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Variation in US hospital emergency department admission rates by clinical condition.

ABSTRACT: BACKGROUND:Variation in hospitalization rates have been described for decades, yet little is known about variation in emergency department (ED) admission rates across clinical conditions. We sought to describe variation in ED risk-standardized admission rates (RSAR) and the consistency between condition-specific ED admission rates within hospitals. METHODS:Cross-sectional analysis of the 2009 National Emergency Department Sample, an all-payer administrative, claims dataset. We identify the 15 most frequently admitted conditions using Clinical Classification Software. To identify conditions with the highest ED RSAR variation, we compared both the ratio of the 75th percentile to the 25th percentile hospital and coefficient of variation between conditions. We calculate Spearman correlation coefficients to assess within-hospital correlation of condition-specific ED RSARs. RESULTS:Of 21,885,845 adult ED visits, 4,470,105 (20%) resulted in admission. Among the 15 most frequently admitted conditions, the 5 with the highest magnitude of variation were: mood disorders (ratio of 75th:25th percentile, 6.97; coefficient of variation, 0.81), nonspecific chest pain (2.68; 0.66), skin and soft tissue infections (1.82; 0.51), urinary tract infections (1.58; 0.43), and chronic obstructive pulmonary disease (1.57; 0.33). For these 5 conditions, the within-hospital RSAR correlations between each pair of conditions were >0.4, except for mood disorders, which was poorly correlated with all other conditions (r<0.3). CONCLUSIONS:There is significant condition-specific variation in ED admission rates across US hospitals. This variation appears to be consistent between conditions with high variation within hospitals.

SUBMITTER: Venkatesh AK

PROVIDER: S-EPMC4858175 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Variation in US hospital emergency department admission rates by clinical condition.

Venkatesh Arjun K AK Dai Ying Y Ross Joseph S JS Schuur Jeremiah D JD Capp Roberta R Krumholz Harlan M HM

Medical care 20150301 3

<h4>Background</h4>Variation in hospitalization rates have been described for decades, yet little is known about variation in emergency department (ED) admission rates across clinical conditions. We sought to describe variation in ED risk-standardized admission rates (RSAR) and the consistency between condition-specific ED admission rates within hospitals.<h4>Methods</h4>Cross-sectional analysis of the 2009 National Emergency Department Sample, an all-payer administrative, claims dataset. We ide ...[more]

PMID: 25397965

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Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Project description:BackgroundRecent literature reports a decrease in healthcare-seeking behaviours by adults during the Covid-19 pandemic. Given that emergency general surgery (GS) conditions are often associated with high morbidity and mortality if left untreated, the objective of this study was to describe and quantify the impact of the Covid-19 pandemic on rates of emergency department (ED) utilization and hospital admission due to GS conditions.MethodsThis cohort study involved the analysis of an institutional database and retrospective chart review. We identified adult patients presenting to the ED in a network of three teaching hospitals in Montreal, Canada during the first wave of the Covid-19 pandemic (March13-May13, 2020) and a control pre-pandemic period (March13-May13, 2019). Patients with GS conditions were included in the analysis. ED utilization rates, admission rates and 30-day outcomes were compared between the two periods using multivariate regression analysis.ResultsDuring the pandemic period, 258 patients presented to ED with a GS diagnosis compared to 351 patients pre-pandemically (adjusted rate ratio (aRR) 0.75; p < 0.001). Rate of hospital admission during the pandemic was also significantly lower (aRR = 0.77, p < 0.001). Patients had a significantly shorter ED stay during the pandemic (adjusted mean difference 5.0 h; p < 0.001). Rates of operative management during the pandemic were preserved compared to the pre-pandemic period. There were no differences in 30-day complications (adjusted odds ratio (aOR) 1.46; p = 0.07), ED revisits (aOR 1.10; p = 0.66) and (re)admissions (aOR 1.42; p = 0.22) between the two periods.ConclusionThere was a decrease in rates of ED utilization and hospital admissions due to GS conditions during the first wave of the Covid -19 pandemic; however, rates of operative management, complications and healthcare reutilization were unchanged. Although our findings are not generalizable to patients who did not seek healthcare, it was possible to successfully uphold institutional standards of care once patients presented to the ED.

Project description:ImportanceA surgical consultation is a critical first step in the care of patients with emergency general surgery conditions. It is unknown if Black Medicare patients and White Medicare patients receive surgical consultations at similar rates when they are admitted from the emergency department.ObjectiveTo determine whether Black Medicare patients have similar rates of surgical consultations when compared with White Medicare patients after being admitted from the emergency department with an emergency general surgery condition.Design, setting, and participantsThis was a retrospective cohort study that took place at US hospitals with an emergency department and used a computational generalization of inverse propensity score weight to create patient populations with similar covariate distributions. Participants were Medicare patients age 65.5 years or older admitted from the emergency department for an emergency general surgery condition between July 1, 2015, and June 30, 2018. The analysis was performed during February 2022. Patients were classified into 1 of 5 emergency general surgery condition categories based on principal diagnosis codes: colorectal, general abdominal, hepatopancreatobiliary, intestinal obstruction, and upper gastrointestinal.ExposuresBlack vs White race.Main outcomes and measuresReceipt of a surgical consultation after admission from the emergency department with an emergency general surgery condition.ResultsA total of 1 686 940 patients were included in the study. Of those included, 214 788 patients were Black (12.7%) and 1 472 152 patients were White (87.3%). After standardizing for medical and diagnostic imaging covariates, Black patients had 14% lower odds of receiving a surgical consultation (odds ratio [OR], 0.86; 95% CI, 0.85-0.87) with a risk difference of -3.17 (95% CI, -3.41 to -2.92). After standardizing for socioeconomic covariates, Black patients remained at an 11% lower odds of receiving a surgical consultation compared with similar White patients (OR, 0.89; 95% CI, 0.88-0.90) with a risk difference of -2.49 (95% CI, -2.75 to -2.23). Additionally, when restricting the analysis to Black patients and White patients who were treated in the same hospitals, Black patients had 8% lower odds of receiving a surgical consultation when compared with White patients (OR, 0.92; 95% CI, 0.90-0.93) with a risk difference of -1.82 (95% CI, -2.18 to -1.46).Conclusions and relevanceIn this study, Black Medicare patients had lower odds of receiving a surgical consultation after being admitted from the emergency department with an emergency general surgery condition when compared with similar White Medicare patients. These disparities in consultation rates cannot be fully attributed to medical comorbidities, insurance status, socioeconomic factors, or individual hospital-level effects.

Project description:ObjectivesHospitalisation is frequently used as a marker of disease severity in observational Emergency Department (ED) studies. The comparison of ED admission rates is complex in potentially being influenced by the characteristics of the region, ED, physician and patient. We aimed to study variation in ED admission rates of febrile children, to assess whether variation could be explained by disease severity and to identify patient groups with large variation, in order to use this to reduce unnecessary health care utilization that is often due to practice variation.DesignMOFICHE (Management and Outcome of Fever in children in Europe, part of the PERFORM study, www.perform2020.org), is a prospective cohort study using routinely collected data on febrile children regarding patient characteristics (age, referral, vital signs and clinical alarming signs), diagnostic tests, therapy, diagnosis and hospital admission.Setting and participantsData were collected on febrile children aged 0-18 years presenting to 12 European EDs (2017-2018).Main outcome measuresWe compared admission rates between EDs by using standardised admission rates after adjusting for patient characteristics and initiated tests at the ED, where standardised rates >1 demonstrate higher admission rates than expected and rates <1 indicate lower rates than expected based on the ED patient population.ResultsWe included 38,120 children. Of those, 9.695 (25.4%) were admitted to a general ward (range EDs 5.1-54.5%). Adjusted standardised admission rates ranged between 0.6 and 1.5. The largest variation was seen in short admission rates (0.1-5.0), PICU admission rates (0.2-2.2), upper respiratory tract infections (0.4-1.7) and fever without focus (0.5-2.7). Variation was small in sepsis/meningitis (0.9-1.1).ConclusionsLarge variation exists in admission rates of febrile children evaluated at European EDs, however, this variation is largely reduced after correcting for patient characteristics and therefore overall admission rates seem to adequately reflect disease severity or a potential for a severe disease course. However, for certain patient groups variation remains high even after adjusting for patient characteristics.

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Variation in US hospital emergency department admission rates by clinical condition.

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Variation in US hospital emergency department admission rates by clinical condition.

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