Estimated population access to acute stroke and telestroke centers in the US, 2019

This cross-sectional study assesses US population access to emergency departments with acute stroke capabilities and telestroke capacity in 2019

A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data.

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 resource requirements, 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 a limited amount of summary statistics, Fed-GLMM can achieve nearly identical results as the gold-standard method where the GLMM is directly fitted to the pooled dataset. We demonstrate the performance of Fed-GLMM in numerical experiments and an application to longitudinal EHR data from multiple healthcare facilities

Parameter estimates generated by Fed-GLMM for the virtual care utilization analysis.

We displayed the adjusted odds ratios with 95% confidence intervals obtained through Fed-GLMM for both single EHR from Facility 4 (centralized setting to demonstrate computation improvement) and all facilities (federated setting to demonstrate privacy preservation). We adopted a complete-case analysis where 6.8% of the observations with missing values were removed. Abbreviation: Ref—Reference Group. (DOCX)</p

Accuracy of Fed-GLMM and meta-analysis estimates relative to the gold-standard pooled analysis.

We compared the accuracy of Fed-GLMM with the standard meta-analysis by calculating the median absolute relative difference compared to the gold-standard pooled estimate of the coefficient of a binary exposure variable. The underlying model has a binary outcome, a binary exposure, three more covariates with 8 site-specific fixed effect coefficients for the normally distributed covariate and a patient-level random intercept. The model also includes 8 site-specific parameters for variance components. We considered 25 combinations of outcome and exposure prevalence to assess the model accuracy with 100 simulation replicates per combination. Fed-GLMM demonstrated reduced relative bias after 1–2 iterations compared with the meta-analysis, which was highly biased in the presence of rare events.</p

Association of Emergency Department Payer Mix with ED Receipt of Telehealth Services: An Observational Analysis

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 &gt;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

Clustering-based splitting strategy.

This strategy aims to cluster physicians who shared patients together by examining physicians’ patient-sharing network. With the clustering-based splitting strategy, two physicians are more likely to be assigned to the same subsets if they share more patients so that the patients’ visits linked to multiple physicians are likely to be included in the same data subsets. (DOCX)</p

Descriptive statistics for the virtual care utilization analysis.

We identified all outpatient encounters spanning 10/1/2020 through 9/30/2021 from the 8 acute care hospital facilities in the New England area, which we deidentified and denoted as Facilities 1 through 8. We described all the variables in the dataset to be used in the Fed-GLMM analysis, as well as the overall observation distribution across facilities. (DOCX)</p

Impact of Emergency Department Crowding on Delays in Acute Stroke Care

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