COVID-19 cases presenting to the Emergency Department predict Qatar National COVID-19 trends and numbers.

In this short communication, we summarized the analyses, models, and interpretations of the corporate department of emergency medicine's (CDEM) COVID-19 numbers and their relationship to predict the national COVID-19 trends and numbers in Qatar. Data included in this analysis were obtained between March 1, 2020 and July 31, 2021. It included the number of COVID-19 cases that presented to four major EDs under the Hamad Medical Corporation CDEM umbrella and published data from the Qatar Ministry of public health (MoPH). On plotting weighted scatterplot smoothing (lowess) trend lines, there were striking similarities between CDEM and national COVID-19 n curves for overall trends and peaks. In conclusion, CDEM COVID-19 spike may be useful to predict national COVID-19 spike in 2-3 weeks.

Qatar Prediction Rule Using ED Indicators of COVID-19 at Triage.

INTRODUCTION: The presence of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and its associated disease, COVID-19 has had an enormous impact on the operations of the emergency department (ED), particularly the triage area. The aim of the study was to derive and validate a prediction rule that would be applicable to Qatar's adult ED population to predict COVID-19-positive patients. METHODS: This is a retrospective study including adult patients. The data were obtained from the electronic medical records (EMR) of the Hamad Medical Corporation (HMC) for three EDs. Data from the Hamad General Hospital ED were used to derive and internally validate a prediction rule (Q-PREDICT). The Al Wakra Hospital ED and Al Khor Hospital ED data formed an external validation set consisting of the same time frame. The variables in the model included the weekly ED COVID-19-positivity rate and the following patient characteristics: region (nationality), age, acuity, cough, fever, tachypnea, hypoxemia, and hypotension. All statistical analyses were executed with Stata 16.1 (Stata Corp). The study team obtained appropriate institutional approval. RESULTS: The study included 45,663 adult patients who were tested for COVID-19. Out of these, 47% (n = 21461) were COVID-19 positive. The derivation-set model had very good discrimination (c = 0.855, 95% Confidence intervals (CI) 0.847-0.861). Cross-validation of the model demonstrated that the validation-set model (c = 0.857, 95% CI 0.849-0.863) retained high discrimination. A high Q-PREDICT score ( ≥ 13) is associated with a nearly 6-fold increase in the likelihood of being COVID-19 positive (likelihood ratio 5.9, 95% CI 5.6-6.2), with a sensitivity of 84.7% (95% CI, 84.0%-85.4%). A low Q-PREDICT ( ≤ 6) is associated with a nearly 20-fold increase in the likelihood of being COVID-19 negative (likelihood ratio 19.3, 95% CI 16.7-22.1), with a specificity of 98.7% (95% CI 98.5%-98.9%). CONCLUSION: The Q-PREDICT is a simple scoring system based on information readily collected from patients at the front desk of the ED and helps to predict COVID-19 status at triage. The scoring system performed well in the internal and external validation on datasets obtained from the state of Qatar.

Evaluation of Electronic Medical Record Downtime in a Busy Emergency Department.

OBJECTIVES: This study aimed to investigate electronic medical record (EMR) implementation in a busy urban academic emergency department (ED) and to determine the frequency, duration, and predictors of EMR downtime episodes. MATERIALS AND METHODS: This study retrospectively analyzed data collected real time by the EMR and by the operations group at the study ED from May 2016 to December 2017. The study center has used the First Net Millennium EMR (Cerner Corporation, Kansas City, Missouri, USA). The ED operations data have been downloaded weekly from the EMR and transferred to the analytics software Stata (version 15MP, StataCorp, College Station, Texas, USA). RESULTS: During the study period, 12 episodes of EMRD occurred, with a total of 58 hours and a mean of 4.8 ± 2.7 hours. The occurrence of EMRD event has not been associated with on-duty physician coverage levels (p = 0.831), month (p = 0.850), or clinical shift (morning, evening, or night shift) (p = 0.423). However, EMRD occurrence has been statistically significantly associated with weekdays (p = 0.020). DISCUSSION: In a real-world implementation of EMR in a busy ED, EMRD episodes averaging approximately 5 hours occurred at unpredictable intervals, with a frequency that remained unchanged over the first 20 months of the EMR deployment. CONCLUSION: The study could define downtime characteristics at the study center. The EMRD episodes have been associated with inaccuracies in hourly census reporting, with a rebound phenomenon of over-reporting in the first hour or two after restoration of EMR operations.

The Impact of On-duty Emergency Medicine Trainees on Left-Without-Being-Seen Rates in an Academic Emergency Department.

Objectives: One of the endpoints for assessing the emergency department (ED) performance is the left-without-being-seen (LWBS) proportion. This study aimed to evaluate the impact of increasing proportions of on-duty emergency medicine (EM) trainees on LWBS rates in clinical shifts. Methods: The study was conducted at an urban-academic-ED (annual census: 452,757) over a period of one year. We employed multivariate linear regression (p < 0.05) defining significance to identify and adjust for multiple LWBS influencers related to patient care. Results: After analyzing over 1098 shifts, the median LWBS rate was 8.9% (interquartile range 5.3% to 13.5%). The increasing number of EM trainees in the ED did not adversely impact the LWBS; the opposite was noted. In univariate analysis, the increasing proportion of on-duty EM trainee physicians was significantly (p < 0.001) associated with a decrease in the LWBS rates. The multivariate model adjusted for the statistically significant and confounding LWBS influencers, with an absolute increase of 1% in trainees' proportion of overall on-duty physician coverage, was associated with an absolute decrease of 2.1% in LWBS rates (95% confidence interval 0.43% to 3.8%, p = 0.014). Conclusions: At the study site, there was a statistically and operationally significant improvement in LWBS associated with partial replacement of board-certified specialist-grade EM physicians with EM residents and fellow trainees.

Marginal analysis in assessing factors contributing time to physician in the Emergency Department using operations data.

Background: Standard Emergency Department (ED) operations goals include minimization of the time interval (tMD) between patients' initial ED presentation and initial physician evaluation. This study assessed factors known (or suspected) to influence tMD with a two-step goal. The first step was generation of a multivariate model identifying parameters associated with prolongation of tMD at a single study center. The second step was the use of a study center-specific multivariate tMD model as a basis for predictive marginal probability analysis; the marginal model allowed for prediction of the degree of ED operations benefit that would be affected with specific ED operations improvements. Methods: The study was conducted using one month (May 2015) of data obtained from an ED administrative database (EDAD) in an urban academic tertiary ED with an annual census of approximately 500,000; during the study month, the ED saw 39,593 cases. The EDAD data were used to generate a multivariate linear regression model assessing the various demographic and operational covariates' effects on the dependent variable tMD. Predictive marginal probability analysis was used to calculate the relative contributions of key covariates as well as demonstrate the likely tMD impact on modifying those covariates with operational improvements. Analyses were conducted with Stata 14MP, with significance defined at p < 0.05 and confidence intervals (CIs) reported at the 95% level. Results: In an acceptable linear regression model that accounted for just over half of the overall variance in tMD (adjusted r2 0.51), important contributors to tMD included shift census (p = 0.008), shift time of day (p = 0.002), and physician coverage n (p = 0.004). These strong associations remained even after adjusting for each other and other covariates. Marginal predictive probability analysis was used to predict the overall tMD impact (improvement from 50 to 43 minutes, p < 0.001) of consistent staffing with 22 physicians. Conclusions: The analysis identified expected variables contributing to tMD with regression demonstrating significance and effect magnitude of alterations in covariates including patient census, shift time of day, and number of physicians. Marginal analysis provided operationally useful demonstration of the need to adjust physician coverage numbers, prompting changes at the study ED. The methods used in this analysis may prove useful in other EDs wishing to analyze operations information with the goal of predicting which interventions may have the most benefit.