Effect of Testing and Treatment on Emergency Department Length of Stay Using a National Database

Objectives:  Testing and treatment are essential aspects of the delivery of emergency care. Recognition of the effects of these activities on emergency department (ED) length of stay (LOS) has implications for administrators planning efficient operations, providers, and patients regarding expectations for length of visit; researchers in creating better models to predict LOS; and policy‐makers concerned about ED crowding. Methods:  A secondary analysis was performed using years 2006 through 2008 of the National Hospital Ambulatory Medical Care Survey (NHAMCS), a nationwide study of ED services. In univariate and bivariate analyses, the authors assessed ED LOS and frequency of testing (blood test, urinalysis, electrocardiogram [ECG], radiograph, ultrasound, computed tomography [CT], or magnetic resonance imaging [MRI]) and treatment (providing a medication or performance of a procedure) according to disposition (discharged or admitted status). Two sets of multivariable models were developed to assess the contribution of testing and treatment to LOS, also stratified by disposition. The first was a series of logistic regression models to provide an overview of how testing and treatment activity affects three dichotomized LOS cutoffs at 2, 4, and 6 hours. The second was a generalized linear model (GLM) with a log‐link function and gamma distribution to fit skewed LOS data, which provided time costs associated with tests and treatment. Results:  Among 360 million weighted ED visits included in this analysis, 227 million (63%) involved testing, 304 million (85%) involved treatment, and 201 million (56%) involved both. Overall, visits with any testing were associated with longer LOS (median = 196 minutes; interquartile range [IQR] = 125 to 305 minutes) than those with any treatment (median = 159 minutes; IQR = 91 to 262 minutes). This difference was more pronounced among discharged patients than admitted patients. Obtaining a test was associated with an adjusted odds ratio (OR) of 2.29 (95% confidence interval [CI] = 1.86 to 2.83) for experiencing a more than 4‐hour LOS, while performing a treatment had no effect (adjusted OR = 0.84; 95% CI = 0.68 to 1.03). The most time‐costly testing modalities included blood test (adjusted marginal effects on LOS = +72 minutes; 95% CI = 66 to 78 minutes), MRI (+64 minutes; 95% CI = 36 to 93 minutes), CT (+59 minutes; 95% CI = 54 to 65 minutes), and ultrasound (US; +56 minutes; 95% CI = 45 to 67 minutes). Treatment time costs were less substantial: performing a procedure (+24 minutes; 95% CI = 20 to 28 minutes) and providing a medication (+15 minutes; 95% CI = 8 to 21 minutes). Conclusions:  Testing and less substantially treatment were associated with prolonged LOS in the ED, particularly for blood testing and advanced imaging. This knowledge may better direct efforts at streamlining delivery of care for the most time‐costly diagnostic modalities or suggest areas for future research into improving processes of care. Developing systems to improve efficient utilization of these services in the ED may improve patient and provider satisfaction. Such practice improvements could then be examined to determine their effects on ED crowding.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92123/1/j.1553-2712.2012.01353.x.pd

A latent variable approach to potential outcomes for emergency department admission decisions

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151329/1/sim8210.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151329/2/sim8210_am.pd

Validation of a tool predicting important findings on computed tomography among Crohn’s disease patients

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166167/1/ueg2bf00677.pd

Categorization, Designation, and Regionalization of Emergency Care: Definitions, a Conceptual Framework, and Future Challenges

This article reflects the proceedings of a breakout session, “Beyond ED Categorization—Matching Networks to Patient Needs,” at the 2010 Academic Emergency Medicine consensus conference, “Beyond Regionalization: Integrated Networks of Emergency Care.” It is based on concepts and areas of priority identified and developed by the authors and participants at the conference. The paper first describes definitions fundamental to understanding the categorization, designation, and regionalization of emergency care and then considers a conceptual framework for this process. It also provides a justification for a categorization system being integrated into a regionalized emergency care system. Finally, it discusses potential challenges and barriers to the adoption of a categorization and designation system for emergency care and the opportunities for researchers to study the many issues associated with the implementation of such a system.ACADEMIC EMERGENCY MEDICINE 2010; 17:1306–1311 © 2010 by the Society for Academic Emergency MedicinePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79324/1/j.1553-2712.2010.00932.x.pd

Important Historical Efforts at Emergency Department Categorization in the United States and Implications for Regionalization

This article is drawn from a report created for the American College of Emergency Physicians (ACEP) Emergency Department (ED) Categorization Task Force and also reflects the proceedings of a breakout session, “Beyond ED Categorization—Matching Networks to Patient Needs,” at the 2010 Academic Emergency Medicine consensus conference, “Beyond Regionalization: Integrated Networks of Emergency Care.” The authors describe a brief history of the significant national and state efforts at categorization and suggest reasons why many of these efforts failed to persevere or gain wider implementation. The history of efforts to categorize hospital (and ED) emergency services demonstrates recognition of the potential benefits of categorization, but reflects repeated failures to implement full categorization systems or limited excursions into categorization through licensing of EDs or designation of receiving and referral facilities. An understanding of the history of hospital and ED categorization could better inform current efforts to develop categorization schemes and processes.ACADEMIC EMERGENCY MEDICINE 2010; 17:e154–e160 © 2010 by the Society for Academic Emergency MedicinePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79214/1/j.1553-2712.2010.00931.x.pd

Variation in practice patterns among specialties in the acute management of atrial fibrillation

Abstract Background Atrial fibrillation (AF) is commonly managed by a variety of specialists. Current guidelines differ in their recommendations leading to uncertainty regarding important clinical decisions. We sought to document practice pattern variation among cardiologists, emergency physicians (EP) and hospitalists at a single academic, tertiary-care center. Methods A survey was created containing seven clinical scenarios of patients presenting with AF. We analyzed respondent choices regarding rate vs rhythm control, thromboembolic treatment and hospitalization strategies. Finally, we contrasted our findings with a comparable Australasian survey to provide an international reference. Results There was a 78% response rate (124 of 158), 37% hospitalists, 31.5% cardiologists, and 31.5% EP. Most respondents chose rate over rhythm control (92.2%; 95% CI, 89.1% - 94.5%) and thromboembolic treatment (67.8%; 95% CI, 63.8% - 71.7%). Compared to both hospitalists and EPs, cardiologists were more likely to choose thromboembolic treatment for new and paroxysmal AF (adjusted OR 2.38; 95% CI, 1.05 - 5.41). They were less likely to favor hospital admission across all types of AF (adjusted OR 0.36; 95% CI, 0.17 - 0.79) but thought cardiology consultation was more important (adjusted OR 1.88, 95% CI, 0.97 - 3.64). Australasian physicians were more aggressive with rhythm control for paroxysmal AF with low CHADS2 score compared to US physicians. Conclusions Significant variation exists among specialties in the management of acute AF, likely reflecting a lack of high quality research to direct the provider. Future studies may help to standardize practice leading to decreased rates of hospitalization and overall cost.http://deepblue.lib.umich.edu/bitstream/2027.42/110777/1/12872_2015_Article_9.pd

Quality assessment metrics for whole genome gene expression profiling of paraffin embedded samples

BACKGROUND: Formalin fixed, paraffin embedded tissues are most commonly used for routine pathology analysis and for long term tissue preservation in the clinical setting. Many institutions have large archives of Formalin fixed, paraffin embedded tissues that provide a unique opportunity for understanding genomic signatures of disease. However, genome-wide expression profiling of Formalin fixed, paraffin embedded samples have been challenging due to RNA degradation. Because of the significant heterogeneity in tissue quality, normalization and analysis of these data presents particular challenges. The distribution of intensity values from archival tissues are inherently noisy and skewed due to differential sample degradation raising two primary concerns; whether a highly skewed array will unduly influence initial normalization of the data and whether outlier arrays can be reliably identified. FINDINGS: Two simple extensions of common regression diagnostic measures are introduced that measure the stress an array undergoes during normalization and how much a given array deviates from the remaining arrays post-normalization. These metrics are applied to a study involving 1618 formalin-fixed, paraffin-embedded HER2-positive breast cancer samples from the N9831 adjuvant trial processed with Illumina’s cDNA-mediated Annealing Selection extension and Ligation assay. CONCLUSION: Proper assessment of array quality within a research study is crucial for controlling unwanted variability in the data. The metrics proposed in this paper have direct biological interpretations and can be used to identify arrays that should either be removed from analysis all together or down-weighted to reduce their influence in downstream analyses

Risk factors and outcomes associated with community-onset and hospital-acquired coinfection in patients hospitalized for coronavirus disease 2019 (COVID-19): A multihospital cohort study

BACKGROUND: We sought to determine the incidence of community-onset and hospital-acquired coinfection in patients hospitalized with coronavirus disease 2019 (COVID-19) and to evaluate associated predictors and outcomes. METHODS: In this multicenter retrospective cohort study of patients hospitalized for COVID-19 from March 2020 to August 2020 across 38 Michigan hospitals, we assessed prevalence, predictors, and outcomes of community-onset and hospital-acquired coinfections. In-hospital and 60-day mortality, readmission, discharge to long-term care facility (LTCF), and mechanical ventilation duration were assessed for patients with versus without coinfection. RESULTS: Of 2,205 patients with COVID-19, 141 (6.4%) had a coinfection: 3.0% community onset and 3.4% hospital acquired. Of patients without coinfection, 64.9% received antibiotics. Community-onset coinfection predictors included admission from an LTCF (OR, 3.98; 95% CI, 2.34-6.76; P \u3c .001) and admission to intensive care (OR, 4.34; 95% CI, 2.87-6.55; P \u3c .001). Hospital-acquired coinfection predictors included fever (OR, 2.46; 95% CI, 1.15-5.27; P = .02) and advanced respiratory support (OR, 40.72; 95% CI, 13.49-122.93; P \u3c .001). Patients with (vs without) community-onset coinfection had longer mechanical ventilation (OR, 3.31; 95% CI, 1.67-6.56; P = .001) and higher in-hospital mortality (OR, 1.90; 95% CI, 1.06-3.40; P = .03) and 60-day mortality (OR, 1.86; 95% CI, 1.05-3.29; P = .03). Patients with (vs without) hospital-acquired coinfection had higher discharge to LTCF (OR, 8.48; 95% CI, 3.30-21.76; P \u3c .001), in-hospital mortality (OR, 4.17; 95% CI, 2.37-7.33; P ≤ .001), and 60-day mortality (OR, 3.66; 95% CI, 2.11-6.33; P ≤ .001). CONCLUSION: Despite community-onset and hospital-acquired coinfection being uncommon, most patients hospitalized with COVID-19 received antibiotics. Admission from LTCF and to ICU were associated with increased risk of community-onset coinfection. Future studies should prospectively validate predictors of COVID-19 coinfection to facilitate the reduction of antibiotic use