Characterizing the impact of snowfall on patient attendance at an urban emergency department in Toronto, Canada

Shah, Sparsh; Murray, Joshua; Mamdani, Muhammad; Vaillancourt, Samuel

doi:10.1016/j.ajem.2019.05.032

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…For example, all respiratory and cardiovascular diagnoses combined account for less than 1 in 5 principal diagnoses in our sample, and thus, any increases in the rates of these diagnoses at high smoke intensities are dominated by the accompanying decreases in more common conditions, resulting in a net decline in total ED visits. These competing effects are not well understood, although short-term reductions in hospitalizations have been noted in response to wildfire smoke ( 32 ) and following exposure to other environmental stressors such as hurricanes and snowstorms ( 6 , 19 – 21 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, when environmental conditions are sufficiently hazardous, people may purposefully or inadvertently alter their behavior in ways that shape health outcomes. Previous work has documented that changes in health seeking behavior lead to fewer ED visits on snowy days and during hurricanes ( 6 , 19 – 21 ). In the case of air pollution, research from multiple settings has shown that when individuals are informed of high ambient pollution exposures, they alter their behavior in an effort to reduce exposures and that these efforts shape health outcomes ( 22 – 24 ).…”

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confidence: 99%

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Emergency department visits respond nonlinearly to wildfire smoke

Heft-Neal,

Gould,

Childs

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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Air pollution negatively affects a range of health outcomes. Wildfire smoke is an increasingly important contributor to air pollution, yet wildfire smoke events are highly salient and could induce behavioral responses that alter health impacts. We combine geolocated data covering all emergency department (ED) visits to nonfederal hospitals in California from 2006 to 2017 with spatially resolved estimates of daily wildfire smoke PM 2.5 concentrations and quantify how smoke events affect ED visits. Total ED visits respond nonlinearly to smoke concentrations. Relative to a day with no smoke, total visits increase by 1 to 1.5% in the week following low or moderate smoke days but decline by 6 to 9% following extreme smoke days. Reductions persist for at least a month. Declines at extreme levels are driven by diagnoses not thought to be acutely impacted by pollution, including accidental injuries and several nonurgent symptoms, and declines come disproportionately from less-insured populations. In contrast, health outcomes with the strongest physiological link to short-term air pollution increase dramatically in the week following an extreme smoke day: We estimate that ED visits for asthma, COPD, and cough all increase by 30 to 110%. Data from internet searches, vehicle traffic sensors, and park visits indicate behavioral changes on high smoke days consistent with declines in healthcare utilization. Because low and moderate smoke days vastly outweigh high smoke days, we estimate that smoke was responsible for an average of 3,010 (95% CI: 1,760–4,380) additional ED visits per year 2006 to 2017. Given the increasing intensity of wildfire smoke events, behavioral mediation is likely to play a growing role in determining total smoke impacts.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Emergency department visits respond nonlinearly to wildfire smoke

Heft-Neal,

Gould,

Childs

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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“…Therefore, weather factors can be used as effective additional variables for the demand forecast of healthcare services. For example, in short-term ED volume forecasting, the snowfall variable was added to the forecasting model, resulting in improved model performance [19]. In the formulation of predicting the ED patients, the ambient temperature was considered and proved to have differential effects on ED patient visits of different specialties and severities [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Teleconsultation Demand Using an Ensemble CNN Attention-Based BILSTM Model with Additional Variables

Chen

2021

Healthcare

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To enhance the forecasting accuracy of daily teleconsultation demand, this study proposes an ensemble hybrid deep learning model. The proposed ensemble CNN attention-based BILSTM model (ECA-BILSTM) combines shallow convolutional neural networks (CNNs), attention mechanisms, and bidirectional long short-term memory (BILSTM). Moreover, additional variables are selected according to the characteristics of teleconsultation demand and added to the inputs of forecasting models. To verify the superiority of ECA-BILSTM and the effectiveness of additional variables, two actual teleconsultation datasets collected in the National Telemedicine Center of China (NTCC) are used as the experimental data. Results showed that ECA-BILSTMs can significantly outperform corresponding benchmark models. And two key additional variables were identified for teleconsultation demand prediction improvement. Overall, the proposed ECA-BILSTM model with effective additional variables is a feasible promising approach in teleconsultation demand forecasting.

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“…Interest in radiology applications for AI has skyrocketed over the past few years [35]. Although emergency radiology may benefit from other types of AI such as automatic segmentation of structures [36], ventricular volume measurements for hydrocephalus [37], predicting ED patient volume-which is a driver of ED imaging volume [26,38]-or for protocolling studies [39], the main applications for the ED are for diagnosis [40]. This currently takes the form of flagging studies for worklist prioritization or as a double-check to catch radiologist errors (i.e., "misses") rather than as a radiologist replacement [41].…”

Section: What Is the Role For Artificial Intelligence (Ai) In Ed Radiology Workflow?mentioning

confidence: 99%

Update on establishing and managing an overnight emergency radiology division

Scheinfeld

Dym

2021

Emerg Radiol

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Emergency department (ED) radiology divisions that serve to provide overnight attending coverage have become an increasingly common feature of radiology departments. The purpose of this article is to review the common ED radiology coverage models, describe desirable traits of emergency radiologists, and discuss workflow in the ED radiology setting. ED radiologists may be trained as ED radiologists or may develop the necessary skills and adopt the subspecialty. Choosing radiologists with the correct traits such as being a “night owl” and remaining calm under pressure and implementing an acceptable work schedule such as shift length of 9–10 h and a “one week on, two weeks off” schedule contribute to sustainability of the position. Strategies to address the unique stressors and workflow challenges of overnight emergency radiology coverage are also presented. Workflow facilitators including trainees, PAs, radiology assistants, and clerks all have roles to play in managing high case volumes and in making sure that the service is well staffed. Usage of artificial intelligence software is the latest technique to streamline workflow by identifying cases which should be prioritized on a busy worklist. Implementing such strategies will maintain quality of care for patients regardless of time of day as well as sustainability and quality of life for overnight emergency radiologists.

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Characterizing the impact of snowfall on patient attendance at an urban emergency department in Toronto, Canada

Cited by 5 publications

References 9 publications

Emergency department visits respond nonlinearly to wildfire smoke

Emergency department visits respond nonlinearly to wildfire smoke

Forecasting Teleconsultation Demand Using an Ensemble CNN Attention-Based BILSTM Model with Additional Variables

Update on establishing and managing an overnight emergency radiology division

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