CMAJ Open

ImportanceDue to high community transmission of the Omicron variant, healthcare workers (HCWs) have been increasingly reporting household exposures to confirmed COVID-19 cases. Quebec (Canada) provincial guidelines required to quarantine these HCWs. Facing the risk of staffing shortages, our hospital decided to allow them to work. ObjectiveTo evaluate the risk for HCWs, who were household contacts, to become positive for COVID-19 by RT-PCR and evaluate the risk of nosocomial COVID-19 transmission. DesignCohort of HCWs with a history of household exposure to a confirmed case of COVID-19. SettingCHU Sainte-Justine, a tertiary care mother and child center in Montreal (QC) Canada ParticipantsConsecutive HCWs who contacted OHS between December 20, 2021 and January 17, 2022 for a history of household exposure to COVID-19. ExposureConfirmed case of COVID-19 in the household Main outcome and measuresThe main outcome was a positive RT-PCR for SARS-CoV-2. Outbreaks and nosocomial cases were identified through daily analysis of COVID-19 cases, by sector and part of the usual Infection Prevention and Control surveillance process. ResultsOverall, 237 of 475 (50%) HCWs who declared a known household contact with a confirmed COVID-19 case remained negative. Of those who became positive, 196 (82.4%) were positive upon initial testing and were quarantined. Only 42 (15%) of 279 HCWs who were allowed to work became positive, a median of 4 days after the initial test. The absence of symptoms at initial evaluation (OR 3.8, 95% CI 2.5-5.7) and having received a third vaccine dose more than 7 days before (OR 1.88, 95% CI 1.3 - 2.8) were associated with an increased odds of remaining negative. There was no outbreak among HCWs and no nosocomial transmission to patients from a HCW that was allowed to work, while a known household contact. Conclusion and relevanceMeasures taken to protect the health care environment from COVID-19 must be cautiously balanced with the risk of staffing shortage. Allowing vaccinated asymptomatic HCWs who are known household contacts of confirmed COVID-19 cases to work is likely a safe alternative, when staff shortage is anticipated.

PurposeHealthcare workers (HCWs) play a critical role in responding to the COVID-19 pandemic. Early in the pandemic, urban centres were hit hardest globally; rural areas gradually became more impacted. We compared COVID-19 infection and vaccine uptake in HCWs living in urban versus rural locations within, and between, two health authorities in British Columbia (BC), Canada. We also analyzed the impact of a vaccine mandate for HCWs. MethodsWe tracked laboratory-confirmed SARS-CoV-2 infections, positivity rates, and vaccine uptake in 29,021 HCWs in Interior Health (IH) and 24,634 HCWs in Vancouver Coastal Health (VCH), by occupation, age, and home location, comparing to the general population in that region. We then evaluated the impact of infection rates as well as the mandate on vaccination uptake. ResultsBy October 27, 2021, the date that unvaccinated HCWs were prohibited from providing healthcare, only 1.6% in VCH yet 6.5% in IH remained unvaccinated. Rural workers in both areas had significantly higher unvaccinated rates compared with urban dwellers. Over 1,800 workers, comprising 6.4% of rural HCWs and 3.3% of urban HCWs, remained unvaccinated and set to be terminated from their employment. While the mandate prompted a significant increase in second doses, the impact on the unvaccinated was less clear. ConclusionsAs rural areas often suffer from under-staffing, loss of HCWs could have serious impacts on healthcare provision as well as on the livelihoods of unvaccinated HCWs. Greater efforts are needed to understand how to better address the drivers of rural-related vaccine hesitancy as the pandemic continues.

BackgroundThe requirement for critical care in even a modest fraction of SARS-CoV-2 infected individuals made ICU resources an important societal chokepoint during the recent pandemic. We developed a simple regression-based point score in 2020 based on an objective of forecasting critical care occupancy in the Canadian province of Ontario based on mean age of cases, case numbers, and testing volume. Evolution of the pandemic (variants of concern, vaccination) led us to re-assess and re-calibrate our earlier work, with inclusion of information vaccination which became widespread in 2021. MethodsWe obtained complete provincial SARS-CoV-2 case, testing, and vaccination data for the period from March 2020 to September 2022, with data subdivided into 6 major "waves", following the approach applied by other Canadian investigators. Our initial model was fit only using the first two "wild type" SARS-CoV-2 waves; an updated model included wave 3 (N501Y+ variants). Our model was validated by comparing model projections to waves not used for model fitting; validation model fits were evaluated with Spearmans rho; counterfactuals without vaccination were modeled to impute fraction of critical care admissions prevented with vaccination. Costing was based on published economic estimates. ResultsOur initial model (fit to waves 1 and 2) was well calibrated (rho 0.85) but predictive validity was modest (rho 0.46). Predictive validity improved in models fit to the first 3 pandemic waves without vaccination (rho 0.60) or with vaccination (rho 0.68) (P for inclusion of vaccination 0.013 by Likelihood Ratio Test). Prevented fraction of ICU admissions attributable to vaccination was 144% (22017 admissions expected vs. 9020 observed); based on published estimates of ICU admission cost for SARS-CoV-2 the 12977 admissions averted $2.9 (CDN) billion in economic costs, in contrast to the $3 billion total cost of the vaccination program. ConclusionsSimple time series regression incorporating case and testing characteristics continues to be useful as a tool for forecasting critical care occupancy due to SARS-CoV-2 but early pandemic models need to be updated to capture the preventive effects of widespread vaccination. The economic benefit of vaccination for prevention of critical care resource consumption during the pandemic is substantial, achieving near cost neutrality with the provinces entire vaccination program.

BackgroundThe increased use of telemedicine during the pandemic has led to concerns about potential increased emergency department (ED) admissions and outpatient service use prior to such admissions. We examined the frequency of telemedicine use prior to ED admissions and characterized the patients with prior telemedicine use and the physicians who provided these outpatient visits. MethodsWe conducted a retrospective, population-based, cross-sectional analysis using linked health administrative data in Ontario, Canada to identify patients who had an ED admission between July 1 and September 30, 2021 and patients with an ED admissions during the same period in 2019. We grouped patients based on their use of outpatient services in the 7 days prior to admission and reported their sociodemographic characteristics and healthcare utilization. ResultsThere were 1,080,334 ED admissions in 2021 vs. 1,113,230 in 2019. In 2021, 74% of these admissions had no prior outpatient visits (virtual or in-person) within 7 days of admission, compared to 75% in 2019. Only 3% of ED admissions had both virtual and in-person visits in the 7 days prior to ED admission. Patients with prior virtual care use were more likely to be hospitalized than those without any outpatient care (13% vs 7.7.%). InterpretationThe net amount of ED admissions and outpatient care prior to admission remained the same over a period of the COVID-19 pandemic when cases were relatively stable. Virtual care seems to be able to appropriately triage patients to the ED and may even prove beneficial for diverting patients away from the ED when an ED admission is not appropriate. The COVID-19 pandemic has led to the emergence of standard use of telemedicine in health care across the globe(1,2). In Ontario, Canada the proportion of ambulatory visits completed virtually has been maintained at slightly above 50% from 2020 to 2021 (3). Despite its widespread adoption, it is still unclear when virtual visits are clinically appropriate and how such wide use of telemedicine impacts patient outcomes and healthcare utilization metrics. Before the pandemic, there had been concerns that telemedicine may lead to an increased use of outpatient services with patients having both a virtual and an in-person visit for the same clinical issue(4,5). For example, pre-pandemic data (2007-2016) from Manitoba showed that telemedicine users had on average 1.3 times more ambulatory visits than non-users.(6) In addition, studies have produced mixed evidence with regard to the effect of telemedicine on urgent services such as emergency department (ED) admissions and hospitalizations (7). Many of the studies reported in the literature are based on data from site-specific programs and therefore have limited generalizability. Finally, policymakers and some physicians have become concerned that the high rates of telemedicine during COVID-19 have led to an increase in emergency department admissions because of poor access to in-person outpatient care (8). This concern is exacerbated when one considers rural and lower socioeconomic status patients who already had poor access to care before the pandemic(9). Combined with reports of lower uptake of telemedicine among these patients(10,11), it is not clear how the transition of care from in-person to virtual impacts ED use. The high adoption of telemedicine during the pandemic, in the context of a publicly funded healthcare system allowing us access to most visits across the entire population, offers a unique opportunity to examine the frequency of telemedicine use prior to ED admissions. Therefore, the goal of this study was to characterize the frequency and modality (in-person vs virtual) of outpatient care prior to ED admissions. We examined whether there was an overall increase in outpatient visits prior to ED admissions during a period of the pandemic when access to telemedicine was available compared to a seasonality matched period before the pandemic where access to telemedicine was quite limited. We also aimed to characterize the patients who had a telemedicine visit prior to an ED admission vs. those who had an in-person visit and the physicians who saw patients with virtual only visits prior to their ED admission compared to those who saw patients virtually or in-person prior to their ED admission.

ImportanceMissed opportunities for diagnosis (MODs), sometimes termed diagnostic errors, are a major cause of patient morbidity and mortality in the emergency department (ED). EDs have employed eTriggers, rule-based case collections likely to have a higher than average error rate (e.g. 72 hour returns with admission), but their utility is limited by low error yields. Large language models (LLMs) offer new opportunities to identify MODs and contribute to both individual- and systems-level quality improvement. ObjectiveTo determine whether sequential screening of ED cases with eTriggers and an LLM can more efficiently identify MODs compared to eTriggers alone. DesignRetrospective observational cohort study of ED encounters collected between March 2015 and June 2025. Setting10 EDs (2 academic, 8 community) in a single US health system. ParticipantsEmergency physicians reviewed and adjudicated random samples of cases identified by 3 previously validated eTriggers (72-hour return with admission, 10-day return with ICU admission, and floor-to-ICU escalation within 24 hours) using the SaferDX instrument. An ED physician also evaluated a novel hybrid eTrigger combining an LLM adjudicator with a rules engine for 9-day return admissions with emergency care- sensitive conditions (ECSCs). ExposuresLLM MOD adjudication of ED cases with Claude Sonnet 4 using an iteratively-developed, standardized prompt incorporating the SaferDx instrument. Main Outcome(s) and Measure(s)Positive predictive value (PPV), sensitivity, specificity, negative predictive value (NPV), and number needed to screen (NNS) for MODs. Reviewer time to adjudicate cases and quality improvement stakeholder assessments of LLM case summaries were also measured. ResultsOf the 357 encounters (mean [SD] age, 65.2 [17.8] years; 47.1% female) reviewed, adjudicated MOD PPV ranged from 11.0% to 18.6% across traditional eTriggers. For 72-hour return admissions, the LLM achieved sensitivity 85.7% (95% CI, 65.4%-95.0%), specificity 56.8% (95% CI, 49.3%-64.0%), PPV 19.8%, and NPV 97.0%. For 10-day ICU returns, sensitivity was 100% (95% CI, 56.6%-100%), specificity 43.5% (95% CI, 25.6%-63.2%), PPV 27.8%, and NPV 100%. For floor-to-ICU escalations, sensitivity was 55.6% (95% CI, 33.7%-75.4%), specificity 64.6% (95% CI, 53.6%-74.2%), PPV 26.3%, and NPV 86.4%. The hybrid ECSC eTrigger identified 110 MODs (53.1% of 207 encounters), with blinded review of a stratified sample estimating PPV 45% and NPV 100%. Expert reviewers required a median of 5 minutes per case; restricting review to LLM-positive charts reduced review time by up to 50% without missed errors for these triggers. In stakeholder review, LLM-generated case summaries were rated highly actionable for individual clinician feedback (mean, 4.1 of 5) but less so for systems-level interventions (mean, 1.4 of 5). Conclusions and RelevanceIn this multisite retrospective study, LLMs demonstrated high NPVs across multiple eTrigger criteria. Sequential use of LLM and human review improved efficiency and detection compared with traditional eTriggers, and narrative case summaries offered a novel method to identify opportunities for clinician-level feedback. These findings suggest that LLM-based approaches may provide scalable diagnostic quality oversight in the ED. Key PointsO_ST_ABSQuestionC_ST_ABSCan sequential screening with eTriggers and a large-language-model (LLM) identify missed opportunities for diagnosis (MODs) in the emergency department, improving screening efficiency versus traditional eTriggers? FindingsIn a multicenter retrospective cohort (10 EDs; 317 reviewed encounters), LLM adjudication showed high sensitivity and NPV across three established eTriggers (e.g., 72-hour returns: sensitivity 85.7%, NPV 97.0; 10-day ICU returns: sensitivity 100%, NPV 100%). A sequential approach was validated on a novel eTrigger for 9-day returns for select emergency care sensitive conditions, achieving PPV 45% and NPV 100% in 40 blinded samples. MeaningLLM-augmented eTrigger screening offers scalable, efficient MOD detection to support diagnostic quality oversight in EDs.

BackgroundThe goal of this study was to project the number of COVID-19 cases and demand for acute hospital resources for Fall of 2021 in a representative mid-sized community in southwestern Ontario. We sought to evaluate whether current levels of vaccine coverage and contact reduction could mitigate a potential 4th wave fueled by the Delta variant, or whether the reinstitution of more intense public health measures will be required. MethodsWe developed an age-stratified dynamic transmission model of COVID-19 in a mid-sized city (population 500,000) currently experiencing a relatively low, but increasing, infection rate in Step 3 of Ontarios Wave 3 recovery. We parameterized the model using the medical literature, grey literature, and government reports. We estimated the current level of contact reduction by model calibration to cases and hospitalizations. We projected the number of infections, number of hospitalizations, and the time to re-instate high intensity public health measures over the fall of 2021 under different levels of vaccine coverage and contact reduction. ResultsMaintaining contact reductions at the current level, estimated to be a 17% reduction compared to pre-pandemic contact levels, results in COVID-related admissions exceeding 20% of pre-pandemic critical care capacity by late October, leading to cancellation of elective surgeries and other non-COVID health services. At high levels of vaccination and relatively high levels of mask wearing, a moderate additional effort to reduce contacts (30% reduction compared to pre-pandemic contact levels), is necessary to avoid re-instating intensive public health measures. Compared to prior waves, the age distribution of both cases and hospitalizations shifts younger and the estimated number of pediatric critical care hospitalizations may substantially exceed 20% of capacity. DiscussionHigh rates of vaccination coverage in people over the age of 12 and mask wearing in public settings will not be sufficient to prevent an overwhelming resurgence of COVID-19 in the Fall of 2021. Our analysis indicates that immediate moderate public health measures can prevent the necessity for more intense and disruptive measures later.

BackgroundCOVID-19 patients seen in an emergency department (ED) are at high risk of complications including post-COVID-19 condition (PCC), commonly known as Long COVID. As evidence is emerging concerning the efficacy of early post-acute rehabilitation and therapeutic interventions, early ED identification supported by a clinical prediction rule, combined with appropriate outreach and health education, could contribute to alleviating the burden of the disease on health systems and positively impact the quality of life of those living with the post-COVID-19 condition. This study aimed to derive and validate a clinical prediction rule to identify adult ED patients at high risk of developing PCC three months after an acute infection. Methods and findingsThis derivation and validation study used data from an observational cohort recruited from 33 hospitals in five Canadian provinces participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN). We included adults (age [≥]18 years) with confirmed COVID-19 who presented to the ED of a participating site between October 18, 2020, and October 11, 2022. We randomly assigned participants to derivation (75%) or validation (25%) datasets, and prespecified clinical variables as candidate predictors. We used a fast step-down logistic regression to reduce the model to key predictors for our clinical prediction rule. Validation was planned only if the derived rule had an AUC of at least 80% to support clinically useful discrimination characteristics to separate those who will develop PCC from those who will not. Of 6,070 eligible patients, 2,511 (41.4%) reported PCC symptoms at three months. Our derived clinical prediction rule included nine risk factors (female sex, higher arrival respiratory rate, comorbidities (rheumatologic disorder and mental health condition), acute symptoms (sputum production, dizziness, diarrhea, chest pain, and fatigue)) and one protective factor (self-reported South Asian race). In derivation, the optimism-corrected area under the curve was 0.626 (95% confidence interval [CI] 0.610-0.643). Age and vaccination status were not retained in the final clinical prediction rule. The rule was only slightly better than chance and deemed not accurate enough to meaningfully guide decision-making in the ED. Therefore, we did not proceed to examine its performance in the validation cohort. ConclusionsDespite rigorous methodology, we were unable to derive a clinical prediction rule with sufficient accuracy to predict PCC in emergency department patients at the time of the acute infection. However, we did identify several factors associated with the development of PCC that can guide future studies about the causes of PCC. The ambiguous nature of the current PCC diagnostic criteria and the extended follow-up pose challenges for deriving a useful clinical decision rule. Further research integrating comprehensive surveillance systems and biomarker data may also enhance prediction accuracy and refine personalized management strategies in the emergency department setting.

This study combined public data and geospatial analysis to examine physicians language abilities and locations in Alberta, Canada, and produced an interactive map to allow patients and policymakers to view the data. We identified n=11,370 active physicians in the province of Alberta, of whom we further identified n=194 (1.7%) as University of Ottawa (uOttawa) graduates, n=955 (8.4%) as French-speaking, and n=4,965 (43.7%) as community-based family physicians. French-speaking physicians were concentrated in Census Division 6 (n=464, 48.6%) surrounding Calgary and Census Division 11 (n=356, 37.3%) surrounding Edmonton. Overall reported French-language ability was low, with just 955 (8.4%) of all active physicians reporting competency in French. uOttawa graduates (n=70, 36.1%) were much more likely to report French ability than graduates of other schools (n=885, 7.9%), women (n=457, 9.6%) were slightly more likely than men (n=497, 7.6%), and specialists (n=666, 10.4%) were more likely than family physicians (n=289, 5.8%).

Structured AbstractO_ST_ABSObjectiveC_ST_ABSTo track uptake of workplace SARS-CoV-2 testing programs using publicly-available data (e.g., press releases), supplementing findings from employer surveys. MethodsWe tracked testing programs reported by 1,159 Canadian and 1,081 international employers across sectors from March 1, 2020 to March 31, 2021. We analyzed trends in uptake of testing programs, including over time and by workplace setting. Results9.5% (n=110) of Canadian employers and 24.6% (n=266) of international employers tracked reported testing. The prevalence of reported testing programs was less than 20% in some settings associated with high risk of transmission including retail and customer-facing environments, and indoor and mixed blue collar workplaces. ConclusionsPublicly-available data suggest that fewer employers are testing than indicated by surveys. Workplace safety in high-risk workplaces could be further improved by implementing testing strategies that deploy both screening and diagnostic tests.

BackgroundStandardized training and competency testing is needed for appropriate point-of-care ultrasound (POCUS) clinical use. Our study objective assesses a low-fidelity simulation pig model workshop and tests the knowledge and technical skills of emergency medicine (EM) clinicians when performing simulated ultrasound-guided serratus anterior nerve block (UG-SANB). MethodsEM residents, attendings, and advanced practice providers (APP) participated in a prospective cohort study, completing a one-time simulation-based UG-NB training session at a single academic medical center between November 2024 to February 2025. Training model acceptability, appropriateness, and feasibility was assessed using the validated AIM-IAM-FIM tool (pre/post-surveys). Effectiveness outcomes were participant knowledge score, technical skill score, and self-rated confidence in performing NBs pre-, post-, and 3-months post-intervention. Clinical ED-performed ultrasound-guided nerve blocks were reported pre-/post-intervention. Scores were summarized using mean (S.D.) and total question percent correct. Paired individual assessments were compared pre/post-intervention using paired t-tests and group assessments using t-tests for normal data distribution. Results63/104 ED providers (60.6%) responded to surveys pre-intervention and 57 post-intervention (54.8%). 63 providers (16 EM attendings, 33 residents, and 14 APPs) underwent SANB training and testing. Participant survey responses reported the training model was acceptable, appropriate, and feasible (at least 54/57 agreed or strongly agreed for all three). Mean knowledge scores were 85% (SD 14.8%) post- and 70% (SD 18.2%) 3-months post-workshop. Mean technical skills exam scores were 98% (SD 4.5%) post- and 95% (5.8%) 3-months post-intervention. Perceived confidence in teaching clinical NBs increased pre-/post-intervention (from 11.3% to 58.2%) and for SANB (3.2% to 70.2%). Clinically performed NBs at pre and post were 21 and 15 respectively. ConclusionEmergency clinicians knowledge, technical skills, and confidence scores increased after an UG-NB training intervention. This standardized, reproducible simulation model could improve clinical skills and patient care outcomes but needs additional steps to increase clinical UG-NB performance.

BackgroundRural emergency departments (EDs) are critical to ensuring equitable access to acute care, yet face persistent systemic challenges. In Quebec, Canada, reforms to healthcare governance, funding and resource allocation, and service delivery have transformed rural ED operations. This study aimed to document characteristics, challenges, and improvement priorities for all rural EDs in the province. MethodsA participatory mixed-methods design was used. 26 rural EDs in Quebec were included. Data sources comprised administrative statistics, structured site surveys, individual stakeholder semi structured interviews, and a validation survey of identified local champions. Analyses comprised a triangulation of the quantitative and qualitative data using transversal thematic analysis to determine common issues. Potential solutions identified were validated through stakeholder questionnaires. The study was reported in accordance with the COREQ reporting guideline. ResultsMost respondents were women (64%) and professionals with more than 5 years of experience. Four main themes were identified: governance, healthcare organization, access to resources, and professional practice. Governance challenges included reduced local autonomy, administrative complexity, and budgeting models poorly adapted to rural realities. Participants emphasized the need for standardized but locally flexible administrative processes, regional emergency service managers, and rural-sensitive performance metrics. Organizational barriers included geographic isolation, limited access to primary care, and difficulties with interfacility transfers due to referral-center capacity and ambulance shortages. Resource constraints centered on shortages of human resources, diagnostic services and specialty coverage, especially anesthesia, obstetrics, and psychiatry. Professional practice was shaped by the need to maintain broad competencies in low-volume contexts, while contending with professional isolation and proximity to patients. Local champions prioritized expanding telemedicine, strengthening prehospital services, enhancing continuing education, and implementing tailored recruitment strategies. ConclusionThis study provides the first province-wide documentation of characteristics, challenges, and improvement priorities for all rural EDs. Findings highlight the need for systemic reforms that restore local decision-making authority, strengthen transfer and prehospital capacity, expand telehealth and specialty access, and support professional development. These results provide a foundation for evidence-based policies and actions to sustain equitable emergency care in rural regions.

Our EDs Discharge Center (EDDC) facilitates appointments and paper-based social determinants of health (SDoH) screening. No criteria guide EDDC utilization. The EDs provider-administrator-run, patient-satisfying follow-up call program contacts only [~]25% of discharges. We describe Learning Organization-principle-guided evaluation of EDDC efficiency, aiming to create EDDC time to expand the follow-up program. We reviewed appointment-making, SDoH-screening, and follow-up program data. We surveyed patients to determine whether adopting SHOUT tool criteria (no home, no primary care physician, or insurance) might yield more-judicious EDDC utilization. EDDC staffs 20 minutes/patient yielded fewer ED returns and admissions. Most patients improved post-discharge and made appointments themselves; 6% met SHOUT criteria for EDDC assistance; 4.5% would benefit from SDoH screening. Adopting SHOUT criteria would create significant time for EDDC-staffed follow-up program expansion. QR-code-accessible SDoH surveys would screen thousands more patients, minimizing EDDC staff involvement, saving 95% of the effort while retaining 100% of the benefit.

ImportanceHospital capacity constraints and rising healthcare costs necessitate innovative models for delivering acute care. While various hospital-substitution models exist, challenges in scalability and long-term viability persist. ObjectiveTo evaluate the feasibility and safety of a novel, high-acuity Next Day Clinic (NDC) as an alternative to hospitalization for select acutely ill emergency department (ED) patients. Design, Setting, and ParticipantsRetrospective matched cohort study of patients referred to the NDC between July 1, 2023-July 31, 2024, matched to patients seen in the ED during the year prior to NDC launch, within a large academic safety-net hospital. InterventionHigh-acuity outpatient therapy for one or more consecutive days in the NDC, consisting of daily IV antibiotics or diuretics, STAT labs, and rapid turnaround imaging and cardiodiagnostics. Main Outcomes and MeasuresDays alive and out of hospital (DAOH) in the 30 days following the index ED visit. Secondary outcomes were the number of hospital bed-days avoided, as well as 30- day ED revisits, hospital readmissions, and mortality. ResultsThe NDC had 1009 encounters (mean age, 54.4 years [SD 14.6]; 448 female [44%]) during the study period, 420 (42%) of which were referred from the ED. Of these, 298 (71%) matched to 4666 ED visits (mean age, 53.3 years [SD 15.2]; 2019 female [43%]) in the year prior to NDC launch on age, sex, the first set of laboratory and vital sign data obtained in the ED (i.e., presenting illness severity), and an exact match on primary diagnosis group. Unadjusted mean DAOH in the NDC cohort was 29.5 days (SD 2.3) compared to 24.9 days (SD 5.5) in the control cohort. Adjusting for the same features in the matching algorithm showed NDC treatment was associated with an average of 3.85 (SD 0.20) more DAOH compared to hospitalization (p<0.001), translating to 358-1294 hospital bed-days saved over the study period. NDC patients had significantly higher rates of 30-day ED revisits per 100 encounters (20.5 versus 13.0, p<0.001), but significantly lower rates of 30-day hospital readmissions per 100 encounters (5.7 versus 11.0, p<0.001) and morality (0% versus 0.9%, p<0.001). Conclusions and RelevanceThe NDC is a feasible and safe alternative to hospitalization, and promising strategy for managing ED and hospital capacity and reducing healthcare expenditures. KEY POINTSO_ST_ABSOuestionC_ST_ABSIs a high-acuity Next Day Clinic (NDC) a feasible and safe alternative to hospitalization for acutely ill emergency department (ED) patients? FindingsIn this matched cohort study of 1009 NDC encounters, 298 hospital admission avoidance referrals were matched with 4666 historical controls. Each avoided hospitalization through the NDC was associated with an average of 3.85 more days alive and out of the hospital over 30 days, lower readmissions and mortality, and a total of 358-1294 hospital bed-days saved. MeaningA centralized, high-acuity outpatient clinic may safely substitute for hospitalization, reducing hospital capacity strain and healthcare expenditures.

As of March 23, 2020 there have been over 354,000 confirmed cases of coronavirus disease 2019 (COVID-19) in over 180 countries, the World Health Organization characterized COVID-19 as a pandemic, and the United States (US) announced a national state of emergency.1, 2, 3 In parts of China and Italy the demand for intensive care (IC) beds was higher than the number of available beds.4, 5 We sought to build an accessible interactive model that could facilitate hospital capacity planning in the presence of significant uncertainty about the proportion of the population that is COVID-19+ and the rate at which COVID-19 is spreading in the population. Our approach was to design a tool with parameters that hospital leaders could adjust to reflect their local data and easily modify to conduct sensitivity analyses. We developed a model to facilitate hospital planning with estimates of the number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. Inputs to the model include estimates of the characteristics of the patient population and hospital capacity. We deployed this model as an interactive online tool.6 The model is implemented in R 3.5, RStudio, RShiny 1.4.0 and Python 3.7. The parameters used may be modified as data become available, for use at other institutions, and to generate sensitivity analyses. We illustrate the use of the model by estimating the demand generated by COVID-19+ arrivals for a hypothetical acute care medical center. The model calculated that the number of patients requiring an IC bed would equal the number of IC beds on Day 23, the number of patients requiring a ventilator would equal the number of ventilators available on Day 27, and the number of patients requiring an AC bed and coverage by the Medicine Service would equal the capacity of the Medicine service on Day 21. In response to the COVID-19 epidemic, hospitals must understand their current and future capacity to care for patients with severe illness. While there is significant uncertainty around the parameters used to develop this model, the analysis is based on transparent logic and starts from observed data to provide a robust basis of projections for hospital managers. The model demonstrates the need and provides an approach to address critical questions about staffing patterns for IC and AC, and equipment capacity such as ventilators.

ObjectivesMobile health clinics (MHCs) provide flexible, community-based care to underserved populations facing geographic and socioeconomic barriers. Maximizing coverage enables MHCs to reach more individuals, improve preventive and continuous care, and reduce health disparities. However, few strategies exist to guide placement and routing decisions. We present a framework to increase MHC utilization by optimizing service coverage. Study DesignThis is a retrospective study. MethodsWe analyzed MHC deployments for Hepatitis C Virus (HCV) screening and treatment from a local health system in South Carolina. We used a location-allocation model to identify potential MHC placement sites that maximized the number of uninsured residents within a 5-minute drive or 10-minute walk. Demand was represented by block centroids weighted by the size of the uninsured population. We compared service area population, defined as the size of the target population within driving or walking distance, for model-proposed sites with coverage from previous MHC deployments. We fit negative binomial mixed effects models to evaluate the association between service area population and MHC utilization. ResultsOptimized placements can nearly double population coverage, expanding access to uninsured residents within practical travel distances by 90% for driving and 135% for walking--without requiring additional vehicles or resources. This approach also substantially reduces redundant service areas while shortening average travel times. Results show that small geographic shifts can yield significant improvements. In rural regions, greater geographic coverage is significantly associated with higher MHC utilization for HCV screening (drive p=.0037; walk p=.0095). We applied this framework with local health partners to guide real-world MHC deployment in South Carolina. ConclusionsThis framework connects spatial analytics to service delivery, offering a replicable, operationally ready tool adaptable to various travel modes, site types, and disease contexts. It supports strategic placement in high-need locations by reducing travel time and service redundancy and ultimately improving health outcomes in medically underserved populations.

BackgroundSince the emergence of SARS-CoV-2, health care workers (HCWs) have been on the front line in caring for COVID-19 patients. Better knowledge of risk factors for SARS-CoV-2 infection is crucial for the prevention of disease among this population. MethodsWe conducted a seroprevalence survey among HCWs in a French university hospital after the first wave (May-June 2020), based on a validated lateral flow immuno-assay test (LFIAT) for SARS-CoV-2. Demographic characteristics as well as data on the working characteristics of COVID-19 and non-COVID-19 wards and 23 care activities were systematically recorded. The effectiveness of protective equipment was also estimated, based on self-declaration of mask use. SARS-CoV-2 IgG status was modelled by multiple imputations approach, accounting for the performance of the test and data on serum validation ELISA immunoassay. FindingsAmong the 3,234 enrolled HCWs, the prevalence of SARS-CoV-2 IgG was 3.8%. Contact with relatives or HCWs who developed COVID-19 were risk factors for SARS-CoV-2 infection, but not contact with COVID-19 patients. In multivariate analyses, suboptimal use of protective equipment during naso-pharyngeal sampling, patient mobilisation, clinical and eye examination was associated with SARS-CoV-2 infection. In addition, patients washing and dressing and aerosol-generating procedures were risk factors for SARS-CoV-2 infection with or without self-declared appropriate use of protective equipment. InterpretationMain routes of transmission of SARS-CoV-2 IgG among HCWs were i) contact with relatives or HCWs with COVID-19, ii) close or prolonged contact with patients, iii) aerosol-generating procedures.

BackgroundOn June 6, 2011 the Emergency Department (ED) at Southlake Regional Health Center, a very high-volume ED, initiated a comprehensive redesign project to improve patient waiting times. The primary initial goal of the project was to reduce Time to Physicians Initial Assessment (TPIA) - one of the Key Performance Indicators (KPIs) tracked by the Ontario Ministry of Health and Long-Term Care. The objective was to achieve a significant improvement in TPIA without sacrificing performance on any other important KPIs such as Length of Stay (LOS), Left Without Being Seen (LWBS), or time to admission (T2A). The effect on TPIA was immediate and dramatic: the 90-th percentile TPIA declining from 4 hrs to under 2.5 hrs, with further improvements seen over time. The patient in-flows also increased; anecdotally this increase was directly related to shorter wait time. However, like any other large-scale and on-going system redesign project, the impacts are not limited to the listed KPIs, but are multi-dimensional, affecting patient inflows, flows within the ED, workloads, staffing levels, etc. Thus, teasing out the impact of system redesign requires from other concurrent factors (population changes, staffing changes, etc.) requires a comprehensive system assessment. The available data exhibits auto-correlations, heteroscedasticity, and interdependence among variables, rendering simple statistical analysis of individual KPIs inapplicable. We develop a novel methodology and conduct counterfactual analysis demonstrating that the decrease in TPIA, as well as new patient inflows can indeed be attributed to the ED redesign. This suggests that a similar system redesign should be considered by other EDs looking to improve wait times. ObjectivesTo (1) statistically estimate the impacts of the redesign project on various performance measures over time, (2) examine whether the projects initial goal of improvement in TPIA without compromising other service performance measures was achieved, and (3) study whether the project impacted patient inflows. MethodsWe (1) estimate simultaneous equations models to quantify interdependent and timevarying relations among variables, (2) conduct an iterative counterfactual analysis to estimate the mean-level impacts of the project, and (3) construct 95% confidence intervals for the estimated impacts using the Bootstrap method. ResultsWe study project impacts over 720 days after it was initiated. During this time, the 90th percentile of TPIA has been reduced by nearly 2.5 hours on average (translating into an over 50% improvement), with continuous improvement over the study period. This effect is statistically and operationally significant. The project also improved LOS for non-admitted patients (both acute and non-acute), and did not have statistically significant impact on LOS for admitted patients. There was also a decrease in LWBS, though it was not statistically significant. Thus the project achieved its stated primary goals. We also observed an increase in inflows of both acute and nonacute patients; our analysis confirms that this increase can be attributed to the project, indicating that improvements in TPIA attracted new patients to the ED. All of these effects have persisted over the 720-day post-project period. ConclusionsThe redesign project has significantly reduced TPIA over time while also improving some LOS measures; none of the waiting time KPIs were compromised. The reduction in TPIA also attracted significant volumes of new patients. However, the redesigned process was able to deal with this volume without compromising performance. The redesign project involved a number of major changes in ED operations. We provide an overview of these changes, and while our analysis cannot attribute specific project impacts to specific changes, we believe that implementing similar changes should receive strong consideration by other EDs. Conflicts of interestNone

BackgroundThe global spread of coronavirus disease 2019 (COVID-19) continues in several jurisdictions, causing significant strain to healthcare systems. The purpose of our study is to predict the impact of the COVID-19 pandemic on patient outcomes and the healthcare system in Ontario, Canada. MethodsWe developed an individual-level simulation to model the flow of COVID-19 patients through the Ontario healthcare system. We simulated different combined scenarios of epidemic trajectory and healthcare capacity. Outcomes include numbers of patients needing admission to the ward, Intensive Care Unit (ICU), and requiring ventilation; days to resource depletion; and numbers of patients awaiting resources and deaths associated with limited access to resources. FindingsWe demonstrate that with effective early public health measures system resources need not be depleted. For scenarios considering late or ineffective implementation of physical distancing, health system resources would be depleted within 14-26 days. Resource depletion was also avoided or delayed with aggressive measures to rapidly increase ICU, ventilator, and acute care hospital capacity. InterpretationWe found that without aggressive physical distancing measures the Ontario healthcare system would have been inadequately equipped to manage the expected number of patients with COVID-19, despite the rapid capacity increase. This overall lack of resources would have led to an increase in mortality. By slowing the spread of the disease via ongoing public health measures and having increased healthcare capacity, Ontario may have avoided catastrophic stresses to its health care system.

As SARS-CoV-2 threatens to overwhelm health systems in Canada, it is imperative that provinces are able to plan and manage an effective and reduced risk response. For this response to be most effective, it must reflect an evidence-based, pan-Canadian response. We designed four different prototypical patients with a combination of common COVID-19 symptoms and opportunities for exposure who were made to self-assess using the 10 provincial COVID-19 self-assessment tools on 1 April. These tools were developed to allow individuals to self-triage, allowing health systems direct capacity to testing and care. We assessed the consistency of the self-assessment tools and of the guidance provided to the patients. While the tools generally screen in three areas, the scope of included COVID-19 associated symptoms as well as the opportunities for exposure, and therefore transmission, vary between provinces such that no two provinces screened in the same way. This was, in turn, reflected in the inconsistency in guidance found. A patient with cough who had travelled abroad or had close contact with a confirmed case within 14 days received the most consistent guidance, with remaining patients receiving guidance ranging from mandatory quarantine or self-isolation to being told they did not have COVID-19 symptoms, guidance at odds with medical evidence. Thus, there is not a single, evidence-based Canadian standard of care simply for self-assessment. Without consistency in public health guidance, Canadians cannot appropriately self-isolate to mitigate community transmission, nor can the necessary valid and reliable data be collected to inform critical epidemiological models that help guide pandemic response. If federal and provincial governments are unable to coordinate a response, Parliament must use its available jurisdiction to legislate a duty on both to follow national standards, so as to improve coordination on COVID-19 in coming months.

BackgroundAdvanced Practice Providers (APPs), including physician assistants and nurse practitioners, represent a growing proportion of the emergency medicine workforce, including in high-acuity community emergency departments (EDs). Despite this growth, many sites lack formal onboarding structures, particularly for new graduate or inexperienced APPs transitioning to practice. Unlike postgraduate residencies and fellowships, limited literature exists on structured onboarding models outside academic settings. This study evaluated the feasibility and perceived impact of a structured onboarding program for APPs in a non-academic community ED. MethodsThis mixed-methods feasibility study was conducted at a single-site community ED without an existing formal onboarding process. New graduate or inexperienced APPs hired within 12 months of program implementation completed a post-intervention survey assessing satisfaction across five domains derived from a conceptual framework of human resource practices and retention. Quantitative data was collected using 5-point Likert-scale items, and qualitative data was obtained through open responses. Leadership and preceptors completed a secondary survey evaluating feasibility and perceived impact. Descriptive statistics and thematic analysis were performed. ResultsFour new graduate APPs (100% response rate) completed the post-implementation survey. Mean scores across domains ranged from 3.33 to 5.00, with highest ratings observed in supervisor support (mean = 5.00), employee engagement (4.33), and alternative training via online modules (4.67). Qualitative themes included clear communication of expectations, value of asynchronous educational modules, and strong mentorship support. Fifteen leaders and preceptors reported that although the program required additional effort, it improved tracking of APP progress, preparedness for transition to practice (4.67), and was perceived as worthwhile to reduce attrition. ConclusionsA structured onboarding program for new graduate APPs in a community ED was feasible, well accepted, and perceived to support transition to practice. These findings support the need for further study of structured onboarding as a scalable strategy to enhance preparedness, engagement, and potential retention in high-acuity clinical settings.