INTRODUCTION: Cancer patients attending emergency departments (EDs) often present with acute symptoms and are frequently admitted. This study aimed to characterise the profile of oncology patients who were discharged from the ED. METHODS: This was a retrospective audit of patients with cancer-related diagnoses who presented to the ED at the Singapore General Hospital (SGH) over a 6-month period from 1 October 2018 to 31 March 2019 and were directly discharged from the ED. Data was extracted from the hospital's electronic medical record system. RESULTS: Of the 492 participants included in the study, the majority were triaged as Priority 2 (61.4%), while 30.7% were triaged as Priority 3, 6.9% as Priority 1 and 1.0% as Priority 4. There was no statistical difference between the National Early Warning scores across the different triage categories in these patients. The most common complaint was (44.3%), followed by genitourinary symptoms (19.5%) and those related to devices, catheters or stomas (17.3%). More investigations of all types were done for patients being managed in Priority 1 (57.6%) than in the other triage categories (40.1% for Priority 2, 23.2% for Priority 3 and 12.0% for Priority 4). Treatment procedures carried out were mainly symptomatic (analgesics, antiemetics, proton pump inhibitors) for 79.8% of the patients. There were no significant differences in the proportion of patients requiring various treatment modalities among the triage categories. CONCLUSION Selected oncological patients may potentially be managed in an ambulatory setting.
Humans ; Emergency Service, Hospital/statistics & numerical data* ; Retrospective Studies ; Female ; Neoplasms/diagnosis* ; Male ; Singapore ; Patient Discharge/statistics & numerical data* ; Middle Aged ; Aged ; Triage ; Adult ; Emergencies ; Aged, 80 and over

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

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: 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

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

Readiness of Health Care Staff: Statement 1. Family physicians and their staff should prepare themselves mentally, physically and emotionally before resuming clinic services. Prior to starting every clinic day, physicians and their staff should take their temperature and note respiratory symptoms. Statement 2. All clinical staff should be properly trained on proper use of PPEs, clinic disinfection, infection control and other safety procedures. Statement 3. Family Physicians should design an office management and operations plan that includes triage, patient flow, treatment and other patient care protocols including strict implementation of infection prevention and control procedures, management of PPE supplies and potential staff shortages. Statement 4. The clinic staff must inform their patients of the changes that may result from the new management and operations plan that will be made in the facility Clinic Procedures, Disinfection and Infection Control: Statement 5. After undergoing proper triage, non-COVID 19 patients entering the clinic should use a hand sanitizer, step on a foot bath or pad soaked in chlorine or any approved disinfectant solution at the entrance. All clinic staff, patients and accompanying persons should be wearing at least a mask inside the clinic. They should be instructed to avoid touching their face or mask and perform hand hygiene immediately before and after if cannot be avoided. Statement 6. Appropriate visual alerts or educational posters regarding infection control, proper handwashing, cough or sneezing etiquette should be visible inside the clinic. Statement 7. The clinic facility must have infection prevention and control measures that adhere to international and local standards. Statement 8. After appropriate triaging, a family physician when attending to a patient shall wear mask, single use gloves and eye protection while apron or gown is optional. It is up to the discretion of the family physician to use higher level of protection based on his risk assessment of the clinic environment and if resources are available. Clinical Services Statement 9. As much as possible, family physicians should continue all primary care services in the clinics. However, it is advisable to first limit the service to non-COVID-19 (suspect or diagnosed) patients. Patients needing COVID-19 assessment and management should be referred to appropriate facilities and follow the guidelines set forth by the Department of Health. Statement 10. A patient who consulted and whose symptoms were resolved may choose not come back for follow-up. Patients with chronic diseases may be followed-up at longer intervals if their illness is stable. Statement 11. Referrals for further assessment, diagnostic tests, or other procedures not available in the clinic must first be coordinated with the referral center/site
Personal Protective Equipment ; Triage ; Disinfection

Background: In a low resource setting, strategies to optimize Personal Protective Equipment (PPE) supplies are being observed. Alternative protective measures were identified to protect health care personnel during delivery of care Objective: To provide list of recommendations on alternative protective equipment during this Coronavirus Disease 2019 (COVID-19) pandemic Methodology: Articles available on the various research databases were reviewed, appraised and evaluated for its quality and relevance. Discrepancies were rechecked and consensus was achieved by discussion. Recommendations: The use of engineering control such as barriers in the reception areas minimize the risk of healthcare personnel. Personal protective equipment needed are face shields or googles, N95 respirators, impermeable gown and gloves. If supplies are limited, the use of N95 respirators are prioritized in performing aerosol-generating procedures, otherwise, surgical masks are acceptable alternative. Cloth masks do not give adequate protection, but can be considered if it is used with face shield. Fluid-resistance, impermeable gown and non-sterile disposable gloves are recommended when attending to patients suspected or confirmed COVID-19. Used, soiled or damaged PPE should be carefully removed and properly discarded. Extended use of PPE can be considered, while re-use is only an option if supplies run low. Reusable equipment should be cleaned and disinfected every after use Conclusion In supplies shortage, personal protective equipment was optimized by extended use and reuse following observance of standard respiratory infection control procedures such as avoid touching the face and handwashing. The addition of physical barriers in ambulatory and triage areas add another layer of protection
Personal Protective Equipment ; Triage

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

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