The effects of emergency department crowding on triage and hospital admission decisions.

BACKGROUND: Emergency department (ED) crowding is a recognized issue and it has been suggested that it can affect clinician decision-making. OBJECTIVES: Our objective was to determine whether ED census was associated with changes in triage or disposition decisions made by ED nurses and physicians. METHODS: We performed a retrospective study using one year of data obtained from a US academic center ED (65,065 patient encounters after cleaning). Using a cumulative logit model, we investigated the association between a patient's acuity group (low, medium, and high) and ED census at triage time. We also used multivariate logistic regression to investigate the association between the disposition decision for a patient (admit or discharge) and the ED census at the disposition decision time. In both studies, control variables included census, age, gender, race, place of treatment, chief complaint, and certain interaction terms. RESULTS: We found statistically significant correlation between ED census and triage/disposition decisions. For each additional patient in the ED, the odds of being assigned a high acuity versus medium or low acuity at triage is 1.011 times higher (95% confidence interval [CI] for Odds Ratio [OR] = [1.009,1.012]), and the odds of being assigned medium or high acuity versus low acuity at triage is 1.009 times higher (95% CI for OR = [1.008,1.010]). Similarly, the odds of being admitted versus discharged increases by 1.007 times (95% CI for OR = [1.006,1.008]) per additional patient in the ED at the time of disposition decision. CONCLUSION: Increased ED occupancy was found to be associated with more patients being classified as higher acuity as well as higher hospital admission rates. As an example, for a commonly observed patient category, our model predicts that as the ED occupancy increases from 25 to 75 patients, the probability of a patient being triaged as high acuity increases by about 50% and the probability of a patient being categorized as admit increases by around 25%.

Decay of fecal indicator bacterial populations and bovine-associated source-tracking markers in freshly deposited cow pats.

Understanding the survival of fecal indicator bacteria (FIB) and microbial source-tracking (MST) markers is critical to developing pathogen fate and transport models. Although pathogen survival in water microcosms and manure-amended soils is well documented, little is known about their survival in intact cow pats deposited on pastures. We conducted a study to determine decay rates of fecal indicator bacteria (Escherichia coli and enterococci) and bovine-associated MST markers (CowM3, Rum-2-bac, and GenBac) in 18 freshly deposited cattle feces from three farms in northern Georgia. Samples were randomly assigned to shaded or unshaded treatment in order to determine the effects of sunlight, moisture, and temperature on decay rates. A general linear model (GLM) framework was used to determine decay rates. Shading significantly decreased the decay rate of the E. coli population (P < 0.0001), with a rate of -0.176 day(-1) for the shaded treatment and -0.297 day(-1) for the unshaded treatment. Shading had no significant effect on decay rates of enterococci, CowM3, Rum-2-bac, and GenBac (P > 0.05). In addition, E. coli populations showed a significant growth rate (0.881 day(-1)) in the unshaded samples during the first 5 days after deposition. UV-B was the most important parameter explaining the decay rate of E. coli populations. A comparison of the decay behaviors among all markers indicated that enterococcus concentrations exhibit a better correlation with the MST markers than E. coli concentrations. Our results indicate that bovine-associated MST markers can survive in cow pats for at least 1 month after excretion, and although their decay dynamic differs from the decay dynamic of E. coli populations, they seem to be reliable markers to use in combination with enterococci to monitor fecal pollution from pasture lands.

Accuracy of the electronic health record's problem list in describing multimorbidity in patients with heart failure in the emergency department.

Patients with heart failure (HF) often suffer from multimorbidity. Rapid assessment of multimorbidity is important for minimizing the risk of harmful drug-disease and drug-drug interactions. We assessed the accuracy of using the electronic health record (EHR) problem list to identify comorbid conditions among patients with chronic HF in the emergency department (ED). A retrospective chart review study was performed on a random sample of 200 patients age ≥65 years with a diagnosis of HF presenting to an academic ED in 2019. We assessed participant chronic conditions using: (1) structured chart review (gold standard) and (2) an EHR-based algorithm using the problem list. Chronic conditions were classified into 37 disease domains using the Agency for Healthcare Research Quality's Elixhauser Comorbidity Software. For each disease domain, we report the sensitivity, specificity, positive predictive value, and negative predictive of using an EHR-based algorithm. We calculated the intra-class correlation coefficient (ICC) to assess overall agreement on Elixhauser domain count between chart review and problem list. Patients with HF had a mean of 5.4 chronic conditions (SD 2.1) in the chart review and a mean of 4.1 chronic conditions (SD 2.1) in the EHR-based problem list. The five most prevalent domains were uncomplicated hypertension (90%), obesity (42%), chronic pulmonary disease (38%), deficiency anemias (33%), and diabetes with chronic complications (30.5%). The positive predictive value and negative predictive value of using the EHR-based problem list was greater than 90% for 24/37 and 32/37 disease domains, respectively. The EHR-based problem list correctly identified 3.7 domains per patient and misclassified 2.0 domains per patient. Overall, the ICC in comparing Elixhauser domain count was 0.77 (95% CI: 0.71-0.82). The EHR-based problem list captures multimorbidity with moderate-to-good accuracy in patient with HF in the ED.