Understanding the Value of Emergency Care: A Framework Incorporating Stakeholder Perspectives

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confidence: 99%

Computed Tomography Use for Adults With Head Injury: Describing Likely Avoidable Emergency Department Imaging Based on the Canadian CT Head Rule

Sharp

Nagaraj

Rippberger

et al. 2017

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“…E mergency care is receiving increasing scrutiny as a potential area for cost reductions. [1][2][3] Management of emergency department (ED) costs begins with examining how ED operational characteristics contribute to both patient-level and system-level outcomes. One such characteristic under scrutiny is ED crowding, a phenomenon that can be partially attributed to rising practice intensity 4,5 and inpatient boarding.…”

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confidence: 99%

The Impact of Emergency Department Census on the Decision to Admit

Gorski

Batt

Ötleş

et al. 2017

Objective: We evaluated the effect of emergency department (ED) census on disposition decisions made by ED physicians.Methods: We performed a retrospective analysis using 18 months of all adult patient encounters seen in the main ED at an academic tertiary care center. Patient census information was calculated at the time of physician assignment for each individual patient and included the number of patients in the waiting room (waiting room census) and number of patients being managed by the patient's attending (physician load census). A multiple logistic regression model was created to assess the association between these census variables and the disposition decision, controlling for potential confounders including Emergency Severity Index acuity, patient demographics, arrival hour, arrival mode, and chief complaint.Results: A total of 49,487 patient visits were included in this analysis, of whom 37% were admitted to the hospital. Both census measures were significantly associated with increased chance of admission; the odds ratio (OR) per patient increase for waiting room census was 1.011 (95% confidence interval [CI] = 1.001 to 1.020), and the OR for physician load census was 1.010 (95% CI = 1.002 to 1.019). To put this in practical terms, this translated to a modeled rise from 35.3% to 40.1% when shifting from an empty waiting room and zero patient load to a 12-patient wait and 16-patient load for a given physician. Conclusion:Waiting room census and physician load census at time of physician assignment were positively associated with the likelihood that a patient would be admitted, controlling for potential confounders. Our data suggest that disposition decisions in the ED are influenced not only by objective measures of a patient's disease state, but also by workflow-related concerns. E mergency care is receiving increasing scrutiny as a potential area for cost reductions.1-3 Management of emergency department (ED) costs begins with examining how ED operational characteristics contribute to both patient-level and system-level outcomes. One such characteristic under scrutiny is ED crowding, a phenomenon that can be partially attributed to rising practice intensity 4,5 and inpatient boarding. 6-8 As ED utilization rises, research on the effects of crowding has primarily focused on its influence on patient outcomes, including treatment delays 9-11 and patient mortality.12-15 Less well characterized are organizational responses to increasing operational pressure, such as changes to rates of admission.Highly relevant to the discussion of the cost of emergency care is the growing role of the ED as a "gatekeeper" for inpatient services. 1,16,17 In a 2013 RAND report, hospital admissions rose 4% from 34.7 The authors have no relevant financial information or potential conflicts to disclose. Author contributions: JKG, RJB, and BWP conceived the study; JKG conducted the primary data analysis, with advice and input from RJB and BWP; EO helped with data analysis and offered further methodologic input; JKG pri...

“…3,4 As the key decision makers about hospitalization, emergency department (ED) providers play a key role in assuring appropriate use of the hospital. 5 Several studies show that there is unexplained variability among ED physicians' decisions to admit or discharge patients. [6][7][8][9] One way for providers to reduce variation in hospitalization would be to use proven decision-making tools.…”

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confidence: 99%

CURB‐65 Performance Among Admitted and Discharged Emergency Department Patients With Community‐acquired Pneumonia

Sharp

Jones²,

et al. 2016

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Objectives: Pneumonia severity tools were primarily developed in cohorts of hospitalized patients, limiting their applicability to the emergency department (ED). We describe current community ED admission practices and examine the accuracy of the CURB-65 to predict 30-day mortality for patients, either discharged or admitted with community-acquired pneumonia (CAP).Methods: A retrospective, observational study of adult CAP encounters in 14 community EDs within an integrated healthcare system. We calculated CURB-65 scores for all encounters and described the use of hospitalization, stratified by each score (0-5). We then used each score as a cutoff to calculate sensitivity, specificity, positive predictive value, negative predictive value (NPV), positive likelihood ratios, and negative likelihood ratios for predicting 30-day mortality.Results: The sample included 21,183 ED encounters for CAP (7,952 discharged and 13,231 admitted). The C-statistic describing the accuracy of CURB-65 for predicting 30-day mortality in the full sample was 0.761 (95% confidence interval [CI], 0.747-0.774). The C-statistic was 0.864 (95% CI, 0.821-0.906) among patients discharged from the ED compared with 0.689 (95% CI, 0.672-0.705) among patients who were admitted. Among all ED encounters a CURB-65 threshold of ≥1 was 92.8% sensitive and 38.0% specific for predicting mortality, with a 99.9% NPV. Among all encounters, 62.5% were admitted, including 36.2% of those at lowest risk (CURB-65 = 0).Conclusions: CURB-65 had very good accuracy for predicting 30-day mortality among patients discharged from the ED. This severity tool may help ED providers risk stratify patients to assist with disposition decisions and identify unwarranted variation in patient care.ACADEMIC EMERGENCY MEDICINE 2016;23:400-405 © 2016 by the Society for Academic Emergency Medicine P neumonia is a leading cause of death in the United States and accounts for over 1.2 million hospitalizations annually, resulting in $10.2 billion in health care costs.1,2 Unnecessary hospitalizations put patients at risk for adverse events and strain the limited resources of an already taxed health care system. 3,4 As the key decision makers about hospitalization, emergency department (ED) providers play a key role in assuring appropriate use of the hospital.5 Several studies show that there is unexplained variability among ED physicians' decisions to admit or discharge patients. [6][7][8][9] One way for providers to reduce variation in hospitalization would be to use proven decision-making tools. 10To date, pneumonia severity tools have been developed and validated in cohorts of hospitalized patients with very little information in outpatient cohorts.11-13 To