Emergency Department Demand Research Articles

The rate, causes and predictors of ambulance call outs to residential aged care in the Australian Capital Territory: A retrospective observational cohort study.

Older people in residential aged care are susceptible to acute illness or injury which may necessitate an ambulance call out, assessment/treatment by a paramedic and transfer to a hospital emergency department. Understanding the case mix of residential aged care ambulance attendances is important for prevention strategies and for planning services. A retrospective observational closed cohort study was designed to investigate the characteristics of emergency ambulance call outs to 15 residential aged care sites in the Australian Capital Territory over a 12-month period. Data were collected from the local ambulance service and the aged care sites. Case load data were analysed to determine rates, clinical characteristics, ambulance attendance outcomes and the temporal distribution of call outs. A Poisson regression model was developed to investigate demographic, morbidity and medication-related risk factors associated with the number of ambulance call outs per resident. Annual ambulance call out costs were estimated. There were 1,275 residents, with 396 (31.1%) requiring at least one ambulance call out over 12 months. Of 669 ambulance attendances, the majority (87.0%) were transported to emergency departments. Trauma (23.9%), pain (16.9%) and infections (9.4%) were the most common primary assessments by the ambulance attendees. Cases/day were similar throughout the year and on weekdays compared to weekends/public holidays. The main predictors of ambulance call out were multi-morbidity, taking regular anticholinergic medicines, being male and younger age. Estimated costs of ambulance call outs/year were $475/resident and $40,375/residential aged care site. The most frequent primary assessments (trauma, pain, infections) may constitute priorities for developing prevention strategies and for treatment initiatives within residential aged care. Strategies to reduce anticholinergic medication prescribing may also be a potential intervention to decrease ambulance call outs and hospital emergency department demand. The ambulance usage data from this study may be useful to compare with future datasets to measure the impact of the introduction of new services.

The Impact of Increasing Entry Fee on Emergency Department Demand: A Territory-Wide Study

The Impact of Increasing Entry Fee on Emergency Department Demand: A Territory-Wide Study

Emergency Department Demand and the First Year of the COVID-19 Pandemic.

We present a retrospective analysis of Emergency Department daily patient flow across 84 hospitals in Queensland, Australia over a four-year period from 2017 - 2020, leading up to and including the start of the COVID-19 pandemic. Daily ED demand significantly increased year-on-year over the study period, though significant increases in 2020 were likely attributed to ED fever screening clinics. Compliance against a four-hour ED Length of Stay target had been slightly decreasing since 2017, and the first year of the pandemic showed significant improvements in target compliance compared to previous years for all patients including the cohort admitted from ED. The length of stay for ED patients was also significantly less in 2020 (mean = 3.1 hours) compared to previous years. As an area of topical interest, a special focus on influenza-like illness presentations to ED helps quantify changes in volume of this cohort. This knowledge assists hospitals in planning and responding to variations in hospital demand.

Assessing the reach and safety of an innovative urgent care service tailored to older adults.

Traditional emergency departments (EDs) are overcrowded and sometimes not suitable for older adults with complex needs. Specialised geriatric urgent care pathways for selected patients can alleviate ED demand and improve patient experience. To address urgent care needs for older adults in Southern Adelaide, the Complex And RestorativE (CARE) service was established. CARE offers alternative, geriatrician-led treatment pathways consisting of a ward-based treatment centre and an in-home visiting team called Eyes on Scene (EoS). Both pathways offer medical, nursing and allied health treatment. Routinely collected clinical data were analysed to explore the reach of the service and the incidence and nature of adverse events. Between September 2021 and March 2023, the CARE service attended to 5324 older adults requiring urgent care. A significant proportion of patients were discharged to or remained in their regular place of residence, with few requiring inpatient admissions or referrals to other facilities. A total of 7% of patients required transfer to ED and adverse events were rare. The CARE service demonstrates a feasible and safe alternative model of urgent care for older Australians.

Evaluation of the priority primary care centre program to reduce emergency department burden in regional Victoria, Australia: a mixed-method study

IntroductionIn Australia, the Victorian State Government has established a number of priority primary care centres (PPCCs) across the state to address the increasing demand for emergency departments (EDs). PPCCs are...

CrowdQ

Hospital Emergency Departments (EDs) are essential for providing emergency medical services, yet often overwhelmed due to increasing healthcare demand. Current methods for monitoring ED queue states, such as manual monitoring, video surveillance, and front-desk registration are inefficient, invasive, and delayed to provide real-time updates. To address these challenges, this paper proposes a novel framework, CrowdQ, which harnesses spatiotemporal crowdsensing data for real-time ED demand sensing, queue state modeling, and prediction. By utilizing vehicle trajectory and urban geographic environment data, CrowdQ can accurately estimate emergency visits from noisy traffic flows. Furthermore, it employs queueing theory to model the complex emergency service process with medical service data, effectively considering spatiotemporal dependencies and event context impact on ED queue states. Experiments conducted on large-scale crowdsensing urban traffic datasets and hospital information system datasets from Xiamen City demonstrate the framework's effectiveness. It achieves an F1 score of 0.93 in ED demand identification, effectively models the ED queue state of key hospitals, and reduces the error in queue state prediction by 18.5%-71.3% compared to baseline methods. CrowdQ, therefore, offers valuable alternatives for public emergency treatment information disclosure and maximized medical resource allocation.

Impact of ridesharing platforms on hospitals' emergency department admissions

This paper examines the impact of information technology-based ridesharing platforms on hospitals' emergency department (ED) admissions. Employing a difference-in-differences design, we analyze patient-level data for 42 million ED visits in Florida. We find that the availability of ridesharing positively affects ED arrivals, indicating enhanced accessibility. Our analysis of the heterogeneous impact of ridesharing suggests the most significant effect applies to young, middle-aged, low-income, and non-critical patients. Additionally, we find a significant increase in ED service time for patients with less critical conditions and no change in visit length for critical patients. The rise in ED demand due to ridesharing entry can have two distinct implications. On the one hand, ridesharing entry addresses the access barrier when the patient's condition is non-critical, but ED usage is necessary. This is a positive impact of ridesharing services availability, especially since it helps low-income communities the most. Therefore, hospitals and ridesharing companies can collaborate to address the healthcare access challenge. On the other hand, ridesharing entry may exacerbate already overcrowded EDs by facilitating non-critical and unnecessary ED usage, which can be an adverse effect. In this case, effective patient triage to identify those with urgent needs may become even more essential after ridesharing entry.

Frequency and Trends of Pre-Pandemic Surge Periods in U.S. Emergency Departments, 2006-2019.

To quantify the frequency, outside of the pandemic setting, with which individual healthcare facilities faced surge periods due to severe increases in demand for emergency department (ED) care. Retrospective cohort study. U.S. EDs. All ED encounters in the all-payer, nationally representative Nationwide Emergency Department Sample from the Healthcare Cost and Utilization Project, 2006-2019. None. Frequency of surge periods defined as ED months in which an individual facility ED saw a greater than 50% increase in ED visits per month above facility-/calendar month-specific medians. During 2006-2019, 3,317 U.S. EDs reported 354,534,229 ED visits across 142,035 ED months. Fifty-seven thousand four hundred ninety-five ED months (40.5%) during the study period had a 0% to 50% increase in ED visits that month above facility-specific medians and 1,952 ED months (1.4%) qualified as surge periods and had a greater than 50% increase in ED visits that month above facility-specific medians. These surge months were experienced by 397 unique facility EDs (12.0%). Compared with 2006, the most proximal pre-pandemic period of 2016-2019 had a notably elevated likelihood of ED-month surge periods (odds ratios [ORs], 2.36-2.84; all p < 0.0005). Compared with the calendar month of January, the winter ED months in December through March have similar likelihood of an ED-month qualifying as a surge period (ORs, 0.84-1.03; all p > 0.05), while the nonwinter ED months in April through November have a lower likelihood of an ED-month qualifying as a surge period (ORs, 0.65-0.81; all p < 0.05). Understanding the frequency of surges in demand for ED care-which appear to have increased in frequency even before the COVID-19 pandemic and are concentrated in winter months-is necessary to better understand the burden of potential and realized acute surge events and to inform cost-effectiveness preparedness strategies.

Forecasting emergency department waiting time using a state space representation.

The provision of waiting time information in emergency departments (ED) has become an increasingly popular practice due to its positive impact on patient experience and ED demand management. However, little scientific attention has been given to the quality and quantity of waiting time information presented to patients. To improve both aspects, we propose a set of state space models with flexible error structures to forecast ED waiting time for low acuity patients. Our approach utilizes a Bayesian framework to generate uncertainties associated with the forecasts. We find that the state-space models with flexible error structures significantly improve forecast accuracy of ED waiting time compared to the benchmark, which is the rolling average model. Specifically, incorporating time-varying and correlated error terms reduces the root mean squared errors of the benchmark by 10%. Furthermore, treating zero-recorded waiting times as unobserved values improves forecast performance. Our proposed model has the ability to provide patient-centric waiting time information. By offering more accurate and informative waiting time information, our model can help patients make better informed decisions and ultimately enhance their ED experience.

Use of Real-Time Information to Predict Future Arrivals in the Emergency Department.

We aimed to build prediction models for shift-level emergency department (ED) patient volume that could be used to facilitate prediction-driven staffing. We sought to evaluate the predictive power of rich real-time information and understand 1) which real-time information had predictive power and 2) what prediction techniques were appropriate for forecasting ED demand. We conducted a retrospective study in an ED site in a large academic hospital in New York City. We examined various prediction techniques, including linear regression, regression trees, extreme gradient boosting, and time series models. By comparing models with and without real-time predictors, we assessed the potential gain in prediction accuracy from real-time information. Real-time predictors improved prediction accuracy on models without contemporary information from 5% to 11%. Among extensive real-time predictors examined, recent patient arrival counts, weather, Google trends, and concurrent patient comorbidity information had significant predictive power. Out of all the forecasting techniques explored, SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogenous factors) achieved the smallest out-of-sample the root mean square error (RMSE) of 14.656 and mean absolute prediction error (MAPE) of 8.703%. Linear regression was the second best, with out-of-sample RMSE and MAPE equal to 15.366 and 9.109%, respectively. Real-time information was effective in improving the prediction accuracy of ED demand. Practice and policy implications for designing staffing paradigms with real-time demand forecasts to reduce ED congestion were discussed.

Impact of a high-resolution primary care emergency center over emergency department demand

Impact of a high-resolution primary care emergency center over emergency department demand

Delivery system emergency department capacity and its effect on nonsystem service utilization.

Emergency department (ED) use is often seen as a source of excess health care spending, prompting managers to limit ED capacity in their health systems. However, if limited ED capacity in a delivery system leads patients to seek emergency care elsewhere, then health care quality and efficient management may be compromised within the system. The objective of this study was to explore the effect of the Veterans Health Administration (VHA) in-house ED clinician capacity on VHA community care (CC) ED claims. We used administrative data from the VHA to identify CC ED claims and Department of Veterans Affairs emergency physician (EP) capacity for 2014-2019. We used quasi-experimental instrumental variables approach with two different instruments: percent weekday federal holidays and VHA EP full-time equivalents (FTEs). We controlled for VHA ED variables such as ED wait times (door to triage, door to doctor, and door to admission) and demand variables such as alternative insurance coverage, driving time to VHA care, and demographic variables (employment, age, household income, race, gender, and VHA priority status). After instrumenting for capacity with percent weekday federal holidays, we found that one clinic-day capacity (one 8-h ED shift) per 10,000 enrollees increase at the VHA ED will result in a reduction of 61 CC ED claims per 10,000 enrollees. After instrumenting for capacity with EP FTE, we found that one clinic-day capacity (one 8-h ED shift) per 10,000 enrollees increase at the VHA ED will result in a reduction of 48 CC ED claims per 10,000 enrollees. Both of these results are statistically significant at p< 0.001. Our findings imply that offering more in-house ED care, in the form of clinician capacity, can substantially reduce out-of-system ED use. The results may be of interest to integrated health care system managers who prefer their patients to stay within network.

A multi-granular stacked regression for forecasting long-term demand in Emergency Departments

BackgroundIn the United Kingdom, Emergency Departments (EDs) are under significant pressure due to an ever-increasing number of attendances. Understanding how the capacity of other urgent care services and the health of a population may influence ED attendances is imperative for commissioners and policy makers to develop long-term strategies for reducing this pressure and improving quality and safety.MethodsWe developed a novel multi-granular stacked regression (MGSR) model using publicly available data to predict future mean monthly ED attendances within Clinical Commissioning Group regions in England. The MGSR combines measures of population health and health service capacity in other related settings. We assessed model performance using the R-squared statistic, measuring variance explained, and the Mean Absolute Percentage Error (MAPE), measuring forecasting accuracy. We used the MGSR to forecast ED demand over a 4-year period under hypothetical scenarios where service capacity is increased, or population health is improved.ResultsMeasures of service capacity explain 41 ± 4% of the variance in monthly ED attendances and measures of population health explain 62 ± 22%. The MGSR leads to an overall improvement in performance, with an R-squared of 0.79 ± 0.02 and MAPE of 3% when forecasting mean monthly ED attendances per CCG. Using the MGSR to forecast long-term demand under different scenarios, we found improving population health would reduce peak ED attendances per CCG by approximately 1000 per month after 2 years.ConclusionCombining models of population health and wider urgent care service capacity for predicting monthly ED attendances leads to an improved performance compared to each model individually. Policies designed to improve population health will reduce ED attendances and enhance quality and safety in the long-term.

Effect of the COVID-19 pandemic on the frequency of emergency department visits in Portugal: An interrupted time series analysis until July 2021.

This study aims to evaluate the effect of the COVID-19 pandemic on the frequency of emergency department (ED) visits in Portugal between March 2020 and July 2021. We used data on the monthly number of visits for all public hospitals' EDs from mainland Portugal between January 2017 and July 2021. We studied the impact of the pandemic overall, by type of ED (general, pediatric, and obstetric) and by Manchester Triage System color (red, orange, yellow, green, and blue) using an interrupted time series analysis. The prepandemic period corresponded to the months from January 2017 to February 2020 and the pandemic period to the months from March 2020 to July 2021. We observed over 26 million ED visits, the majority in general EDs (74.0%) and triaged yellow (48.4%) or green (38.4%). During the pandemic period, ED visits decreased 45.7% (95% confidence interval [CI]: -39.8% to -51.2%) and pediatric ED visits decreased by 72.4% (95% CI: -64.6% to -78.6%). A decrease was observed for all colors but tended to be progressively smaller as the priority increased. There was an increase in ED visits during the pandemic period (2.3%; 95% CI: 1.4% to 3.2%), eventually returning to prepandemic values. Our data indicate a considerable and long-lasting effect of the COVID-19 pandemic affecting mainly pediatric and milder cases, which were returning toward prepandemic values as the pandemic progressed. In a country with frequent use of EDs, the health system may need to be prepared to respond to prepandemic baseline ED demand, together with additional demand because of long-term sequels of COVID-19 cases and delayed care for chronic and acute conditions.

The effect of increased emergency department demand on throughput times and disposition status for pediatric psychiatric patients

ObjectivesEmergency department (ED) crowding has been shown to increase throughput measures of length of stay (LOS), wait time, and boarding time. Psychiatric utilization of the ED has increased, particularly among younger patients. This investigation quantifies the effect of ED demand on throughput times and discharge disposition for pediatric psychiatric patients in the ED. MethodsUsing electronic medical record data from 1,151,396 ED visits in eight North Carolina EDs from January 1, 2018, through December 31, 2020, we identified 14,092 pediatric psychiatric visits. Measures of ED daily demand rates included overall occupancy as well as daily proportion of non-psychiatric pediatric patients, adult psychiatric patients, and pediatric psychiatric patients. Controlling for patient-level factors such as age, sex, race, insurance, and triage acuity, we used linear regression to predict throughput times and logistic regression to predict disposition status. We estimated effects of ED demand by academic versus community hospital status due to ED and inpatient resource differences. ResultsMost ED demand measures had insignificant or only very small associations with throughput measures for pediatric psychiatric patients. Notable exceptions were that a one percentage point increase in the proportion of non-psychiatric pediatric ED visits increased boarding times at community sites by 1.06 hours (95% CI: 0.20–1.92), while a one percentage point increase in the proportion of pediatric psychiatric ED visits increased LOS by 3.64 hours (95% CI: 2.04–5.23) at the academic site. We found that ED demand had a minimal effect on disposition status, with small increases in demand rates favoring <1 percentage point increases in the likelihood of discharge. Instead, patient-level factors played a much stronger role in predicting discharge disposition. ConclusionsED demand has a meaningful effect on throughput times, but a minimal effect on disposition status. Further research is needed to validate these findings across other state and healthcare systems.

Benchmarking emergency department prediction models with machine learning and public electronic health records

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop prediction models and decision support systems to address these challenges. To date, there is no widely accepted clinical prediction benchmark related to the ED based on large-scale public EHRs. An open-source benchmark data platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. Based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we created a benchmark dataset and proposed three clinical prediction benchmarks. This study provides future researchers with insights, suggestions, and protocols for managing data and developing predictive tools for emergency care.

Potential role of general practice in reducing emergency department demand: A qualitative study.

To inform local, state and national strategies intended to reduce demand for ED care, the present study aimed to identify key factors influencing the current provision of acute care within primary healthcare (PHC) and explore the policy and system changes potentially required. Semi-structured interviews with key stakeholders were audio-recorded, transcribed verbatim and analysed through content and thematic approaches incorporating the Walt and Gilson health policy framework. Eleven interviews were conducted. Five key considerations were highlighted, namely the barriers and enablers for general practitioners (GPs) in providing acute care, barriers to patient use of PHC instead of ED, suggestions for new PHC models and improvements for current ED models. Additionally, economic issues relating to clinic funding and GP remuneration, complexities of state or federal funding and management of urgent care centres (UCC) were identified. Potential policy changes included GP clinics incorporating emergency appointments, GP triage, further patient streaming and changes to the ED medical workforce model, as well as linking hospitals with PHC clinics. Suggested system changes included improving rapid access to non-GP specialists, offering qualifications for urgent care within PHC, developing integrated information technology systems and educating patients regarding appropriate healthcare system pathways. The present study suggested that while PHC has the potential to attenuate the demands for ED services, a whole-of-system approach focusing on realignment of priorities and integrated changes are needed.

Understanding Demand and Capacity Mismatch in an Academic Emergency Department Using a Staircase Model of Provider Capacity and Staggered Shift Start Times.

Staffing and provider capacity are essential components of emergency department (ED) throughput. Patient flow is dependent on matching patient arrivals with provider capacity. Current models assume a static rate of patients per hour for providers; however, this metric has been shown to decrease throughout a shift in a pattern we describe as a staircase. We sought to analyze the demand capacity mismatch based on both a static and staircase model of resident productivity. We then suggest a new staggered staffing model that would improve flow in the ED. This was a retrospective analysis of patient demand and productivity, analyzing both static and staircase models of provider capacity. An alternative staggered shift model was then suggested, and a 2-sample t test was performed to assess if a new model reduces the amount of demand/capacity mismatch. Seventeen thousand five hundred twenty data points were analyzed over the 2-year interval, comparing the difference between actual patients placed into a treatment space at each hour and projected resident capacity based on the staircase model, using both the existing schedule and a new staggered schedule. Mean absolute values for the disparity in coverage was 2.69 (95% confidence interval 2.65-2.72) for the staircase scheduling model, and 2.14 (95% confidence interval 2.12-2.17) when staggering provider start times. The mean difference between these data sets was 0.54 (95% confidence interval 0.52-0.57; p < 0.0001). Academic EDs may find value in using a staircase model to analyze provider capacity because it is more reflective of actual capacity. EDs may benefit from visualizing their capacity curves to identify mismatches and staggering resident shifts to improve throughput and flow.

COVID-19 and diabetes: Impact on emergency department demand by diabetic patients. experience from a portuguese center

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Impact of the modifiable areal unit problem in assessing determinants of emergency department demand.

To examine the impact of the modifiable areal unit problem (MAUP) in an investigation of factors associated with ED demand in Perth, Western Australia, in 2016. Furthermore, to advocate a means of avoiding this impact. ED presentations were classified as: urgent medical, non-urgent medical, urgent trauma or non-urgent trauma. In each group, sex-stratified, age-adjusted multivariate associations with socio-economic status and distance to the nearest ED and general practitioner (GP) were estimated. Modelling was undertaken using different sets of spatial units: Australian Bureau of Statistics (ABS) Statistical Areas Level 1 (SA1s) and numerous aggregate-level zonations of SA1s (ABS SA2s and others). Estimates obtained using the different units often varied widely: for seven (30%) of 24 strata defined by combinations of sex, ED type and covariate, the smallest and largest effect sizes differed in terms of direction; further, for 11 (65%) of the remaining 17 strata, the largest effect size was at least twice as high as the smallest. This demonstrates the MAUP's impact and that analyses based on a single set of spatial units are unreliable. To resolve the observed variation, we highlight the SA1-level estimates. When formulating interventions targeting reduced ED utilisation, policy planners should be guided by evidence based on analysis of appropriate spatial units. This ideal is undermined by the widespread lack of acknowledgement of the MAUP in studies examining drivers of ED demand using spatially aggregated data. To avoid the MAUP, only estimates obtained through examining a minimal geographic unit should be relied upon.