Effect of Emergency Department Crowding on Outcomes of Admitted Patients

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<ul><li><p>HEALTH POLICY/ORIGINAL RESEARCH</p><p>ntP. Weath</p><p>a prasselized</p><p>f pay ouas eospitthegrap</p><p>diagnosis, and hospital fixed effects. We used bootstrap sampling to estimate excess outcomes attributable toED crowding.</p><p>01Cohtt</p><p>SE</p><p>INBa</p><p>intamthebrea srestar</p><p>Im</p><p>an</p><p>VoResults: We studied 995,379 ED visits resulting in admission to 187 hospitals. Patients who were admitted ondays with high ED crowding experienced 5% greater odds of inpatient death (95% confidence interval [CI] 2% to8%), 0.8% longer hospital length of stay (95% CI 0.5% to 1%), and 1% increased costs per admission (95% CI0.7% to 2%). Excess outcomes attributable to periods of high ED crowding included 300 inpatient deaths (95%CI 200 to 500 inpatient deaths), 6,200 hospital days (95% CI 2,800 to 8,900 hospital days), and $17 million(95% CI $11 to $23 million) in costs.</p><p>Conclusion: Periods of high ED crowding were associated with increased inpatient mortality and modestincreases in length of stay and costs for admitted patients. [Ann Emerg Med. 2013;61:605-611.]</p><p>Please see page 606 for the Editors Capsule Summary of this article.</p><p>A feedback survey is available with each research article published on the Web at www.annemergmed.com.A podcast for this article is available at www.annemergmed.com.</p><p>96-0644/$-see front matterpyright 2012 by the American College of Emergency Physicians.p://dx.doi.org/10.1016/j.annemergmed.2012.10.026</p><p>E EDITORIAL, P. 612.</p><p>TRODUCTIONckgroundEmergency department (ED) crowding has become anernational health delivery problem.1-3 Increasing frequency ofbulance diversion and left-without-being-seen visits have ledInstitute of Medicine to describe US EDs as nearing theaking point,1 and multiple other countries have experienced</p><p>urge of ED crowding during the past decade. National policyponses have varied from none to system-wide performancegets.2</p><p>portanceEstablishing a definitive relationship between ED crowding</p><p>d subsequent mortality may motivate policymakers to address</p><p>ED crowding as a top public health priority. Limitations ofprevious studies assessing the effect of ED crowding onadmitted patients include small hospital samples (n1 to 6),4-8</p><p>lack of case-mix adjustment for comorbidities and primaryillness diagnosis,3-6,8 lack of adjustment for potential hospital-level confounders, and restriction to specific subgroups such aspatients with acute myocardial infarction,9 trauma,10</p><p>pneumonia,11 or critical illness.12</p><p>Goals of This InvestigationTo address these limitations, we studied the effect of ED</p><p>crowding on patient outcomes in a regional cohort of adultpatients admitted through an ED. ED crowding wasrepresented by a hospital-normalized measure of ambulancediversion hours on the day of admission. We hypothesized thathigh ED crowding would be associated with increased inpatient</p><p>lume , . : June Annals of Emergency Medicine 605Effect of Emergency DepartmeAdmitted</p><p>Benjamin C. Sun, MD, MPP; Renee Y. Hsia, MD; Robert EWeijuan Han, MS; Heather McCr</p><p>Study objective: Emergency department (ED) crowding isaffect the outcomes of patients requiring admission. Wesubsequent outcomes in a general population of hospita</p><p>Methods: We performed a retrospective cohort analysis ononfederal, acute care hospitals in California. The primarincluded hospital length of stay and costs. ED crowding wdiversion hours on the day of admission. To control for hdefined periods of high ED crowding as those days withinfacility. Hierarchic regression models controlled for demoCrowding on Outcomes ofatientseiss, PhD; David Zingmond, MD; Li-Jung Liang, PhD;, PhD; Steven M. Asch, MD</p><p>evalent health delivery problem and may adverselyss the association of ED crowding withpatients.</p><p>tients admitted in 2007 through the EDs oftcome was inpatient mortality. Secondary outcomesstablished by the proxy measure of ambulanceal-level confounders of ambulance diversion, wetop quartile of diversion hours for a specifichics, time variables, patient comorbidities, primary</p></li><li><p>mopo</p><p>MStu</p><p>adforbathechpeholocthetosucasshofacinc18ad</p><p>ofAn</p><p>Da</p><p>toHonofinSta</p><p>We obtained available data on all episodes of ambulancedivdivCacoumainproamconepiobbyEMpreunEDamreadiswewedivfro</p><p>Ou</p><p>oumofrovisdayintawappinDe</p><p>admcrolontoprodevrou</p><p>EMintTodivperdaitopthrcon</p><p>Editors Capsule Summary</p><p>Effect of Crowding on Patient Outcome Sun et al</p><p>60rtality rates, length of stay, and hospital costs in a generalpulation of hospitalized patients.</p><p>ATERIALS AND METHODSdy Design and ParticipantsWe performed a retrospective cohort study of adult</p><p>missions through the EDs of nonfederal California hospitals2007. Hospital-level exclusion criteria were the absence of</p><p>sic or comprehensive emergency services, facilities that closedir hospital or ED in 2007, and facilities that primarily served</p><p>ildren (because ED crowding may have differential effects indiatric compared with adult populations13). We excludedspitals that were prohibited from diverting ambulances byal emergency medical services (EMS) policy anytime duringstudy period. We also excluded hospitals that were allowed</p><p>but never requested ambulance diversion in 2007 becauseh hospitals would provide no information about theociation between ED crowding and outcomes withinspitals. Finally, we excluded hospitals with incompleteility-level information. Admission-level exclusion criterialuded transfers from other hospitals, patients younger thanyears, and missing ambulance diversion data on the day of</p><p>mission.This study was approved by the institutional review boardsthe state of California; the University of California, Losgeles; and the University of California, San Francisco.</p><p>ta Collection and ProcessingAll nonfederal health care facilities in California are requiredprovide hospital discharge data to the Office of Statewidespital Planning and Development. We obtained their</p><p>npublic use files for all admissions in 2007. Hospital-levelancial and structural data were extracted from 2007 Office oftewide Hospital Planning and Development public-use files.</p><p>What is already known on this topicEmergency department (ED) crowding is widelyprevalent and initiatives to end this condition havebecome a policy priority.</p><p>What question this study addressedThis study describes the association between EDcrowding and mortality, length of stay, and cost.</p><p>What this study adds to our knowledgeThe authors demonstrate that crowding is associatedwith increased mortality, length of stay, and cost.</p><p>How this is relevant to clinical practiceThe association between ED crowding and patientoutcomes strengthens the argument to end thepractice of ED boarding.6 Annals of Emergency Medicineersion in California for the study period. Ambulanceersion policies in 2007 were verified by the directors of all 31lifornia EMS agencies overseeing out-of-hospital care in 58nties. Depending on local EMS policy, ambulance diversiony have been permitted for all, some, or none of the hospitalsa county. Daily electronic ambulance diversion logs werevided by EMS agencies in California that permittedbulance diversion during the study period. These logstained the facility, date, duration, and reason for each</p><p>sode of ambulance diversion. We reformatted these data totain daily, facility-specific ambulance diversion hours causedED saturation. The definition of ED saturation varied byS agency; some agencies had explicit criteria (eg, the</p><p>sence of boarded patients awaiting an inpatient critical careit bed), although this was not universal. The enforcement of</p><p>saturation criteria to justify ambulance diversion also variedong the EMS agencies. Episodes of ambulance diversion forsons other than ED saturation, such as hospital internalaster or temporary lack of subspecialty or imaging services,re excluded from this analysis. Three of the EMS agenciesre missing data for 2 to 4 weeks because of upgrades to theirersion tracking software, and 1 EMS agency was missing datam January through March.</p><p>tcome MeasuresThe primary outcome was inpatient mortality. Secondarytcomes included length of stay and hospital costs. Inpatientrtality, length of stay, and hospital charges were obtainedm Office of Statewide Hospital Planning and Developmentit-level data. The length of stay was defined as the number ofs between the date of admission to the date of discharge; this</p><p>erval included time spent in the ED as a boarded patientaiting an inpatient bed. Hospital costs were estimated bylying overall facility-specific cost-to-charge ratios, available</p><p>2007 Office of Statewide Hospital Planning andvelopment public-use files, to hospital charges.We used daily ambulance diversion hours on the day ofission to create a hospital-normalized measure of ED</p><p>wding. Ambulance diversion occurs when ED staff can noger safely care for new patients and ambulances are divertednearby facilities. Ambulance diversion has face validity as axy measure14 and has been used as a criterion standard foreloping ED crowding scales.15 Ambulance diversion is alsotinely measured by out-of-hospital and regulatory agencies.16</p><p>Ambulance diversion rates vary greatly between and withinS systems.16 Differences in EMS policies and how hospitals</p><p>erpret such policies may influence ambulance diversion rates.adjust for potential hospital-level confounding of ambulanceersion that may be unrelated to ED crowding, we definediods of high ED crowding as days within the top quartile ofly ambulance diversion hours for a specific facility. We used aquartile definition because exploratory analyses suggested a</p><p>eshold effect occurring at that cutoff. All other days weresidered periods of normal ED crowding. This approach usesVolume , . : June </p></li><li><p>each hospital as its own control to define high levels of EDcrodivthecro</p><p>betimRaverrelmodaPla(upsyscotheHeIntdiacat</p><p>Pr</p><p>predifvarfortra</p><p>hoavaprecroclufixdiacoadsec</p><p>wistatrasecun</p><p>ressimwhcrodifcroBo</p><p>croconsamcon</p><p>assocconassmefixnocatamaltow</p><p>(ve</p><p>RE</p><p>Exhowhtrehtt99AdTaanninpmi0intfroweOncos</p><p>Tacomof</p><p>Sun et al Effect of Crowding on Patient Outcome</p><p>Vowding. Many facilities (44%) experienced ambulanceersion less than one quarter of the days in 2007; therefore,percentage of facility days categorized as those with high EDwding (17%) was less than 25% of all facility days.We collected data on admission-level characteristics that mayrelated to the outcomes, including age, sex, race or ethnicity,e indicators, comorbidities, and primary discharge diagnosis.</p><p>ce and ethnicity were dichotomized as white or non-Hispanicsus all others (nonwhite). Because ED crowding may beated to time of year,17 we included indicators for calendarnths and for weekend versus weekday corresponding to the</p><p>y of admission. We used Office of Statewide Hospitalnning and Development secondary discharge diagnoses codesto 24 coded per admission) and the Elixhauser classification</p><p>tem18 to identify the presence or absence of 30 comorbidnditions. Primary discharge diagnosis was categorized with</p><p>Clinical Classifications software developed by the Agency foralthcare Research and Quality.19 The software cross-maps allernational Classification of Diseases, Ninth Revision dischargegnosis codes to approximately 200 clinically coherentegories.</p><p>imary Data AnalysisIn descriptive analyses of hospital admissions stratified bysence of high ED crowding, we assessed for baselineference with hospital-level fixed effects models for continuousiables and Cochran-Mantel-Haenzel test stratified by hospitalcategorical variables. Length of stay and costs were log</p><p>nsformed to correct for right-skewed data.All outcomes were modeled with hierarchic regressions, withspital admission as the unit of analysis (see Appendix E1,ilable online at http://www.annemergmed.com). The keydictor in our models was a binary indicator of high EDwding on the day of hospital admission. Admissions werestered within hospitals, and all models included a hospitaled effect (dummy variable for each hospital). Primarygnosis category was included as a random effect, and</p><p>morbidities were included as fixed effects. All models includedmission and hospital-level covariates described in the previoustion.In our primary analysis, inpatient mortality was modeled</p><p>th a logistic link function. Secondary outcomes of length ofy and costs were modeled as continuous variables after a lognsformation. To improve interpretability, results for theondary outcomes were back-transformed to the originalits.We generated population-level estimates of mortality andource use attributable to high ED crowding. We performed aulation comparing the observed data to a hypothetical stateen all admissions occurred on days without high EDwding. Using our hierarchic regression models, we imputedferences in outcomes as if all admissions on high EDwding days had occurred on normal ED crowding days.otstrap sampling (n100) stratified by facility and level oflume , . : June wding (high versus low) was implemented to estimate 95%fidence interval (CI) for each of the 3 outcomes. Randompling with equal probability and with replacement wasducted within strata.20</p><p>We performed 2 additional sensitivity analyses. First, weessed the effect of ED crowding on inpatient deathsurring in the first 3 days because the effect of ED crowdinginhospital mortality may attenuate over time. Second, weessed an alternative definition of ED crowding. Adapting athodology described by Guttmann et al,21 we used a hospitaled-effect model and assessed ED crowding by non-rmalized daily diversion hours. Days were divided into 3egories: 0 (reference), 0 to 5, and greater than 5 hours ofbulance diversion; in contrast to the primary analysis, thisernative ED crowding measure did not use hospitals as theirn internal control.All data management and analysis was performed with SASrsion 9.2; SAS Institute, Inc., Cary, NC).</p><p>SULTSThe Figure illustrates the construction of the study cohort.cluded hospitals were more likely to be located in single-spital, low-population-density counties and served patientso were more likely to be young, white, and poor than thoseated at included hospitals (Table E1, available online atp://www.annemergmed.com). Our study cohort included5,379 admissions occurring through the ED at 187 hospitals.mission and hospital level characteristics are presented inble 1 and Table E2 (available online at http://www.emergmed.com), respectively. There were complete data onatient mortality and length of stay, and there were minimalssing data on covariates used in this analysis (0.8% race;.1% sex; complete data on other covariates). A subset ofegrated health systems and county hospitals was exemptedm cost reporting; approximately 15% of visits (n150,611)re excluded from cost analysis because of missing cost data.</p><p>unadjusted analyses, inpatient mortality, length of stay, andts were higher on high ED crowding days (Table 1).Table 2 presents adjusted analyses. (Full model results are inbles E3 to E5, available online at http://www.annemergmed.</p><p>.) High ED crowding was associated with 5% greater oddsinpatient death (95% CI 2% to 8%), 0.8% longer hospital</p><p>Figure. Study flowchart.Annals of Emergen...</p></li></ul>


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