ObjectivePatients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to ...derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments.MethodsTwelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020).ResultsThere were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period.ConclusionsElectronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.
To compare sociodemographic factors in patients presenting to the emergency department (ED) with emergent and non-emergent eye-related concerns.
Cross-sectional multicenter study.
60,677 patients ...with eye-related concerns who visited EDs at Bascom Palmer Eye Institute, Wills Eye Hospital, Massachusetts Eye and Ear, and Johns Hopkins Hospital/Wilmer Eye Institute from January 1st, 2019 until December 31st, 2019.
Descriptive statistics were performed using STATA 17.
1) Sociodemographic factors associated with emergent diagnoses, 2) Visit patterns across ED settings (i.e. standard ED vs eye ED), and 3) the most common emergent and non-emergent diagnoses.
A total of 60,677 eye-related ED encounters were included in the study, including 22,434 at Bascom Palmer Eye Institute, 16,124 at Wills Eye Hospital, 15,487 at Massachusetts Eye and Ear, and 6,632 at Johns Hopkins Hospital/Wilmer Eye Institute. Most patients had non-emergent diagnoses (56.7%). Males (OR 1.85, 95% CI 1.79-1.92) were more likely to have an emergent diagnosis than females. Patients with private/employer-based insurance (OR 0.88, 95% CI 0.81-0.96), Medicare (OR 0.80, 95% CI 0.72-0.87), and Medicaid (OR 0.81, 95% CI 0.74-0.89) were all less likely to have an emergent diagnosis than uninsured patients. Those with veteran/military insurance (OR 1.08, 95% CI 0.87-1.34) were equally likely to have an emergent diagnosis compared to uninsured patients. Non-White Hispanic patients (OR 1.26, 95% CI 1.12-1.42) were more likely to present with an emergent condition than White patients. Patient seen in the standard ED setting were more likely to have emergent diagnoses than those who visited standalone eye EDs (P < 0.001). The most common emergent diagnoses were corneal abrasion (12.97%), extraocular foreign body (7.61%), and corneal ulcer (7.06%). The most common non-emergent diagnoses were dry eye (7.90%), posterior vitreous detachment (7.76%), and chalazion (6.57%).
ED setting was associated with the acuity of patient diagnoses. Lack of insurance coverage and non-White Hispanic race/ethnicity were associated with emergent eye-related ED visits. Improving access to ophthalmic care in these populations may reduce the incidence of preventable eye emergencies related to untreated chronic conditions. This combined with measures to redirect non-emergent issues to outpatient clinics may alleviate ED overload.
Title 1 Pre-hospital finger thoracostomy in patients with traumatic cardiac arrest 2 Pre-hospital finger thoracostomy in patients with chest trauma 3 In septic patients requiring fluid resuscitation ...can the bedside lung ultrasound be used to assess the pulmonary fluid status?
Each BET is based on a clinical scenario and ends with a clinical bottom line which indicates, in the light of the evidence found, what the reporting clinician would do if faced with the same ...scenario again.
Emergency medicine is a dynamically complex and well-coordinated specialty. However, paediatric emergency sub-specialty is not yet well developed in most low and middle income countries. The purpose ...of the present study is to analyze the pediatric patients who passed through the emergency department of the hospital. Results: Total examined children - 44547. Hospitalized – 12 062 - 30.7% of the total. Boys predominate (54.0%). Children in the age group 1-10 years - 81.8%. The patient flow in the pediatric emergency department from other districts increased by 35.8%. Conclusion: Children are a particularly vulnerable group of patients who go through the emergency department, which is related to the specifics of pediatric care. The results of the study show that the largest proportion of pediatric patients are children aged 1-10 years, males, who sought medical help in connection with respiratory infections.
Emergency Department admissions have changed significantly during the COVID-19 pandemic. Understanding this variation may play a crucial role in rearranging hospital resources for better outcome. In ...this study, we aimed to assess the impact of COVID-19 pandemic on emergency department admission and outcome.
This is a cross-sectional retrospective study conducted at Bharatpur Hospital, Nepal comparing pre- pandemic data of the 4 months (March 24 to July 21, 2019) with the initial 4 months of the pandemic (March 24 to July 21, 2020).
Admission in emergency ward decreased during covid period among female admission (47%vs43%), age-group(0-14)(18%vs12%), Dalit(17%vs11%) p<0.0001.Diagnosis increased during covid for acute abdomen(11%vs13%), animal and insect bite(10%vs13%), psychiatric illness(2%vs6%),poisoning and drug over dose(0.9%vs2.6%)(p<0.0001).The odds for referral(cOR 3.62,95% CI:2.70-4.84), Left against medical advice(cOR 6.03,95% CI:.06-8.94) and death(cOR 3.28,95% CI:1.64-6.68) increased during the covid respectively.
There was decrease in rates of emergency department utilization during the Covid-19 pandemic. Admissions due to trauma, gastrointestinal, respiratory, neurological, musculoskeletal and coronary artery disease showed a decline whereas psychiatric disorders, diabetes and hypertension, animal and insect bites cases increased. Overall, mortality rate was increased.
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