The Impact of Virtual Care in an Emergency Department Observation Unit.

STUDY OBJECTIVE(S): We report the impact of telemedicine virtual rounding in emergency department observation units (EDOU) on the effectiveness, safety, and cost relative to traditional observation care. METHODS: In this retrospective diff-in-diff study, we compared observation visit outcomes from 2 EDOUs before (pre) and after (post) full adoption of telemedicine rounding tele-observation (tele-obs) with usual care in control EDOU and care in a hospital bed in an integrated health system without tele-obs. Tele-obs physicians did not work at the control hospital. Outcomes were the length of stay, total direct costs, admission status, and adverse events (ICU and death). Difference-in-differences modeling evaluated outcomes with covariates including age, sex, payer type, and clinical classification software diagnostic category. Data from a system data warehouse and a cost accounting database were used. RESULTS: Of the 20,861 EDOU visits, 15,630 (74.9%) were seen in the preperiod and 6,657 (31.9%) in control EDOU. Of 23,055 non-EDOU inpatient visits assigned to observation status (nonobservation unit), 76% were seen in the preperiod. Adjusted length of stay was not significantly different for tele-obs and control EDOUs (26.4 hours versus 23.5 hours), which remained lower than in hospital settings (37.9 hours). The pre-post diff-in-diff was not significant (P=.78). Inpatient admission status was similar for tele-obs and control EDOUs (20.9% versus 22.4.%) and lower than in hospital settings (30.3%). Prepost odds ratios for inpatient admission and adverse outcomes did not change significantly for all study groups. Adjusted costs increased over time for all settings; however, the prepost median cost change was not significantly different between tele-obs EDOUs and control EDOUs ($162.5 versus $235) and was lower than the change for control hospital settings ($783). Median tele-obs EDOU cost over both periods ($1,541) remained significantly lower than hospital costs ($2,413). CONCLUSION: Using tele-obs to manage observation patients in an ED observation unit was not associated with significant differences in length of stay, admission status, measured adverse events, or total direct cost.

The impact of emergency department observation units on a health system.

IMPORTANCE: Protocol driven ED observation units (EDOU) have been shown to improve outcomes for patients and payers, however their impact on an entire health system is unknown. Two thirds of US hospitals do not have such units. OBJECTIVE: To determine the impact of a protocol-driven EDOU on health system length of stay, cost, and resource utilization. METHODS: A retrospective, observational, cross-sectional study of observation patients managed over 25 consecutive months in a four-hospital academic health system. Patients were identified using the "admit to observation" order and limited to adult, emergent / urgent, non-obstetric patients. Data was retrieved from a cost accounting database. The primary study exposure was the setting for observation care which was broken into three discrete groups: EDOUs (n = 3), hospital medicine observation units (HMSOU, n = 2), and a non-observation unit (NOU) bed located anywhere in the hospital. Outcomes included observation-to-inpatient admission rate, length of stay (LoS), total direct cost, and inpatient bed days saved. Unadjusted outcomes were compared, and outcomes were adjusted using multiple study variables. LoS and cost were compared using quantile regressions. Inpatient admit rate was compared using logistic regressions. RESULTS: The sample consisted of 48,145 patients who were 57.4% female, 48% Black, 46% White, median age of 58, with some variation in most common diagnoses and payer groups. The median unadjusted outcomes favored EDOU over NOU settings for admission rate (13.1% vs 37.1%), LoS [17.9 vs 35.6 h), and cost ($1279 vs $2022). The adjusted outcomes favored EDOU over NOU settings for admission rates [12.3% (95% CI 9.7-15.3) vs 26.4% (CI 21.3-32.3)], LoS differences [11.1 h (CI 10.6-11.5 h)] and cost differences [$127.5 (CI $105.4 - $149.5)]. Adjusted differences were similar and favored EDOU over HMSOU settings. For the health system, the total adjusted annualized savings of the EDOUs was 10,399 bed days and $1,329,443 in total direct cost per year. CONCLUSION: Within an academic medical center, EDOUs were associated with improved resource utilization and reduced cost. This represents a significant opportunity for hospitals to improve efficiency and contain costs.

Incorporating Alternative Care Site Characteristics Into Estimates of Substitutable ED Visits.

BACKGROUND: Several recent efforts to improve health care value have focused on reducing emergency department (ED) visits that potentially could be treated in alternative care sites (ie, primary care offices, retail clinics, and urgent care centers). Estimates of the number of these visits may depend on assumptions regarding the operating hours and functional capabilities of alternative care sites. However, methods to account for the variability in these characteristics have not been developed. OBJECTIVE: To develop methods to incorporate the variability in alternative care site characteristics into estimates of ED visit "substitutability." RESEARCH DESIGN, SUBJECTS, AND MEASURES: Our approach uses the range of hours and capabilities among alternative care sites to estimate lower and upper bounds of ED visit substitutability. We constructed "basic" and "extended" criteria that captured the plausible degree of variation in each site's hours and capabilities. To illustrate our approach, we analyzed data from 22,697 ED visits by adults in the 2011 National Hospital Ambulatory Medical Care Survey, defining a visit as substitutable if it was treat-and-release and met both the operating hours and functional capabilities criteria. RESULTS: Use of the combined basic hours/basic capabilities criteria and extended hours/extended capabilities generated lower and upper bounds of estimates. Our criteria classified 5.5%-27.1%, 7.6%-20.4%, and 10.6%-46.0% of visits as substitutable in primary care offices, retail clinics, and urgent care centers, respectively. CONCLUSIONS: Alternative care sites vary widely in operating hours and functional capabilities. Methods such as ours may help incorporate this variability into estimates of ED visit substitutability.

Emergency department resource use by supervised residents vs attending physicians alone.

IMPORTANCE: Few studies have evaluated the common assumption that graduate medical education is associated with increased resource use. OBJECTIVE: To compare resources used in supervised vs attending-only visits in a nationally representative sample of patient visits to US emergency departments (EDs). DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional study of the National Hospital Ambulatory Medical Care Survey (2010), a probability sample of US EDs and ED visits. EXPOSURES: Supervised visits, defined as visits involving both resident and attending physicians. Three ED teaching types were defined by the proportion of sampled visits that were supervised visits: nonteaching ED, minor teaching ED (half or fewer supervised visits), and major teaching ED (more than half supervised visits). MAIN OUTCOMES AND MEASURES: Association of supervised visits with hospital admission, advanced imaging (computed tomography, ultrasound, or magnetic resonance imaging), any blood test, and ED length of stay, adjusted for visit acuity, demographic characteristics, payer type, and geographic region. RESULTS: Of 29,182 ED visits to the 336 nonpediatric EDs in the sample, 3374 visits were supervised visits. Compared with the 25,808 attending-only visits, supervised visits were significantly associated with more frequent hospital admission (21% vs 14%; adjusted odds ratio [aOR], 1.42; 95% CI, 1.09-1.85), advanced imaging (28% vs 21%; aOR, 1.27; 95% CI, 1.06-1.51), and a longer median ED stay (226 vs 153 minutes; adjusted geometric mean ratio, 1.32; 95% CI, 1.19-1.45), but not with blood testing (53% vs 45%; aOR, 1.18; 95% CI, 0.96-1.46). Of visits to the sample of 121 minor teaching EDs, a weighted estimate of 9% were supervised visits, compared with 82% of visits to the 34 major teaching EDs. Supervised visits in major teaching EDs compared with attending-only visits were not associated with hospital admission (aOR, 1.15; 95% CI, 0.83-1.58), advanced imaging (aOR, 1.21; 95% CI, 0.96-1.53), or any blood test (aOR, 1.02; 95% CI, 0.79-1.33), but had longer ED stays (adjusted geometric mean ratio, 1.32; 95% CI, 1.14-1.53). CONCLUSIONS AND RELEVANCE: In a sample of US EDs, supervised visits were associated with a greater likelihood of hospital admission and use of advanced imaging and with longer ED stays. Whether these associations are different in EDs in which more than half of visits are seen by residents requires further investigation.

The Impact of "Emergency-only" Hemodialysis on Hospital Cost and Resource Utilization.

INTRODUCTION: Undocumented immigrants are excluded from benefits that help compensate for scheduled outpatient hemodialysis (HD), compelling them to use emergency departments (ED) for HD. Consequently, these patients can receive "emergency-only" HD after presenting to the ED with critical illness due to untimely dialysis. Our objective was to describe the impact of emergency-only HD on hospital cost and resource utilization in a large academic health system that includes public and private hospitals. METHODS: This retrospective observational study of health and accounting records took place at five teaching hospitals (one public, four private) over 24 consecutive months from January 2019 to December 2020. All patients had emergency and/or observation visits, renal failure codes (International Classification of Diseases, 10th Rev, Clinical Modification), emergency HD procedure codes, and an insurance status of "self-pay." Primary outcomes included frequency of visits, total cost, and length of stay (LOS) in the observation unit. Secondary objectives included evaluating the variation in resource use between persons and comparing these metrics between the private and public hospitals. RESULTS: A total of 15,682 emergency-only HD visits were made by 214 unique persons, for an average of 36.6 visits per person per year. The average cost per visit was $1,363, for an annual total cost of $10.7 million. The average LOS was 11.4 hours. This resulted in 89,027 observation-hours annually, or 3,709 observation-days. The public hospital dialyzed more patients compared to the private hospitals, especially due to repeat visits by the same persons. CONCLUSION: Health policies that limit hemodialysis of uninsured patients to the ED are associated with high healthcare costs and a misuse of limited ED and hospital resources.

Emergency department volume and racial and ethnic differences in waiting times in the United States.

BACKGROUND: Racial and ethnic differences in emergency department (ED) waiting times have been observed previously. OBJECTIVES: We explored how adjusting for ED attributes, particularly visit volume, affected racial/ethnic differences in waiting time. RESEARCH DESIGN: We constructed linear models using generalized estimating equations with 2007-2008 National Hospital Ambulatory Medical Care Survey data. SUBJECTS: We analyzed data from 54,819 visits to 431 US EDs. MEASURES: Our dependent variable was waiting time, measured from arrival to time seen by physician, and was log transformed because it was skewed. Primary independent variables were individual race/ethnicity (Hispanic and non-Hispanic white, black, other) and ED race/ethnicity composition (covariates for percentages of Hispanics, blacks, and others). Covariates included patient age, triage assessment, arrival by ambulance, payment source, volume, region, and teaching hospital. RESULTS: Geometric mean waiting times were 27.3, 37.7, and 32.7 minutes for visits by white, black, and Hispanic patients. Patients waited significantly longer at EDs serving higher percentages of black patients; per 25 point increase in percent black patients served, waiting times increased by 23% (unadjusted) and 13% (adjusted). Within EDs, black patients waited 9% (unadjusted) and 4% (adjusted) longer than whites. The ED attribute most strongly associated with waiting times was visit volume. Waiting times were about half as long at low-volume compared with high-volume EDs (P<0.001). For Hispanic patients, differences were smaller and less robust to model choice. CONCLUSIONS: Non-Hispanic black patients wait longer for ED care than whites primarily because of where they receive that care. ED volume may explain some across-ED differences.

National trends in emergency department occupancy, 2001 to 2008: effect of inpatient admissions versus emergency department practice intensity.

STUDY OBJECTIVE: We evaluate recent trends in emergency department (ED) crowding and its potential causes by analyzing ED occupancy, a proxy measure for ED crowding. METHODS: We analyzed data from the annual National Hospital Ambulatory Medical Care Surveys from 2001 to 2008. The surveys abstract patient records from a national sample of hospital EDs to generate nationally representative estimates of visits. We used time of ED arrival and length of ED visit to calculate mean and hourly ED occupancy. RESULTS: During the 8-year study period, the number of ED visits increased by 1.9% per year (95% confidence interval 1.2% to 2.5%), a rate 60% faster than population growth. Mean occupancy increased even more rapidly, at 3.1% per year (95% confidence interval 2.3% to 3.8%), or 27% during the 8 study years. Among potential factors associated with crowding, the use of advanced imaging increased most, by 140%. But advanced imaging had a smaller effect on the occupancy trend than other more common throughput factors, such as the use of intravenous fluids and blood tests, the performance of any clinical procedure, and the mention of 2 or more medications. Of patient characteristics, Medicare payer status and the age group 45 to 64 years accounted for small disproportionate increases in occupancy. CONCLUSION: Despite repeated calls for action, ED crowding is getting worse. Sociodemographic changes account for some of the increase, but practice intensity is the principal factor driving increasing occupancy levels. Although hospital admission generated longer ED stays than any other factor, it did not influence the steep trend in occupancy.

Factors associated with use of urban emergency departments by the U.S. homeless population.

OBJECTIVE: Homeless individuals frequently use emergency departments (EDs), but previous studies have investigated local rather than national ED utilization rates. This study sought to characterize homeless people who visited urban EDs across the U.S. METHODS: We analyzed the ED subset of the National Hospital Ambulatory Medical Care Survey (NHAMCS-ED), a nationally representative probability survey of ED visits, using methods appropriate for complex survey samples to compare demographic and clinical characteristics of visits by homeless vs. non-homeless people for survey years 2005 and 2006. RESULTS: Homeless individuals from all age groups made 550,000 ED visits annually (95% confidence interval [CI] 419,000, 682,000), or 72 visits per 100 homeless people in the U.S. per year. Homeless people were older than others who used EDs (mean age of homeless people = 44 years compared with 36 years for others). ED visits by homeless people were independently associated with male gender, Medicaid coverage and lack of insurance, and Western geographic region. Additionally, homeless ED visitors were more likely to have arrived by ambulance, to be seen by a resident or intern, and to be diagnosed with either a psychiatric or substance abuse problem. Compared with others, ED visits by homeless people were four times more likely to occur within three days of a prior ED evaluation, and more than twice as likely to occur within a week of hospitalization. CONCLUSIONS: Homeless people who seek care in urban EDs come by ambulance, lack medical insurance, and have psychiatric and substance abuse diagnoses more often than non-homeless people. The high incidence of repeat ED visits and frequent hospital use identifies a pressing need for policy remedies.

A Method for Grouping Emergency Department Visits by Severity and Complexity.

INTRODUCTION: Triage functions to quickly prioritize care and sort patients by anticipated resource needs. Despite widespread use of the Emergency Severity Index (ESI), there is still no universal standard for emergency department (ED) triage. Thus, it can be difficult to objectively assess national trends in ED acuity and resource requirements. We sought to derive an ESI from National Hospital Ambulatory Medical Care Survey (NHAMCS) survey items (NHAMCS-ESI) and to assess the performance of this index with respect to stratifying outcomes, including hospital admission, waiting times, and ED length of stay (LOS). METHODS: We used data from the 2010-2015 NHAMCS, to create a measure of ED visit complexity based on variables within NHAMCS. We used NHAMCS data on chief complaint, vitals, resources used, interventions, and pain level to group ED visits into five levels of acuity using a stepwise algorithm that mirrored ESI. In addition, we examined associations of NHAMCS-ESI with typical indicators of acuity such as waiting time, LOS, and disposition. The NHAMCS-ESI categorization was also compared against the "immediacy" variable across all of these outcomes. Visit counts used weighted scores to estimate national levels of ED visits. RESULTS: The NHAMCS ED visits represent an estimated 805,726,000 ED visits over this time period. NHAMCS-ESI categorized visits somewhat evenly, with most visits (42.5%) categorized as a level 3. The categorization pattern is distinct from that of the "immediacy" variable within NHAMCS. Of admitted patients, 89% were categorized as NHAMCS-ESI level 2-3. Median ED waiting times increased as NHAMCS-ESI levels decreased in acuity (from approximately 14 minutes to 25 minutes). Median LOS decreased as NHAMCS-ESI decreased from almost 200 minutes for level 1 patients to nearly 80 minutes for level 5 patients. CONCLUSION: We derived an objective tool to measure an ED visit's complexity and resource use. This tool can be validated and used to compare complexity of ED visits across hospitals and regions, and over time.

Primary Care Office Visits For Acute Care Dropped Sharply In 2002-15, While ED Visits Increased Modestly.

The traditional model of primary care practices as the main provider of care for acute illnesses is rapidly changing. Over the past two decades the growth in emergency department (ED) visits has spurred efforts to reduce "inappropriate" ED use. We examined a nationally representative sample of office and ED visits in the period 2002-15. We found a 12 percent increase in ED use (from 385 to 430 visits per 1,000 population), which was dwarfed by a decrease of nearly one-third in the rate of acute care visits to primary care practices (from 938 to 637 visits per 1,000 population). The decrease in primary care acute visits was also present among two vulnerable populations: Medicaid beneficiaries and adults ages sixty-five and older, either in Medicare or privately insured. As acute care delivery shifts away from primary care practices, there is a growing need for integration and coordination across an increasingly diverse spectrum of venues where patients seek care for acute illnesses.

Racial and Ethnic Differences in Diagnostic Imaging Utilization During Adult Emergency Department Visits in the United States, 2005 to 2014.

OBJECTIVE: To compare the use of medical imaging (x-ray [XR], CT, ultrasound, and MRI) in the emergency department (ED) for adult patients of different racial and ethnic groups in the United States from 2005 to 2014. METHODS: We performed a multilevel stratified regression analysis of the National Hospital Ambulatory Medical Care Survey ED Subfile, a nationally representative database of hospital-based ED visits. We examined race (white, black, Asian, other) and ethnicity (Hispanic versus non-Hispanic) as the primary exposures for the outcomes of ED medical imaging use (XR, CT, ultrasound, MRI, and any imaging). We controlled for other potential patient-level and facility-level determinants of ED imaging use. RESULTS: Approximately half (48.8%) of the 225,037 adult patient ED visits underwent imaging; 36.1% underwent XR, 16.4% CT, 4.1% ultrasound, and 0.8% MRI. White patients received imaging during 51.3% of their encounters, black patients received imaging during 43.6% of their encounters, Asians received imaging during 50.8% of their encounters, and other races received imaging during 46% of their encounters. As compared with white patients, black patients had decreased adjusted odds of receiving imaging in the ED (odds ratio [OR] = 0.86, 95% confidence interval [CI]: 0.84-0.89). Comparatively, black patients had a lower odds of CT scan (OR = 0.80, 95% CI: 0.77-0.83) or MRI (OR = 0.74, 95% CI: 0.65-0.85). Hispanic patients and Asian patients had a higher odds of receiving ultrasound (OR = 1.36, 95% CI: 1.27-1.44 and OR = 1.25, 95% CI: 1.10-1.42), respectively. IMPLICATIONS: We observed significant racial and ethnic differences in medical imaging use in the ED even after controlling for patient- and facility-level factors.

Advanced diagnostic imaging utilization during emergency department visits in the United States: A predictive modeling study for emergency department triage.

BACKGROUND: Emergency department (ED) crowding is associated with negative health outcomes, patient dissatisfaction, and longer length of stay (LOS). The addition of advanced diagnostic imaging (ADI), namely CT, ultrasound (U/S), and MRI to ED encounter work up is a predictor of longer length of stay. Earlier and improved prediction of patients' need for advanced imaging may improve overall ED efficiency. The aim of the study was to detect the association between ADI utilization and the structured and unstructured information immediately available during ED triage, and to develop and validate models to predict utilization of ADI during an ED encounter. METHODS: We used the United States National Hospital Ambulatory Medical Care Survey data from 2009 to 2014 to examine which sociodemographic and clinical factors immediately available at ED triage were associated with the utilization of CT, U/S, MRI, and multiple ADI during a patient's ED stay. We used natural language processing (NLP) topic modeling to incorporate free-text reason for visit data available at time of ED triage in addition to other structured patient data to predict the use of ADI using multivariable logistic regression models. RESULTS: Among the 139,150 adult ED visits from a national probability sample of hospitals across the U.S, 21.9% resulted in ADI use, including 16.8% who had a CT, 3.6% who had an ultrasound, 0.4% who had an MRI, and 1.2% of the population who had multiple types of ADI. The c-statistic of the predictive models was greater than or equal to 0.78 for all imaging outcomes, and the addition of text-based reason for visit information improved the accuracy of all predictive models. CONCLUSIONS: Patient information immediately available during ED triage can accurately predict the eventual use of advanced diagnostic imaging during an ED visit. Such models have the potential to be incorporated into the ED triage workflow in order to more rapidly identify patients who may require advanced imaging during their ED stay and assist with medical decision-making.

Homelessness and Emergency Medicine: A Review of the Literature.

OBJECTIVES: We aimed to synthesize the available evidence on the demographics, prevalence, clinical characteristics, and evidence-based management of homeless persons in the emergency department (ED). Where appropriate, we highlight knowledge gaps and suggest directions for future research. METHODS: We conducted a systematic literature search following databases: PubMed, Ovid, and Google Scholar for articles published between January 1, 1990, and December 31, 2016. We supplemented this search by cross-referencing bibliographies of the retrieved publications. Peer-reviewed studies written in English and conducted in the United States that examined homelessness within the ED setting were included. We used a qualitative approach to synthesize the existing literature. RESULTS: Twenty-eight studies were identified that met the inclusion criteria. Based on our study objectives and the available literature, we grouped articles examining homeless populations in the ED into four broad categories: 1) prevalence and sociodemographic characteristics of homeless ED visits, 2) ED utilization by homeless adults, 3) clinical characteristics of homeless ED visits, and 4) medical education and evidence-based management of homeless ED patients. CONCLUSION: Homelessness may be underrecognized in the ED setting. Homeless ED patients have distinct care needs and patterns of ED utilization that are unmet by the current disease-oriented and episodic models of emergency medicine. More research is needed to determine the prevalence and characteristics of homelessness in the ED and to develop evidence-based treatment strategies in caring for this vulnerable population.

A "Patch" to the NYU Emergency Department Visit Algorithm.

OBJECTIVE: To document erosion in the New York University Emergency Department (ED) visit algorithm's capability to classify ED visits and to provide a "patch" to the algorithm. DATA SOURCES: The Nationwide Emergency Department Sample. STUDY DESIGN: We used bivariate models to assess whether the percentage of visits unclassifiable by the algorithm increased due to annual changes to ICD-9 diagnosis codes. We updated the algorithm with ICD-9 and ICD-10 codes added since 2001. PRINCIPAL FINDINGS: The percentage of unclassifiable visits increased from 11.2 percent in 2006 to 15.5 percent in 2012 (p < .01), because of new diagnosis codes. Our update improves the classification rate by 43 percent in 2012 (p < .01). CONCLUSIONS: Our patch significantly improves the precision and usefulness of the most commonly used ED visit classification system in health services research.

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

OBJECTIVE: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. METHODS: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient's reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model. RESULTS: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN. CONCLUSIONS: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient's reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.