTIVE: To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice. METHODS: Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed. RESULTS: (1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage. CONCLUSIONS The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.
Adult ; Humans ; Big Data ; Emergency Service, Hospital ; Triage/methods* ; Intensive Care Units ; Hospitalization ; Retrospective Studies

INTRODUCTION: The burden of frequent attenders (FAs) of emergency departments (EDs) on healthcare resources is underestimated when single-centre analyses do not account for utilisation of multiple EDs by FAs. We aimed to quantify the extent of multiple ED use by FAs and to characterise FAs. METHODS: We reviewed nationwide ED attendance in Singapore data from 1 January 2006 to 31 December 2018 (13 years). FAs were defined as patients with ≥4 ED visits in any calendar year. Single ED FAs and multiple ED FAs were patients who attended a single ED exclusively and ≥2 distinct EDs within the year, respectively. Mixed ED FAs were patients who attended a mix of a single ED and multiple EDs in different calendar years. We compared the characteristics of FAs using multivariable logistic regression. RESULTS: We identified 200,130 (6.3%) FAs who contributed to1,865,704 visits (19.6%) and 2,959,935 (93.7%) non-FAs who contributed to 7,671,097 visits (80.4%). After missing data were excluded, the study population consisted of 199,283 unique FAs. Nationwide-linked data identified an additional 15.5% FAs and 29.7% FA visits, in addition to data from single centres. Multiple ED FAs and mixed ED FAs were associated with male sex, younger age, Malay or Indian ethnicity, multiple comorbidities, median triage class of higher severity, and a higher frequency of ED use. CONCLUSION A nationwide approach is needed to quantify the national FA burden. The multiple comorbidities and higher frequency of ED use associated with FAs who visited multiple EDs and mixed EDs, compared to those who visited a single ED, suggested a higher level of ED burden in these subgroups of patients. The distinct characteristics and needs of each FA subgroup should be considered in future healthcare interventions to reduce FA burden.
Comorbidity ; Emergency Service, Hospital ; Ethnicity ; Humans ; Logistic Models ; Male ; Retrospective Studies ; Triage

Humans ; Child ; Triage ; Magnetic Resonance Imaging ; Head ; Brain/diagnostic imaging* ; Artificial Intelligence

PURPOSE: Overcrowding in emergency department (ED) is a concerning global problem and has been identified as a national crisis in some countries. Several emergency sorting systems designed successfully in the world. Launched in 2004, a group of branches in South African triage scale (SATS) developed. The effectiveness of the case sorting system of SATS was evaluated to reduce the patient's length of stay (LOS) and mortality rate within the ED at Suez Canal University Hospital. METHODS: The study was designed as an intervention study that included a systematic random sample of patients who presented to the ED in Suez Canal University Hospital. This study was implemented in three phases: pre-intervention phase, 115 patients were assessed by the traditional protocols; intervention phase, a structured training program was provided to the ED staff, including a workshop and lectures; and post-intervention phase, 230 patients were assessed by SATS. All the patients were retriaged 2 h later, calculating the LOS per patient and the mortality. Data was collected and entered using Microsoft Excel software. Collected data from the triage sheet were analyzed using the SPSS software program version 22.0. RESULTS: The LOS in the ED was about 183.78 min before the intervention; while after the training program and the application of SATS, it was reduced to 51.39 min. About 15.7% of the patients died before the intervention; however, after the intervention the ratio decreased to 10.7% deaths. CONCLUSION SATS is better at assessing patients without missing important data. Additionally, it resulted in a decrease in the LOS and reduction in the mortality rate compared to the traditional protocol.
Egypt ; Emergency Service, Hospital ; Humans ; Length of Stay ; South Africa ; Triage/methods*

Introduction: Trauma scoring standardizes the severity of injuries of patients brought to trauma centers and is predictive of the outcome or prognosis among trauma victims. Hence, creating a trauma score allows for proper prioritization as well as proper management of patients in the emergency departments. Objectives: The objective of the study is to come up with a trauma scoring system that correlates to the probability of survival of a patient using the patient databases in major hospitals in the Philippines representing the three major island groups, Luzon, Visayas, and Mindanao. The study will also compare this proposed trauma scoring system with the gold standard (Revised Trauma Score) developed by Champion in 1989. Methods: The proposed Philippine Trauma Scoring System (PTSS) was based on data from the eight largest tertiary hospitals catering to trauma patients. A total of 40,286 patient charts were reviewed. The proposed trauma scoring system integrates concepts used in the Revised Trauma Score (RTS), with addition of age (from Kampala Trauma Scoring), as well as the Injury Score (based on the number of body parts injured). This proposed scoring system was weighted, using logistic regression to come up with coefficients for the components of the PTSS for a more accurate prediction of patient survival. The Receiver Operating Characteristic (ROC) was used to plot Sensitivity vs. 1-Specificity. In this analysis, ROC was used to evaluate and compare how good the models are in predicting patient recovery. Results: The components of GCS, RR, SBP, age, and body parts injured were significant predictors of patient outcomes for patients with trauma, specifically the road crash patients in this Philippine study. This study showed that both the PTSS and RTS have a significantly greater area under the curve than the diagonal reference line, which means that both the scoring system have a significant predictive value. The best predictive value, however, comes from the proposed scoring system that is developed from this study in the Philippines. Compared to the gold standard, PTSS Model 1 is a better predictor of outcomes than the gold standard RTS (ROC-AUC = 0.659 vs. 0.633) using only 22,214 valid subject population that contained all the variables needed for the PTSS analysis. Conclusion In a developing country like the Philippines, there are limited resources especially in the healthcare setting. Therefore, it is important to lessen errors in triaging which may result in resource waste and a higher risk of adverse outcomes for the patients. Thus, the PTSS developed in this study can be used by Philippine hospitals as it is uniquely based on Filipino patients using a large database representative of the eight largest tertiary hospitals in the Philippines. The proposed PTSS is shown in this study as the best classifier for patient outcome compared to the gold standard – RTS of Champion.
Triage

Betacoronavirus ; Clinical Protocols ; Coronavirus Infections ; diagnosis ; pathology ; therapy ; transmission ; Humans ; Pandemics ; Patient Discharge ; Pneumonia, Viral ; diagnosis ; pathology ; therapy ; transmission ; Triage

Objectives: This study aimed to analyze if the indicator 72-hours Unplanned Return Visits after Emergency Department (ED) index discharge was influenced by the patient’s age, triage severity, month, payment methods, and length of stay. Likewise, it aimed to determine if the 72-hour Unplanned Return Visits was a robust indicator in assessing the quality of Emergency Department services. Methods: This was a retrospective single-center study from January to December 2017. Data were retrieved from a tertiary hospital in the Philippines. All Emergency Department patients discharged on their index visit were monitored for Unplanned Return Visits within 72 hours in the hospital. A univariate and multivariate logistic regression model was used to assess the variables associated with the 72-hour Unplanned Return Visits. Results: The 72-hour Unplanned Return Visits rate was measured at 2.67%, with the highest occurrence on the first 24 hours, and with predominance on third-party payer (p.<.0001), pediatrics (p.<0001), January (p<.0001), February (p<.0001), November (p<.0001), December (p<0001), and shorter length of stay (p<.0001) discharged after ED index visit. Conclusions Strong association of Unplanned Return Visits during the first 72 hours after Emergency Department index discharge was found for patients financed through third party-payers, with seasonal variations and inclination to the younger population with shorter length of stay. These findings warrant exploratory studies to determine the reasons for the 72-hour Unplanned Return Visits after Emergency Department index discharge and investigation on the association of premature discharge, socio-economic, health structure, and illness progression.
Triage ; Length of Stay ; Emergency Service, Hospital

COVID-19/prevention & control* ; Communicable Disease Control ; General Surgery/organization & administration* ; Guidelines as Topic ; Health Care Rationing ; Health Services Needs and Demand ; Health Workforce ; Humans ; Practice Guidelines as Topic ; SARS-CoV-2 ; Singapore/epidemiology* ; Surgical Procedures, Operative ; Triage

Ambulatory Care ; COVID-19 ; Health Facility Planning ; Hospital Bed Capacity ; Hospitals, Rehabilitation ; Humans ; Infection Control ; Rehabilitation ; Remote Consultation ; SARS-CoV-2 ; Singapore ; Telerehabilitation ; Triage

China ; Deep Learning ; Emergency Service, Hospital/statistics & numerical data* ; Forecasting/methods* ; Humans ; Triage ; Weather