Introduction: Telehealth is commonly used to connect emergency department (ED) patients with specialists or resources required for their care. Its infrastructure requires substantial upfront and ongoing investment from an ED or hospital and may be more difficult to implement in lower-resourced settings. Our aim was to examine for an association between ED payer mix and receipt of telehealth services.

Methods: Using data from the National Emergency Department Inventory (NEDI)-USA 2016 survey, we categorized EDs based on receipt of telehealth services (yes/no). The NEDI-USA data for EDs in New York state was linked with data from state ED datasets (SEDD) and state inpatient data (SID) to determine EDs’ payer mix (percent self-pay or Medicaid). Other ED characteristics of interest were rural location, academic status, and annual ED visit volume. We compared EDs with and without telehealth receipt, and used a logistic regression model to examine the relationship between ED payer mix and telehealth receipt after accounting for other ED characteristics.

Results: Of the 162 New York EDs in the SEDD-SID dataset, 160 (99%) were linked to the NEDI-USA dataset and 133 of those responded (83%) to the survey. Telehealth receipt was reported by 48 EDs (36%, 95% confidence interval [CI], 28-44%). Emergency departments with and without telehealth receipt were similar (all P >0.40) with respect to rurality (6% vs 9%, respectively), academic status (13% vs 8%), and annual volume (median 36,728 vs 43,000). By contrast, median percent of Medicaid or self-pay patients was lower in telehealth EDs (36%) vs non-telehealth EDs (45%, P = 0.02). In adjusted analysis, increasing proportion of Medicaid and self-pay patients was associated with decreased odds of telehealth receipt (odds ratio 0.87 per 5% increase; 95% CI, 0.77-0.99). Rural location, academic status, and ED volume were not significantly associated with odds of ED telehealth receipt in the adjusted model.

Conclusion: Among EDs in the state of New York, increasing proportion of self-pay and Medicaid patients was associated with decreased odds of ED telehealth receipt, even after accounting for rural location, academic status, and ED volume. The findings support the need for additional infrastructural investment in EDs serving a greater proportion of disadvantaged patients to ensure equitable access.

Introduction: Delays in identification and treatment of acute stroke contribute to significant morbidity and mortality. Multiple clinical factors have been associated with delays in acute stroke care. We aimed to determine the relationship between emergency department (ED) crowding and the delivery of timely emergency stroke care.

Methods: We used prospectively collected data from our institutional Get with the Guidelines-Stroke registry to identify consecutive acute ischemic stroke patients presenting to our urban academic ED from July 2016–August 2018. We used capacity logs to determine the degree of ED crowding at the time of patients’ presentation and classified them as ordinal variables (normal, high, and severe capacity constraints). Outcomes of interest were door-to-imaging time (DIT) among patients potentially eligible for alteplase or endovascular therapy on presentation, door-to-needle time (DTN) for alteplase delivery, and door-to-groin puncture (DTP) times for endovascular therapy. Bivariate comparisons were made using t-tests, chi-square, and Wilcoxon rank-sum tests as appropriate. We used regression models to examine the relationship after accounting for patient demographics, transfer status, arrival mode, and initial stroke severity by the National Institutes of Health Stroke Scale.

Results: Of the 1379 patients with ischemic stroke presenting during the study period, 1081 (78%) presented at times of normal capacity, 203 (15%) during high ED crowding, and 94 (7%) during severe crowding. Median DIT was 26 minutes (interquartile range [IQR] 17-52); DTN time was 43 minutes (IQR 31-59); and median DTP was 58.5 minutes (IQR 56.5-100). Treatment times were not significantly different during periods of higher ED utilization in bivariate or in multivariable testing.

Conclusion: In our single institution analysis, we found no significant delays in stroke care delivery associated with increased ED crowding. This finding suggests that robust processes of care may enable continued high-quality acute care delivery, even during times with an increased capacity burden.

Introduction: Prevention quality indicators (PQI) are a set of measures used to characterize healthcare utilization for conditions identified as being potentially preventable with high quality ambulatory care. These indicators have recently been adapted for emergency department (ED) patient presentations. In this study the authors sought to identify opportunities to potentially prevent emergency conditions and to strengthen systems of ambulatory care by analyzing patterns of ED utilization for PQI conditions.

Methods: Using multivariable logistic regression, the authors analyzed the relationship of patient demographics and neighborhood-level socioeconomic indicators with ED utilization for PQI conditions based on ED visits at an urban, academic medical center in 2017. We also used multilevel modeling to assess the contribution of these variables to neighborhood-level variation in the likelihood of an ED visit for a PQI condition.

Results: Of the included 98,522 visits, 17.5% were categorized as potentially preventable based on the ED PQI definition. On multivariate analysis, age < 18 years, Black race, and Medicare insurance had the strongest positive associations with PQI visits, with adjusted odds ratios (aOR) of 1.41 (95% confidence interval [CI], 1.29, 1.56), 1.40 (95% CI, 1.22, 1.61), and 1.40 (95% CI, 1.28, 1.54), respectively. All included neighborhood-level socioeconomic variables were significantly associated with PQI visit likelihood on univariable analysis; however; only level of education attainment and private car ownership remained significantly associated in the multivariable model, with aOR of 1.13 (95% CI, 1.10, 1.17) and 0.96 (95% CI, 0.93, 0.99) per quartile increase, respectively. This multilevel model demonstrated significant variation in PQI visit likelihood attributable to neighborhood, with interclass correlation decreasing from 5.92% (95% CI, 5.20, 6.73) in our unadjusted model to 4.12% (95% CI, 3.47, 4.87) in our fully adjusted model and median OR similarly decreasing from 1.54 to 1.43.

Conclusion: Demographic and local socioeconomic factors were significantly associated with ED utilization for PQI conditions. Future public health efforts can bolster efforts to target underlying social drivers of health and support access to primary care for patients who are Black, Latino, pediatric, or Medicare-dependent to potentially prevent emergency conditions (and the need for emergency care). Further research is needed to explore other factors beyond demographics and socioeconomic characteristics driving spatial variation in ED PQI visit likelihood.

Introduction: Telemedicine connects emergency departments (ED) with resources necessaryfor patient care; its use has not been characterized nationally, or even regionally. Our primaryobjective was to describe the prevalence of telemedicine use in New England EDs and theclinical applications of use. Secondarily, we aimed to determine if telemedicine use wasassociated with consultant availability and to identify ED characteristics associated withtelemedicine use.

Methods: We analyzed data from the National Emergency Department Inventory-New Englandsurvey, which assessed basic ED characteristics in 2014. The survey queried directors of everyED (n=195) in the six New England states (excluding federal hospitals and college infirmaries).Descriptive statistics characterized ED telemedicine use; multivariable logistic regressionidentified independent predictors of use.

Results: Of the 169 responding EDs (87% response rate), 82 (49%) reported usingtelemedicine. Telemedicine EDs were more likely to be rural (18% of users vs. 7% of nonusers,p=0.03); less likely to be academic (1% of users vs. 11% of non-users, p=0.01); andless likely to have 24/7 access to neurology (p<0.001), neurosurgery (p<0.001), orthopedics(p=0.01), plastic surgery (p=0.01), psychiatry (p<0.001), and hand surgery (p<0.001)consultants. Neuro/stroke (68%), pediatrics (11%), psychiatry (11%), and trauma (10%) were themost commonly reported applications. On multivariable analysis, telemedicine was more likely inrural EDs (odds ratio [OR] 4.39, 95% confidence interval [CI] 1. 30-14.86), and less likely in EDswith 24/7 neurologist availability (OR 0.21, 95% CI [0.09-0.49] ), and annual volume <20,000 (OR0.24, 95% CI [0.08-0.68]).

Conclusion: Telemedicine is commonly used in New England EDs. In 2014, use was morecommon among rural EDs and EDs with limited neurology consultant availability. In contrast,telemedicine use was less common among very low-volume EDs.

Introduction: The American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results.

Methods: Using the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A “group match” was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate.

Results: Of the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a “group match” had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs.

Conclusion: Excluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

Background and purpose

Methods

Results

Conclusions

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has been shown to increase levels of psychological distress among healthcare workers. Little is known, however, about specific positive and negative individual and organizational factors that affect the mental health of emergency physicians (EP) during COVID-19. Our objective was to assess these factors in a broad geographic sample of EPs in the United States. 

Methods: We conducted an electronic, prospective, cross-sectional national survey of EPs from October 6–December 29, 2020. Measures assessed negative mental health outcomes (depression, anxiety, post-traumatic stress, and insomnia), positive work-related outcomes, and strategies used to cope with COVID-19. After preliminary analyses and internal reliability testing, we performed four separate three-stage hierarchical multiple regression analyses to examine individual and organizational predictive factors for psychological distress. 

Results: Response rate was 50%, with 517 EPs completing the survey from 11 different sites. Overall, 85% of respondents reported negative psychological effects due to COVID-19. Participants reported feeling more stressed (31%), lonelier (26%), more anxious (25%), more irritable (24%) and sadder (17.5%). Prevalence of mental health conditions was 17% for depression, 13% for anxiety, 7.5% for post-traumatic stress disorder (PTSD), and 18% for insomnia. Regular exercise decreased from 69% to 56%, while daily alcohol use increased from 8% to 15%. Coping strategies of behavioral disengagement, self-blame, and venting were significant predictors of psychological distress, while humor and positive reframing were negatively associated with psychological distress. 

Conclusion: Emergency physicians have experienced high levels of psychological distress during the COVID-19 pandemic. Those using avoidant coping strategies were most likely to experience depression, anxiety, insomnia, and PTSD, while humor and positive reframing were effective coping strategies.