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The prehospital phase is a critical component of delivering high-quality acute stroke care. This topical review discusses the current state of prehospital acute stroke screening and transport, as well as new and emerging advances in prehospital diagnosis and treatment of acute stroke. Topics include prehospital stroke screening, stroke severity screening, emerging technologies to aid in the identification and diagnosis of acute stroke in the prehospital setting, prenotification of receiving emergency departments, decision support for destination determination, and the capabilities and opportunities for prehospital stroke treatment in mobile stroke units. Further evidence-based guideline development and implementation of new technologies are critical for ongoing improvements in prehospital stroke care.

As a primary access point for crisis psychiatric care, the emergency department (ED) is uniquely positioned to improve the quality of care and outcomes for patients with psychiatric emergencies. Quality measurement is the first key step in understanding the gaps and variations in emergency psychiatric care to guide quality improvement initiatives. Our objective was to develop a quality measurement framework informed by a comprehensive review and gap analysis of quality measures for ED psychiatric care. We conducted a systematic literature review and convened an expert panel in emergency medicine, psychiatry, and quality improvement to consider if and how existing quality measures evaluate the delivery of emergency psychiatric care in the ED setting. The expert panel reviewed 48 measures, of which 5 were standardized, and 3 had active National Quality Forum endorsement. Drawing from the measure appraisal, we developed a quality measurement framework with specific structural, process, and outcome measures across the ED care continuum. This framework can help shape an emergency medicine roadmap for future clinical quality improvement initiatives, research, and advocacy work designed to improve outcomes for patients presenting with psychiatric emergencies.

Acute stroke care begins before hospital arrival, and several prehospital factors are critical in influencing overall patient care and poststroke outcomes. This topical review provides an overview of the state of the science on prehospital components of stroke systems of care and how emergency medical services systems may interact in the system to support acute stroke care. Topics include layperson recognition of stroke, prehospital transport strategies, networked stroke care, systems for data integration and real-time feedback, and inequities that exist within and among systems.

Reperfusion is critical for improving outcomes of patients experiencing ischemic stroke. Increasing evidence supports tenecteplase as an alternative to alteplase, with many potential benefits. This article discusses the current state of the evidence for intravenous reperfusion and considers strategies for improving access, including possible modifications to efficacy-related regulatory updates.

Arrival by emergency medical services (EMS) and prenotification among ischemic stroke patients are well-established to improve the timeliness and quality of stroke care, yet the association of prenotification with in-hospital mortality has not been previously described. Our cross-sectional study aimed to assess the association between EMS prenotification and in-hospital mortality for patients with acute ischemic stroke or transient ischemic attack.We analyzed data from the Massachusetts Paul Coverdell National Acute Stroke Program registry. Our study population included adult patients presenting by EMS with transient ischemic attack or acute ischemic stroke from non-health care settings between 2016 and 2020. We excluded patients who were comfort measures only on arrival or day after arrival. We used generalized estimating equations to assess the association between prenotification and in-hospital stroke mortality.In the adjusted model, prenotification was associated with lower odds of in-hospital mortality (odds ratio [OR] 0.87, 95% confidence interval [CI] 0.76-0.98). Other variables associated with in-hospital mortality were longer door-to-imaging interval (OR 1.03, 95% CI 1.03-1.04) and year of presentation (OR 0.91 for each year, 95% CI 0.88-0.93). Odds of in-hospital mortality also varied by insurance, race, and ethnicity.Prenotification by EMS was associated with reduced in-hospital mortality for patients with ischemic stroke and transient ischemic attack. These findings add to the large body of literature demonstrating the key role of EMS in the stroke systems of care. Our study underscores the importance of standardizing prehospital screening and triage, increasing rates of prenotification via feedback and education, and encouraging active collaborations between prehospital personnel and stroke-capable hospitals to increase in-hospital survival among patients with stroke and transient ischemic attack.

In the Furthering Access to Stroke Telemedicine (FAST) Act, passed as part of a budget omnibus in 2018, Congress permanently expanded Medicare payment for telemedicine consultations for acute stroke ("telestroke") from delivery only in rural areas to delivery in both urban and rural areas, effective January 1, 2019. Using a controlled time-series analysis, we found that one year after FAST Act implementation, billing for Medicare telestroke increased substantially in emergency departments at both directly affected urban hospitals and indirectly affected rural hospitals. However, at that time only a minority of hospitals with known telestroke capacity had ever billed Medicare for that service, and there was substantial billing inconsistent with Medicare requirements. As Congress considers options for Medicare telemedicine payment after the COVID-19 pandemic, our findings, which are consistent with confusion among providers regarding telemedicine billing requirements, suggest that simplified payment rules would help ensure that expanded reimbursement achieves its intended impact.

Background: Telestroke has been increasingly adopted by hospitals with limited access to neurological expertise and is often followed by transfer for a higher level of care. We sought to characterize factors independently associated with patient transfers after telestroke consultation in our regional telestroke network. Methods: We studied all telestroke consults from our urban comprehensive stroke center from 1/1/2005 to 06/30/2022 using the records in our telehealth portal. We examined the annual proportion of transfers among all consults overall and stratified by 6 levels of NIHSS severity and 4 levels of spoke hospital rurality. We used generalized linear mixed models to evaluate the association between transfer and patient demographics, hospital characteristics, use of video and care quality metrics (timeliness and tPA use), accounting for clustering by spoke hospital. Results: We identified 20,618 consecutive telestroke consults, of which 5,276 (25.6%) were transferred (602 cases excluded with unknown transfer status). The proportion of transfers decreased significantly over the study period (Figure). Decreases were observed for all rurality strata and all but the most severe strokes. After adjusting for temporal trends, factors independently associated with transfer included younger age, male gender, more severe stroke, and tPA use (Table). Conclusion: In our regional telestroke network, the proportion of transfers decreased substantially from 2005 to 2022 overall and across almost all patient and hospital groups. Telestroke may facilitate capacity management and retaining care in lower cost community settings, which may increase healthcare value.

Introduction: Ischemic stroke (IS) patients are frequently transferred between hospitals. Our objective was to determine whether patient insurance status contributes to variation in access to stroke center care among transferred patients with IS. Methods: We compiled California data on every nonfederal hospital admission from 2010-17 and used ICD-9, ICD-10, and DRG codes to identify IS patients transferred from an initial emergency department to another hospital. Transfers were categorized based on whether or not the patient was ultimately discharged from a stroke center hospital (primary or comprehensive). Patient insurance status was categorized as private, Medicare, Medicaid or self/uninsured. Clusters of closely connected hospitals via transfer frequency were identified using network science community detection methods. Within each cluster, we examined the degree of disparity in stroke center access by quantifying the difference between the insurance groups with the highest and lowest proportion of transfers discharged from a stroke center. Results: We identified 10,049 IS transfers during the study period (private 5,297 [53%]; Medicare 3,328 [33%]; Medicaid 904 [9%]; self/uninsured 520 [5%]). Stroke center access varied by patient insurance (overall 87%, private 89%, Medicare 87%, Medicaid 82%, self 72%). There were 14 clusters of closely connected hospitals via transfers. In the highest performing cluster, 100% of transferred patients in each insurance category were discharged from a stroke center (delta 0). The lowest performing cluster was also the largest (n=2,364 transfers); in this cluster 69% of transfers were discharged from a stroke center, ranging from 32% of self-pay transfers to 81% of privately insured transfers (highest delta among all clusters: 49%). Conclusions: These findings demonstrate that current care patterns differ by insurance status. Further research is needed to determine interventions to address this disparity.

Introduction: Many stroke patients initially present to non-stroke center hospitals. However, the delivery of Emergency Department (ED)-based acute stroke care at smaller, non-academic (i.e., community) EDs is less well-described than for larger, academic hospitals. Hypothesis: There is wide variation in community EDs’ performance on acute stroke care delivery measures. Methods: This is a retrospective analysis of a cohort of stroke patients from EDs participating in the Emergency Quality Network (E-QUAL) stroke collaborative, a national stroke quality improvement project targeted to community EDs. Sites used ICD10 codes to identify ischemic stroke patients and submitted data using a web-based submission portal. EDs with data from at least 20 patients were included. For each site, we calculated median door-to-imaging (DTI), door-to-needle (DTN), and door-in-door-out (DIDO) times among transferred patients and ED length of stay (LOS) among admitted patients. We also determined the proportions of eligible patients arriving with 3.5 hours of last known well who received thrombolysis within 4.5 hours, of patients with documentation of severity assessment performance and of dysphagia screening. We used descriptive statistics to illustrate variation. Results: Of the 54 participating EDs, data were available for 45, and 28 included ≥ 20 patients. Of included EDs, median annual ED volume was 34,648 (IQR 21,250-47,120) and 40% were rural. Performance varied on DTI, DIDO among transferred patients, and LOS among admitted patients (Table). Performance was more consistent on documentation of severity assessment and dysphagia screening. Conclusions: Performance on stroke care delivery measures varied between these community EDs and data in the literature from larger hospitals that typically participate in national registries. Future efforts to improve emergency stroke care delivery should consider unique factors impacting care at smaller, community EDs.

Study Objective: Though select inpatient-based performance measures exist for the care of patients with nontraumatic intracranial hemorrhage (ICH), Emergency Departments (EDs) lack measurement instruments designed to support and improve care processes in the hyperacute phase. To address this, we propose a set of measures applying a syndromic (rather than diagnosis-based) approach informed by performance data from a national sample of community EDs participating in the Emergency Quality Network Stroke Initiative (E-QUAL). Methods: We convened a workgroup of experts in acute neurologic emergencies. The group considered the appropriate use case for each proposed measure (internal quality improvement [QI], benchmarking, or accountability) and examined data from E-QUAL-participating EDs to consider the validity and feasibility of proposed measures. The initially conceived set included 14 measure concepts of which 7 were selected for inclusion in the measure set after review of data and further deliberation. To inform the process, we collected data from E-QUAL participating EDs. EDs used a web-based portal to submit data from chart reviews on patients with ICH during 2020-2021. Descriptive statistics characterize performance and identify variation. Results: Proposed measures include 2 for QI, benchmarking, and accountability, 3 for QI and benchmarking and 2 for QI only (Table). Of the E-QUAL participating EDs, 35 had sufficient case volume (≥25) and were included for review of preliminary performance data. Median annual ED volume was 44,000 (IQR 31,337-67,000) and 16 (48%) reported typically transferring patients with ICH. The Table includes available performance data. Conclusion: Application of these measures may identify opportunities for improvement and focus QI resources on evidence-based targets. The proposed measures warrant further development and validation to support broader implementation and advance national healthcare quality goals.

IntroductionPatients with acute ischaemic strokes (AIS), on average, fare better with timely neurologist consultation, and a growing proportion of them receive one. However, little is known about trends in the characteristics of neurologists who treat AIS.MethodsWe identified AIS and transient ischaemic attack (TIA) episodes with neurologist consults in fee-for-service Medicare from January 2008 to September 2021. For each episode, we determined whether the neurologist was a vascular neurologist, was a high-volume provider, whether the patient was transferred between hospitals and the distance between the patient’s home and physician’s practice.ResultsFrom 2008 to 2021, the share of AIS/TIA episodes (n=5 073 294) with neurologist consults increased (52.9% to 61.7%). Among episodes with consults, the fraction conducted by a vascular neurologist (5.2% to 13.7%) or by a high-volume neurologist (13.2% to 14.9%) also increased. The fraction with the patient’s home and neurologist greater than 100 miles apart (4.8% to 9.6%) or in different states (5.1% to 8.1%) increased, as did the fraction with transfers (4.2% to 8.5%).DiscussionOver the study period, the proportion of AIS/TIA episodes with consultations from neurologists with either vascular neurology certifications or high volumes increased substantially.

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.

Telehealth provides a novel bridge between patient needs and available resources. On-demand telehealth visits provide urgent medical services in a virtual setting. Telehealth can be used to provide care for patients despite geographical distance. Emergency Medicine quickly adapted in response to the COVID-19 pandemic through utilization of telehealth to solve various problems. Tele-triage was used to coordinate COVID-19 testing and treatment. Greater utilization of all current and emerging telehealth modalities could increase access and quality of care for all Missourians.

Pediatric hospital care is becoming increasingly regionalized, and previous data have suggested that insurance may be associated with transfer. The aims of the study are to describe regionalization of pediatric care and density of the interhospital transfer network and to determine whether these varied by insurance status.Using the New York State ED Database and State Inpatient Database from 2016, we identified all pediatric patients and calculated regionalization indices (RI) and network density, overall and stratified by insurance. Regionalization indices are based on the likelihood of a patient completing care at the initial hospital. Network density is the proportion of actual transfers compared with the number of potential hospital transfer connections. Both were calculated using the standard State ED Database/State Inpatient Database transfer definition and in a sensitivity analysis, excluding the disposition code requirement.We identified 1,595,566 pediatric visits (emergency department [ED] or inpatient) in New York in 2016; 7548 (0.5%) were transferred and 7374 transferred visits had eligible insurance status (Medicaid, private, uninsured). Of the transfers, 24% were from ED to ED with discharge, 28% from ED to ED with admission, 31% from ED to inpatient, 16% from inpatient to inpatient, and 1.2% from inpatient to ED. The overall RI was 0.25 (95% confidence interval [95% CI], 0.20-0.31). The overall weighted RI was 0.09 (95% CI, 0.06-0.12) and was 0.09 (95% CI, 0.06-0.13) for Medicaid-insured patients, 0.08 (95% CI, 0.05-0.11) for privately insured patients, and 0.08 (95% CI, 0.05-0.11) for patients without insurance. The overall network density was 0.018 (95% CI, 0.017-0.020). Network density was higher, and transfer rates were lower, for patients with Medicaid insurance as compared with private insurance.We found significant regionalization of pediatric emergency care. Although there was not material variation by insurance in regionalization, there was variation in network density and transfer rates. Additional work is needed to understand factors affecting transfer decisions and how these patterns might vary by state.

Background Optimized stroke systems of care enable access to timely care, including endovascular thrombectomy (EVT). Stroke systems have likely evolved after publication of EVT benefit (2015). Our objective was to map the stroke patient transfer network in California in terms of EVT access and patient transfer patterns, and to examine changes after 2015. Methods In this observational study, we identified all ischemic stroke encounters, transfers, alteplase use, and EVT procedures in California from 2010 to 2017. An established connection between any hospital pair was defined as the transfer of ≥2 patients between them. A 2‐level logistic regression model assessed whether encounters were more frequently transferred to EVT‐capable hospitals post‐2015, adjusting for patient‐ and hospital‐level factors. Linear regression examined trends in key network characteristics over time, and interrupted time series regressions examined for changes post‐2015. Results Among 336 247 encounters, 3.4% were transferred, 9.3% received alteplase, and 2.3% underwent EVT. From 2010 to 2017 the proportion that were EVT treated increased (1.0%–4.3%; P ‐for‐trendP =0.01). Odds of transfer to EVT‐capable hospital were greater post‐2015 (adjusted odds ratio, 6.54; [95% CI 5.52–7.74]), but were lower for patients who were older, Black, Hispanic, and presented to a rural hospital. Significant network changes from 2010 to 2017 included increased number of encounters transferred, increased number of transferring hospitals, decreasing number of receiving hospitals, and increased proportion of receiving hospitals performing EVT. However, none of these network changes demonstrated a significant inflection point after 2015. Conclusion The California stroke network has been dynamic over time. Although most changes have been incremental, after the release of EVT trial data in 2015 changes in transfer patterns did lead to increased access to EVT.

Trauma center certifications across the United States (U.S.) are not unified. Participation in the national trauma certification program established through the American College of Surgeons (ACS) is not universal, and many states maintain unique trauma certification systems with varying criteria. We investigated degree of similarity between the ACS national trauma certification program and state trauma certifications, then combined these distinct certifications into a unified national trauma center database.We performed a cross-sectional study of all non-specialty, non-federal emergency U.S. departments (EDs) open in 2018 to determine availability and levels of trauma centers. We created a "Standard" definition of trauma levels using ACS criteria as a benchmark. ACS similar trauma levels were then assigned to state levels I-III by comparing trauma receiving protocol, maximum response times, and general surgical coverage; through this process, levels across distinct systems established through different criteria were standardized.In 2018, ACS certifications spanned 47 states and DC; 3 states did not participate in ACS (Mississippi, Pennsylvania, and Washington). A distinct, non-ACS state certification system was present in 47 states and DC; 3 states had no ongoing state certification system in 2018 (Maine, Rhode Island, and Vermont). Among 5,514 US EDs open in 2018, we identified 2,132 associated with adult and pediatric trauma centers (39%) holding certification (ACS, state, or both); 1,083 (51%) were certified levels I-III, and the rest (1,049, 49%) were levels IV-V. Of the 1,083 centers with any level I-III certification, 498 (46%) held ACS certification, and 1,059 (98%) held state certification. Applying ACS-similar criteria to centers with state levels I-III (n=1,059) resulted in a level change for 124 centers (12%). Using our "Standard" definition of a trauma level based on ACS criteria, our unified level I-III database included 959 (89%) adult and pediatric centers, with 24 (3%) ACS-certified only, 461 (48%) state-certified only, and 474 (49%) certified by both.Discrepancies exist between ACS and state trauma certification systems. The differences in level I-III state criteria confirm discrepant standards for a given trauma "level" across the U.S. We combined these certifications into a unified national trauma center database available to researchers and the public.

Objective During the first 9 months of the coronavirus disease 2019 (COVID-19) pandemic, many emergency departments (EDs) experimented with telehealth applications to reduce virus exposure, decrease visit volume, and conserve personal protective equipment. We interviewed ED leaders who implemented telehealth programs to inform responses to the ongoing COVID-19 pandemic and future emergencies. Materials and Methods From September to November 2020, we conducted semi-structured interviews with ED leaders across the United States. We identified EDs with pandemic-related telehealth programs through literature review and snowball sampling. Maximum variation sampling was used to capture a range of experiences. We used standard qualitative analysis techniques, consisting of both inductive and deductive approaches to identify and characterize themes. Results We completed 15 interviews with EDs leaders in 10 states. From March to November 2020, participants experimented with more than a dozen different types of telehealth applications including tele-isolation, tele-triage, tele-consultation, virtual postdischarge assessment, acute care in the home, and tele-palliative care. Prior experience with telehealth was key for implementation of new applications. Most new telehealth applications turned out to be temporary because they were no longer needed to support the response. The leading barriers to telehealth implementation during the pandemic included technology challenges and the need for “hands-on” implementation support in the ED. Conclusions In response to the COVID-19 pandemic, EDs rapidly implemented many telehealth innovations. Their experiences can inform future responses.

Background and Purpose: Patient care-seeking has likely changed during the coronavirus disease 2019 (COVID-19) pandemic. In stroke, delayed or avoided care may translate to substantial morbidity. We sought to determine the effect of the pandemic on patterns of stroke patient presentation and quality of care. Methods: We analyzed data from 25 New England hospitals: one urban, academic comprehensive stroke center and telestroke hub, and 24 spoke hospitals in the telestroke network. We included all telestroke consultations from the 24 spokes, and all stroke admissions to the comprehensive stroke center hub from November 1, 2019 through April 30, 2020. We compared rates of presentation, timeliness presentation, and quality of care pre- versus post-March 1, 2020. We examined trends in patient demographics, stroke severity, timeliness, diagnoses including large vessel occlusion, alteplase use, and endovascular thrombectomy among eligible subjects. We compared proportions and bivariate comparisons to examine for changes pre- versus post-March 1, 2020 and used linear regression to examine trends over time. Results: Among 1248 patient presentations (844 telestroke consultations, 404 comprehensive stroke center admissions), telestroke consultations and ischemic stroke patient admissions decreased among the spokes and hub. Age and stroke severity were unchanged over the study period. We found no change in alteplase administration at telestroke spoke hospitals but did note a decrease in both alteplase use and thrombectomy at our comprehensive stroke center. Time metrics for patient presentation and care delivery were unchanged; however, rates of adherence for the quality measures dysphagia screening, early antithrombotic initiation, and early venous thromboembolism prophylaxis were reduced during the pandemic. Conclusions: In this regional analysis, we found decreasing telestroke consultations and ischemic stroke admissions, and reduced performance on stroke quality of care measures during the COVID-19 pandemic. Contrary to prior reports, we did not find an increase in thrombectomy nor decrease in clinical severity that might be expected if patients with milder symptoms avoided hospitalization.

Aim: Sepsis is a top contributor to in-hospital mortality and, healthcare expenditures and telehealth have been shown to improve short-term sepsis care in rural hospitals. This study will evaluate the effect of provider-to-provider video telehealth in rural emergency departments (EDs) on healthcare costs and long-term outcomes for sepsis patients. Materials & methods: We will use Medicare administrative claims to compare total healthcare expenditures, mortality, length-of-stay, readmissions, and category-specific costs between telehealth-subscribing and control hospitals. Results: The results of this work will demonstrate the extent to which telehealth use is associated with total healthcare expenditures for sepsis care. Conclusion: These findings will be important to inform future policy initiatives to improve sepsis care in rural EDs. Clinical Trial Registration: NCT05072145 ( ClinicalTrials.gov )

Background: Patients with stroke are frequently transferred between hospitals. This may have implications on the quality of care received by patients; however, it is not well understood how the characteristics of sending and receiving hospitals affect the likelihood of a transfer event. Our objective was to identify hospital characteristics associated with sending and receiving patients with stroke. Methods: Using a comprehensive statewide administrative dataset, including all 78 Massachusetts hospitals, we identified all transfers of patients with ischemic stroke between October 2007 and September 2015 for this observational study. Hospital variables included reputation (US News and World Report ranking), capability (stroke center status, annual stroke volume, and trauma center designation), and institutional affiliation. We included network variables to control for the structure of hospital-to-hospital transfers. We used relational event modeling to account for complex temporal and relational dependencies associated with transfers. This method decomposes a series of patient transfers into a sequence of decisions characterized by transfer initiations and destinations, modeling them using a discrete-choice framework. Results: Among 73 114 ischemic stroke admissions there were 7189 (9.8%) transfers during the study period. After accounting for travel time between hospitals and structural network characteristics, factors associated with increased likelihood of being a receiving hospital (in descending order of relative effect size) included shared hospital affiliation (5.8× higher), teaching hospital status (4.2× higher), stroke center status (4.3× and 3.8× higher when of the same or higher status), and hospitals of the same or higher reputational ranking (1.5× higher). Conclusions: After accounting for distance and structural network characteristics, in descending order of importance, shared hospital affiliation, hospital capabilities, and hospital reputation were important factor in determining transfer destination of patients with stroke. This study provides a starting point for future research exploring how relational coordination between hospitals may ensure optimized allocation of patients with stroke for maximal patient benefit.

Introduction Our objective was to determine whether acute ischemic stroke (AIS) patients' language preference is associated with differences in time from symptom discovery to hospital arrival, activation of emergency medical services, door-to-imaging time (DTI), and door-to-needle (DTN) time. Methods We identified consecutive AIS patients presenting to a single urban, tertiary, academic center between 01/2003–05/2014 for whom language preference was available. Data were abstracted from the institution's Research Patient Data Registry and Get with the Guidelines-Stroke Registry. Bivariate and regression models evaluated the relationship between language preference and: 1) time from symptom onset to hospital arrival, 2) use of EMS, 3) DTI, and 4) DTN time. Results Of 3190 AIS patients, 300 (9.4%) were non-English preferring (NEP). Comparing NEP to English preferring (EP) patients in unadjusted or adjusted analyses, time from symptom discovery to arrival and rate of EMS utilization were not significantly different (overall median time 157 min, IQR 55–420; EMS utilization: 65% vs. 61.3% p = 0.21). There was also no significant differences in DTI or in likelihood of guideline-recommended DTI ≤ 25 min (overall median 59 min, IQR 29–127; DTI ≤ 25 min 24.3% vs. 21.3% p = 0.29) or DTN time or in likelihood of guideline-recommended DTN ≤ 60 min (overall median 53 min, IQR 36–73; DTN ≤ 60 min 62.5% vs. 58.2% p = 0.60). Conclusion Consistent with prior reports examining disparities in care, a systems-based approach to acute stroke prevents differences in hospital-based metrics. Reassuringly, NEP and EP patients also had similar speed of symptom recognition and EMS utilization.

Study objective There has been increasing attention to screening for health-related social needs. However, little is known about the screening practices of emergency departments (EDs). Within New England, we seek to identify the prevalence of ED screening for health-related social needs, understand the factors associated with screening, and understand how screening patterns for health-related social needs differ from those for violence, substance use, and mental health needs. Methods We analyzed data from the 2018 National Emergency Department Inventory–New England survey, which was administered to all 194 New England EDs during 2019. We used descriptive statistics to compare ED characteristics by screening practices, and multivariable logistic regression models to identify factors associated with screening. Results Among the 166 (86%) responding EDs, 64 (39%) reported screening for at least one health-related social need, 160 (96%) for violence (including intimate partner violence or other violent exposures), 148 (89%) for substance use disorder, and 159 (96%) for mental health needs. EDs reported a wide range of social work resources to address identified needs, with 155 (93%) reporting any social worker availability and 41 (27%) reporting continuous availability. Conclusion New England EDs are screening for health-related social needs at a markedly lower rate than for violence, substance use, and mental health needs. EDs have relatively limited resources available to address health-related social needs. We encourage research on the development of scalable solutions for identifying and addressing health-related social needs in the ED.

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

Emergency clinician-staffed telehealth programs seek to provide equitable, safe, efficient, effective, and patient-centered care. However, early studies show conflicting evidence on whether this aim is accomplished. Furthermore, how programs track the efficacy and safety of their programs remains largely unexplored. We sought to characterize ongoing quality monitoring among emergency clinician-staffed telehealth programs.We identified representatives at emergency clinician-staffed telehealth programs through professional networks and published literature. Qualitative interviews were conducted, assessing quality metrics captured as well as motivations for and barriers to quality measurement. We classified quality metric measurement using the National Quality Forum Telehealth Measurement Framework Domains and Subdomains. We developed a codebook from interview transcripts for qualitative analysis to classify motivations for and barriers to quality measurement.We held 8 qualitative interviews with physician representatives at primarily academic (7/8) and urban institutions (5/8). Most widely used quality metrics were related to patient and care team experience (7/8) as well as to access to care (6/8) and effectiveness (6/8). Few programs (2/8) measured finance-related quality metrics. Motivations for quality measurement varied considerably. Common barriers to implementation included technology challenges, data availability, and the lack of quality metric standardization.We identified variation in the use and content of quality metrics across emergency clinician-staffed telehealth programs. Most commonly, programs used metrics related to clinical experience; financial metrics were rarely captured. Technology barriers to quality measurement were often cited across programs. Further work is needed to support the standardization and implementation of future quality measurement initiatives.

There are significant racial and ethnic disparities in receipt of reperfusion interventions for acute ischemic stroke. Our objective was to determine whether there are disparities in access to stroke center care by race or ethnicity that help explain differences in reperfusion therapy and to understand whether interhospital patient transfer plays a role in improving access.Using statewide administrating data including all emergency department and hospital discharges in California from 2010 to 2017, we identified all acute ischemic stroke patients. Primary outcomes of interest included presentation to primary or comprehensive stroke center (PSC or CSC), interhospital transfer, discharge from PSC or CSC, and discharge from CSC alone. We used hierarchical logistic regression modeling to identify the relationship between patient- and hospital-level characteristics and outcomes of interest.Of 336,247 ischemic stroke patients, 55.4% were non-Hispanic White, 19.6% Hispanic, 10.6% non-Hispanic Asian/Pacific Islander, and 10.3% non-Hispanic Black. There was no difference in initial presentation to stroke center hospitals between groups. However, adjusted odds of reperfusion intervention, interhospital transfer and discharge from CSC did vary by race and ethnicity. Adjusted odds of interhospital transfer were lower among Hispanic (odds ratio [OR] 0.94, 95% confidence interval [CI] 0.89 to 0.98) and non-Hispanic Asian/Pacific Islander patients (OR 0.84, 95% CI 0.79 to 0.90) and odds of discharge from a CSC were lower for Hispanic (OR 0.91, 95% CI 0.85 to 0.97) and non-Hispanic Black patients (OR 0.74, 95% CI 0.67 to 0.81).There are racial and ethnic disparities in reperfusion intervention receipt among stroke patients in California. Stroke system of care design, hospital resources, and transfer patterns may contribute to this disparity.

Large collaborative research networks provide opportunities to jointly analyze multicenter electronic health record (EHR) data, which can improve the sample size, diversity of the study population, and generalizability of the results. However, there are challenges to analyzing multicenter EHR data including privacy protection, large-scale computation, heterogeneity across sites, and correlated observations. In this paper, we propose a federated algorithm for generalized linear mixed models (Fed-GLMM), which can flexibly model multicenter longitudinal or correlated data while accounting for site-level heterogeneity. Fed-GLMM can be applied to both federated and centralized research networks to enable privacy-preserving data integration and improve computational efficiency. By communicating only a limited amount of summary statistics, Fed-GLMM can achieve nearly identical results as the gold-standard method where the GLMM is directly fitted on the pooled dataset. We demonstrate the performance of Fed-GLMM in both numerical experiments and an application to longitudinal EHR data from multiple healthcare facilities.

Background: Optimal care for patients with stroke relies on timely recognition and rapid transport to appropriate treatment, often by emergency medical services (EMS). Our primary objective was to describe EMS time intervals for patients with suspected stroke in the United States. We also sought to quantify the variation in EMS time intervals by geographic location and urbanicity. Methods: We conducted a cross-sectional evaluation of EMS 9-1-1 activations (ie, calls for service) included in the 2018 and 2019 National EMS Information System datasets. We included ground or air EMS activations for a 9-1-1 scene response where a patient aged ≥18 years with suspected stroke was treated and transported by EMS. Time intervals for dispatch, response, scene, transport, and total prehospital time (ie, from dispatch to hospital arrival) were calculated, stratified by ground and air transport type. Results: A total of 410 187 activations for suspected stroke were included, of which 98% were a ground transport. The median total prehospital time for ground transports was 35 minutes (interquartile range, 27–45, 90th percentile 58). Median total prehospital time for air transports was substantially longer at 56 minutes (interquartile range, 43–70, 90th percentile 86). Times varied by Census division and urbanicity with the shortest ground total prehospital times in the East North Central division and urban areas and longest times in the East South Central and rural and frontier areas. Conclusions: Timely EMS response and transport is critical for optimizing care of patients with suspected stroke. Using a large, national dataset of EMS activations, we found variations by geographic location and urbanicity in total prehospital time for ground and air EMS activations for patients with stroke.

Objective: Many U.S. emergency departments (EDs) lack access to stroke neurologists to support decision-making for thrombolytics and identification of thrombectomy-eligible patients. We outline a strategy to identify hospitals where telestroke might improve access and estimate potential gains in both the number of patients receiving reperfusion treatment and lives saved. Methods: We identified all EDs that provided ischemic stroke care for a Medicare beneficiary during 2018. We then excluded those with clear stroke expertise or with another ED with stroke expertise within 20 miles. At these EDs, we used annual ischemic stroke volumes and previously-derived risk ratios to quantify estimated marginal benefits (additional patients receiving reperfusion and additional lives saved) with the introduction of telestroke. Results: Among 4657 US EDs that provided stroke care in 2018, 1057 had limited stroke capabilities in their ED or within 20 miles. Of these 1057 EDs, 83.1% were in rural communities, and they cared for a median of 6 ischemic stroke patients per year. We estimate telestroke introduction to all 1057 would lead to 164 (95% CI 93-247) additional patients receiving reperfusion treatment and 90 (95% CI 2-180) additional lives saved annually (Figure). If only 263 EDs in the the top quartile of marginal benefit were targeted, this would capture over half of the estimated benefits. Conclusions: We estimate that approximately a quarter of U.S. EDs, primarily small rural EDs, would benefit most from new telestroke capacity. Our strategy may be used to improve stroke systems of care and maximize specialist access for the U.S. population.

Backgound: In 2011, nearly 20% of Americans lacked timely access to alteplase-capable hospitals. We update this work by assessing access to stroke centers and emergency departments (EDs) with telestroke capacity. Our objectives are to identify all US EDs with acute stroke capabilities (i.e., in a confirmed stroke center or with telestroke capacity), and to characterize the proportion of the US population with access to an ED with either capacity. Methods: We used the 2019 National ED Inventory-USA to identify all US EDs and characterize stroke capabilities by hospital stroke center status (none, acute stroke ready hospital [ASRH], primary stroke center [PSC], thrombectomy-capable or comprehensive stroke center [TSC/CSC]) and telestroke capacity. We used 2020 US Census data for census block group population and centroid. For each block group, we used ArcGIS to assess whether an ED with stroke expertise was within a 60 minute (min) response and transport time by ground emergency medical services (EMS). To determine the transport time, we used data from actual EMS stroke transports using the 2019 National EMS Information System with median EMS dispatch, response, and scene times in access calculations. Results: Of 5,587 US EDs, 2,563 (46%) were in a stroke center (691 ASRH, 1,505 PSC, 367 TSC/CSC); of these, 55% also had telestroke capacity. Of the 3,024 (54%) that were not a confirmed stroke center, 36% had telestroke. We estimate that 91% of the US population is within 60 min of a confirmed stroke center by ground EMS and 96% is within 60 min of a confirmed stroke center or telestroke ED (Figure). The percentage of the population without access to a confirmed stroke center or telestroke ED varied by region, from 1% in the Middle Atlantic to 9% in the West Mountain. Conclusion: Relative to previous reports, an increasing proportion of the US population has access to acute stroke expertise. While geographic disparities in access remain, telestroke plays an important role in filling this gap.

Introduction: Prehospital recognition and rapid transport of stroke patients by emergency medical services (EMS) is critical to optimize access to time-sensitive care. We aimed to describe EMS time intervals for suspected stroke patients in the U.S. and to quantify variation in EMS time intervals by geographic location and urbanicity. Methods: We conducted a cross-sectional evaluation of EMS 9-1-1 activations (i.e., calls for service) included in the 2018 and 2019 National EMS Information System. We included ground and air EMS activations for a scene response where a patient aged ≥18 years old with suspected stroke (EMS impression of I60-I63 or G45) was treated and transported by EMS. Time intervals for dispatch, response, scene, transport, and total prehospital time were calculated, stratified by ground and air transport type. Results: A total of 415,342 activations for suspected stroke were included, of which 98% were a ground transport. The overall median total prehospital time for ground transports was 35 minutes (IQR 27-45, 90 th percentile 58; Table). Median total prehospital time for air transports was substantially longer at 56 minutes (IQR 43-70, 90 th percentile 86). Times varied by census division and urbanicity with the shortest median total prehospital time of ground transports in the East North Central division (31 min, IQR 25-40) and urban areas (34 min, IQR 27-43) and longest times in the East South Central (39 min, IQR 30-52), rural (40 min, IQR 28-56), and frontier (44 min, IQR 29-63) areas. Conclusions: With ongoing focus on optimizing time-to-treatment for acute stroke interventions, prehospital transport can be targeted for quality improvement. While some variation is expected due to geographical constraints, we found substantial variation in prehospital transport times for suspected stroke that may present opportunities for future improvement. These findings may inform health services research and policy efforts to improve stroke care access.

Backgrounds: Telestroke is associated with improved reperfusion and improved outcomes for stroke patients. Our objective was to examine the effect of telestroke on procedural timeliness and outcomes among patients transferred for endovascular therapy (EVT). Methods: We identified all EVT encounters at our urban, telestroke-enabled, academic comprehensive stroke center (CSC) from 1/1/2017 to 12/31/2020 in our institutional Get With The Guidelines-Stroke registry linked with our telestroke consultation records. EVT encounters were categorized based on mode of arrival: i) transferred via the telestroke network (Tx+TS), ii) transferred without telestroke (Tx-TS), or iii) directly presenting to the CSC (DP). The three groups were then compared on efficacy and safety measurements including first-pass rate, successful reperfusion (TICI 2b/3) and post-procedure complications, as well as mortality and functional independence (mRS ≤ 2) outcomes both at discharge and 90-day post discharge using multi-group testing. Results: After excluding 29 encounters with unknown arrival mode, our sample of 287 EVT encounters included 168 Tx+TS cases, 35 Tx-TS cases, and 84 DP cases. Patients’ demographics and baseline characteristics (NIHSS at admission and prior comorbidities) were balanced across the groups (Table). Compared to both Tx-TS and DP, Tx+TS has significantly shorter door to puncture time (Tx+TS: 30 mins vs. Tx-TS: 62 mins vs. DP: 92 mins, p < 0.001). Procedural efficacy and safety, in-hospital outcomes and post discharge outcomes were similar across groups. Conclusion: Our study demonstrates that telestroke plays an important role among patients transferred for EVT with improved procedural timeliness, and outcomes similar to those of directly presenting patients.

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Objective Physician assistant (PA) and nurse practitioner (NP) staffing is increasingly common in emergency departments (EDs), with variable physician supervision. We examined the feasibility of using publicly reported metrics as a measure of ED performance by staffing model. Methods We classified a convenience sample of 915 EDs by staffing model using the National Emergency Department Inventory‐USA 2016 and a follow‐up survey. Staffing models included 24/7 attending coverage with PAs/NPs, 24/7 attending coverage without PAs/NPs, and PAs/NPs without 24/7 attending coverage. We linked EDs with Hospital Compare data to examine availability of metrics and compared metric performance by staffing model. We used regression modeling to examine the independent relationship between staffing model and ED performance after adjusting for ED characteristics. Results Of 915 EDs surveyed, 767 (83%) responded and 436 (48%) had complete staffing data and any Hospital Compare data. The 216 EDs without any Hospital Compare data more frequently had no 24/7 attending coverage, were smaller, and were more often rural. Of 5 clinical metrics, 3 had data from