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Our objectives were to describe time intervals of EMS encounters for suspected stroke patients in North Carolina (NC) and evaluate differences in EMS time intervals by community socioeconomic status (SES) and rurality.This cross-sectional study used statewide data on EMS encounters of suspected stroke in NC in 2019. Eligible patients were adults requiring EMS transport to a hospital following a 9-1-1 call for stroke-like symptoms. Incident street addresses were geocoded to census tracts and linked to American Community Survey SES data and to rural-urban commuting area (RUCA) codes. Community SES was defined as high, medium, or low based on tertiles of an SES index. Urban, suburban, and rural tracts were defined by RUCA codes 1, 2-6, and 7-10, respectively. Multivariable quantile regression was used to estimate how the median and 90th percentile of EMS time intervals varied by community SES and rurality, adjusting for each other; patient age, gender, and race/ethnicity; and incident characteristics.We identified 17,117 eligible EMS encounters of suspected stroke from 2028 census tracts. The population was 65% 65+ years old; 55% female; and 69% Non-Hispanic White. Median response, scene, and transport times were 8 (interquartile range, IQR 6-11) min, 16 (IQR 12-20) min, and 14 (IQR 9-22) minutes, respectively. In quantile regression adjusted for patient demographics, minimal differences were observed for median response and scene times by community SES and rurality. The largest median differences were observed for transport times in rural (6.7 min, 95% CI 5.8, 7.6) and suburban (4.7 min, 95% CI 4.2, 5.1) tracts compared to urban tracts. Adjusted rural-urban differences in 90th percentile transport times were substantially greater (16.0 min, 95% CI 14.5, 17.5). Low SES was modesty associated with shorter median (-3.3 min, 95% CI -3.8, -2.9) and 90th percentile (-3.0 min, 95% CI -4.0, -2.0) transport times compared to high SES tracts.While community-level factors were not strongly associated with EMS response and scene times for stroke, transport times were significantly longer rural tracts and modestly shorter in low SES tracts, accounting for patient demographics. Further research is needed on the role of community socioeconomic deprivation and rurality in contributing to delays in prehospital stroke care.

ObjectivesThe COVID-19 pandemic has required significant modifications of hospital care. The objective of this study was to examine the operational approaches taken by US hospitals over time in response to the COVID-19 pandemic.Design, setting and participantsThis was a prospective observational study of 17 geographically diverse US hospitals from February 2020 to February 2021.Outcomes and analysisWe identified 42 potential pandemic-related strategies and obtained week-to-week data about their use. We calculated descriptive statistics for use of each strategy and plotted percent uptake and weeks used. We assessed the relationship between strategy use and hospital type, geographic region and phase of the pandemic using generalised estimating equations (GEEs), adjusting for weekly county case counts.ResultsWe found heterogeneity in strategy uptake over time, some of which was associated with geographic region and phase of pandemic. We identified a body of strategies that were both commonly used and sustained over time, for example, limiting staff in COVID-19 rooms and increasing telehealth capacity, as well as those that were rarely used and/or not sustained, for example, increasing hospital bed capacity.ConclusionsHospital strategies during the COVID-19 pandemic varied in resource intensity, uptake and duration of use. Such information may be valuable to health systems during the ongoing pandemic and future ones.

ObjectiveTo determine the effect of a user centered clinical decision support tool versus usual care on rates of initiation of buprenorphine in the routine emergency care of individuals with opioid use disorder.DesignPragmatic cluster randomized controlled trial (EMBED).Setting18 emergency department clusters across five healthcare systems in five states representing the north east, south east, and western regions of the US, ranging from community hospitals to tertiary care centers, using either the Epic or Cerner electronic health record platform.Participants599 attending emergency physicians caring for 5047 adult patients presenting with opioid use disorder.InterventionA user centered, physician facing clinical decision support system seamlessly integrated into user workflows in the electronic health record to support initiating buprenorphine in the emergency department by helping clinicians to diagnose opioid use disorder, assess the severity of withdrawal, motivate patients to accept treatment, and complete electronic health record tasks by automating clinical and after visit documentation, order entry, prescribing, and referral.Main outcome measuresRate of initiation of buprenorphine (administration or prescription of buprenorphine) in the emergency department among patients with opioid use disorder. Secondary implementation outcomes were measured with the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework.Results1 413 693 visits to the emergency department (775 873 in the intervention arm and 637 820 in the usual care arm) from November 2019 to May 2021 were assessed for eligibility, resulting in 5047 patients with opioid use disorder (2787 intervention arm, 2260 usual care arm) under the care of 599 attending physicians (340 intervention arm, 259 usual care arm) for analysis. Buprenorphine was initiated in 347 (12.5%) patients in the intervention arm and in 271 (12.0%) patients in the usual care arm (adjusted generalized estimating equations odds ratio 1.22, 95% confidence interval 0.61 to 2.43, P=0.58). Buprenorphine was initiated at least once by 151 (44.4%) physicians in the intervention arm and by 88 (34.0%) in the usual care arm (1.83, 1.16 to 2.89, P=0.01).ConclusionsUser centered clinical decision support did not increase patient level rates of initiating buprenorphine in the emergency department. Although streamlining and automating electronic health record workflows can potentially increase adoption of complex, unfamiliar evidence based practices, more interventions are needed to look at other barriers to the treatment of addiction and increase the rate of initiating buprenorphine in the emergency department in patients with opioid use disorder.Trial registrationClinicalTrials.govNCT03658642.

Patients with heart failure (HF) often suffer from multimorbidity. Rapid assessment of multimorbidity is important for minimizing the risk of harmful drug-disease and drug-drug interactions. We assessed the accuracy of using the electronic health record (EHR) problem list to identify comorbid conditions among patients with chronic HF in the emergency department (ED). A retrospective chart review study was performed on a random sample of 200 patients age ≥65 years with a diagnosis of HF presenting to an academic ED in 2019. We assessed participant chronic conditions using: (1) structured chart review (gold standard) and (2) an EHR-based algorithm using the problem list. Chronic conditions were classified into 37 disease domains using the Agency for Healthcare Research Quality’s Elixhauser Comorbidity Software. For each disease domain, we report the sensitivity, specificity, positive predictive value, and negative predictive of using an EHR-based algorithm. We calculated the intra-class correlation coefficient (ICC) to assess overall agreement on Elixhauser domain count between chart review and problem list. Patients with HF had a mean of 5.4 chronic conditions (SD 2.1) in the chart review and a mean of 4.1 chronic conditions (SD 2.1) in the EHR-based problem list. The five most prevalent domains were uncomplicated hypertension (90%), obesity (42%), chronic pulmonary disease (38%), deficiency anemias (33%), and diabetes with chronic complications (30.5%). The positive predictive value and negative predictive value of using the EHR-based problem list was greater than 90% for 24/37 and 32/37 disease domains, respectively. The EHR-based problem list correctly identified 3.7 domains per patient and misclassified 2.0 domains per patient. Overall, the ICC in comparing Elixhauser domain count was 0.77 (95% CI: 0.71-0.82). The EHR-based problem list captures multimorbidity with moderate-to-good accuracy in patient with HF in the ED.

Introduction: Patients with heart failure (HF) often suffer from multimorbidity, the co-occurrence of two or more chronic conditions. Electronic health records (EHRs) store data on chronic conditions within the problem list, which can be readily extracted for research purposes. Objective: To assess the reliability in using the EHR-based problem list to identify comorbid conditions among patients with HF in the emergency department. Methods: A retrospective chart review study was performed on a random sample of 200 patients age >65 years with a diagnosis of HF who presented to an academic emergency department (ED) in 2019 (mean age 78.3 years old, 52.5% women). Manual chart review was performed by a trained medical student (BK) as the gold standard in identifying additional chronic conditions in patients with HF. All diagnoses were mapped to International Classification of Diseases, Tenth Revision, Clinical Modification diagnostic codes and classified into 38 domains using the Agency for Healthcare Research Quality’s Elixhauser Comorbidity Software. We computed sensitivity, specificity, positive predictive value, and negative predictive value of using the EHR’s problem list to capture each domain. We calculated the intra-class correlation coefficient (ICC) to estimate reliability in the total number of domains captured by each method. Results: Our chart review found that patients with HF had a mean of 5.4 chronic conditions (SD 2.1). In comparison, the EHR's problem list identified a mean of 4.0 chronic conditions (SD 2.0). The five most prevalent domains were hypertension, diabetes, obesity, chronic pulmonary disease, and renal failure. Specificities for the five most prevalent domains were at least 97% whereas sensitivities were considerably lower and varied widely (28.6%-80.7%; Table 1). The ICC comparing chart review to the problem list was 0.77 (95% CI: 0.68-0.80). Conclusion: Multimorbidity in patients with HF in the ED was captured with good reliability using the EHR problem list.

Introduction: Rapid emergency medical services (EMS) response, care, and transport of acute stroke patients increases the likelihood of receiving time-sensitive treatment. There is limited research on the relationship of community factors with prehospital delays in EMS management of stroke. We compared EMS prehospital time intervals for suspected stroke patients by community socioeconomic status (SES) and urban-rural status in North Carolina (NC). Methods: We analyzed 2019 NC EMS encounter data collected in the National EMS Information System Version 3 standard. Eligible patients were adults requiring a 911 call response by EMS for stroke-like symptoms and transport to the hospital. Incident street addresses were geocoded to census tracts using ArcGIS Pro and linked to SES data from the American Community Survey and rural-urban commuting area (RUCA) codes. Community SES was defined as high, medium, or low based on tertiles of the AHRQ SES index. Urban, suburban, and rural tracts were defined by RUCA codes 1, 2-6, and 7-10, respectively. Differences in mean times by community SES and urban-rural status were evaluated using Kruskal Wallis tests. Results: We identified 17,117 eligible EMS encounters of suspected stroke (65% (11,121 of 17,117) 65+ years old; 55% (9,330 of 17,117) female; 70% (11,839 of 17,117) White) from 2,028 tracts. Median response, scene, and transport times were 8 (interquartile range, IQR 6-11) min, 16 (IQR 12-20) min, and 14 (IQR 9-22) min, respectively. Mean transport times were shorter in low SES (16 min) than in medium and high SES communities (17 min, p Conclusions: EMS transport times were on average longer for suspected stroke patients in rural but not in low SES tracts. Further research is needed on the role of community socioeconomic deprivation and rurality in contributing to delays in prehospital stroke care.