The Impact of Emergency Department Census on the Decision to Admit

Emergency department staff opinion on newly introduced phlebotomy services in the department. A cross-sectional study incorporating thematic analysis

OBJECTIVES

The demand for ED services, both in terms of patient numbers and complexity has risen over the past decades. According to reports, there has been an increase in the ED patient presentation rate from 330 per 1000 to 334 per 1000 between 2018-2019 and 2022-2023. Consequently, new care models have been introduced to address this surge in demand, mitigate associated risks and improve overall safety. Among these models is the concept of 'front loading' clinical care, involving the initiation of interventions at the point of arrival. The present study evaluates the impact of introducing phlebotomists at triage.

METHODS

We conducted a cross-sectional survey using purposive sampling at a single quaternary metropolitan ED with an annual census of greater than 90 000, encompassing all clinical staff in the ED. The survey data were analysed quantitatively and complemented by a thematic analysis.

RESULTS

The response rate for the questionnaire was 61% (n = 207), with good representation from all ED craft groups. Nearly all the staff (99.5%) reported being aware of the presence of phlebotomists in the ED, whereas only 57% of the staff reported working in triage (P = 0.05, 0.00 to 0.04). 'Valuable/vital resource' featured as a common response. Early decision-making, patient safety, staff and patient satisfaction emerged as consistent themes.

CONCLUSIONS

Staff expressed satisfaction that patient care now begins in the waiting room, especially after extended waiting periods prior to cubicle allocation. They assert that this improvement significantly enhances timely treatment and disposition decisions, as well as overall patient satisfaction.

Traumatic Brain Injury Patients Admitted on High-Census Days Receive Less Critical Care and Have an Increased Risk for Delirium

INTRODUCTION

The utilization of healthcare services in a growing population has raised concerns about its impact on clinical outcomes. Studies have shown that increased hospital census is associated with higher admission rates and unnecessary consults, tests, and procedures in various areas of healthcare. Traumatic brain injuries (TBIs), a significant concern due to their potential for long-term disabilities, are commonly encountered in intensive care units (ICUs) and are a leading cause of patient mortality. Despite extensive research on various aspects of TBI, the effect of the patient census on TBI outcomes remains unexplored. This study aims to investigate the relationship between healthcare provider patient census and clinical outcomes in TBI patients at a level I trauma center.

METHODS

A retrospective review was conducted from 2017 to 2022. The mean number of patients per day in the trauma service was determined, with patients below this average considered to be present on low-census days and those above it on high-census days. Patient demographics, mechanisms of injury, vital signs, TBI severity, and associated injuries were analyzed. Adjusted regression analyses were conducted.

RESULTS

Over the study period, 1,527 TBI patients were identified. Demographics were similar between patients admitted on high- and low-census days. Patients with moderate TBI were 30% less likely to be admitted to the ICU on high-census days, whereas there was no difference in ICU admission for patients with mild or severe TBI. Delirium was significantly higher in patients admitted on high-census days compared to those on low-census days. This was further identified to be predominantly driven by patients with mild TBI admitted on high-census days.

CONCLUSION

While most outcomes remained consistent, significant rates of delirium were found in our mild TBI patients admitted on high-census days suggesting the need for additional factors in the evaluation of these patients on admission. This study also reveals potential under-triage in moderate TBI patients on high-census days as they had significantly lower rates of ICU admission. These findings emphasize the need for further investigations to optimize patient care strategies within the context of fluctuating healthcare system demands.

Frontiers in Operations: Valuing Nursing Productivity in Emergency Departments

Problem definition

We quantify the increase in productivity in emergency departments (ED) from increasing nurse staff. We then estimate the associated revenue gains for the hospital and the associated welfare gains for society.

Academic/practical relevance

The United States is over a decade into the worst nursing shortage crisis in history, fueled by chronic under-investment. To demonstrate to hospital managers and policymakers the benefits of investing in nursing, we clarify the positive downstream effects of doing so in the ED setting.

Methodology

We use a high-resolution data set of patient visits to the ED of a major U.S. academic hospital. Time-dependent hazard estimation methods (nonparametric and parametric) are used to study how the realtime service speed of a patient varies with the state of the ED, including the time-varying workloads of the assigned nurse. A counterfactual simulation is used to estimate the gains from increasing nurse staff in the ED.

Results

We find that lightening a nurse's workload by one patient is associated with a 14% service speedup for every patient under the nurse's care. Simulation studies suggest that adding one more nurse to the busiest 12-hour shift of each day can shorten stays and avert $160,000 in lost patient wages per 10,000 visits. The reduction in service times also frees up capacity for treating more patients and generate $470,000 in additional net revenues for the hospital per 10,000 visits. Extensive sensitivity analyses suggest that our key message-investing in nursing will more than pay for itself-is likely to hold across a wide range of EDs.

Managerial implications

In determining whether to invest in more nursing resources, hospital managers need to look beyond whether payer reimbursements alone are sufficient to cover the upfront costs, to also account for the resulting downstream benefits.

Patients pathways before and after treatments in emergency departments: A retrospective analysis of secondary data in Germany

Increasing emergency department (ED) utilization induces considerable pressure on ED staff and organization in Germany. Reasons for certain ED attendances are seen partly in insufficient continuity of care outside of hospitals. To explore the health care patterns before and after an ED attendance in Germany, we used claims data from nine statutory health insurance funds, covering around 25 % of statutory health insurees (1). We descriptively analyzed ED attendances for adult patients in 2016 according to their sociodemographic characteristics and diagnoses (2). Based on the ED attendance as initial event, we investigated health care provider utilization 180 days before and after the respective ED treatment and are presented by means of Sankey diagrams. In total, 4,757,536 ED cases of 3,164,343 insured individuals were analyzed. Back pain was the most frequent diagnosis in outpatient ED cases (5.0 %), and 80.2 % of the patients visited primary care physicians or specialists 180 days before and 78.8 % 180 days after ED treatment. Among inpatient cases, heart failure (4.6 %) was the leading diagnosis and 74.6 % used primary care physicians or specialists 180 days before and 65.1 % 180 days after ED treatment. The ED re-attendance slightly increased for back pain (4.9 % to 7.9 %) and decreased for heart failure (13.4 % to 12.6 %).

A short multi-factor screening tool to assess falls-risk in older people presenting to an Australian emergency department: A feasibility study

OBJECTIVE

The purpose of this study was to evaluate use of a short multi-factor falls-risk screening tool for older people within the emergency department, to enable rapid identification of falls-risk and triggers for multidisciplinary referral for further falls-specific assessment.

METHODS

Older people, aged ≥70 years, presenting to the emergency department with a fall-related injury or disease (n = 137) were recruited by a research nurse following randomisation. A short multi-factor screening tool was completed, comprised of 14 falls-risk-related assessment components.

RESULTS

Only one participant did not generate any referrals. Participants generated most referrals for medications (85.4%), social and housing (84.6%), vision (67.2%), podiatry (66.9%), or function and mobility (54.7%). Based on our results, the screening tool could be reduced to eleven components. The median time-to-screen was 11 min (IQR 9-15), with 736 triggers generated for referral and further assessment of falls-risk.

CONCLUSION

Falls are a major cause of ED presentation for older people. A short multi-factor screening tool with eleven components could be adapted to local familiar falls-risk tools and be completed in less than 10 min. Further research to trial the feasibility of completing ED referrals based on screening results is required to confirm the usefulness of such screening and referral within the ED.

The effect of the COVID-19 pandemic on hospital admissions and outpatient visits in Ontario, Canada

INTRODUCTION

The wave-over-wave effect of the COVID-19 pandemic on hospital visits for non-COVID-19-related diagnoses in Ontario, Canada remains unknown.

METHODS

We compared the rates of acute care hospitalizations (Discharge Abstract Database), emergency department (ED) visits, and day surgery visits (National Ambulatory Care Reporting System) during the first five "waves" of Ontario's COVID-19 pandemic with prepandemic rates (since January 1, 2017) across a spectrum of diagnostic classifications.

RESULTS

Patients admitted in the COVID-19 era were less likely to reside in long-term-care facilities (OR 0.68 [0.67-0.69]), more likely to reside in supportive housing (OR 1.66 [1.63-1.68]), arrive by ambulance (OR 1.20 [1.20-1.21]) or be admitted urgently (OR 1.10 [1.09-1.11]). Since the start of the COVID-19 pandemic (February 26, 2020), there were an estimated 124,987 fewer emergency admissions than expected based on prepandemic seasonal trends, representing reductions from baseline of 14% during Wave 1, 10.1% in Wave 2, 4.6% in Wave 3, 2.4% in Wave 4, and 10% in Wave 5. There were 27,616 fewer medical admissions to acute care, 82,193 fewer surgical admissions, 2,018,816 fewer ED visits, and 667,919 fewer day-surgery visits than expected. Volumes declined below expected rates for most diagnosis groups, with emergency admissions and ED visits associated with respiratory disorders exhibiting the greatest reduction; mental health and addictions was a notable exception, where admissions to acute care following Wave 2 increased above prepandemic levels.

CONCLUSIONS

Hospital visits across all diagnostic categories and visit types were reduced at the onset of the COVID-19 pandemic in Ontario, followed by varying degrees of recovery.

Does What Happens in the ED Stay in the ED? The Effects of Emergency Department Physician Workload on Post-ED Care Use

Problem definition

Emergency department (ED) crowding has been a pressing concern in healthcare systems in the U.S. and other developed countries. As such, many researchers have studied its effects on outcomes within the ED. In contrast, we study the effects of ED crowding on system performance outside the ED-specifically, on post-ED care utilization. Further, we explore the mediating effects of care intensity in the ED on post-ED care use.

Methodology/results

We utilize a dataset assembled from more than four years of microdata from a large U.S. hospital and exhaustive billing data in an integrated health system. By using count models and instrumental variable analyses to answer the proposed research questions, we find that there is an increasing concave relationship between ED physician workload and post-ED care use. When ED workload increases from its 5th percentile to the median, the number of post-discharge care events (i.e., medical services) for patients who are discharged home from the ED increases by 5% and it is stable afterwards. Further, we identify physician test-ordering behavior as a mechanism for this effect: when the physician is busier, she responds by ordering more tests for less severe patients. We document that this "extra" testing generates "extra" post-ED care utilization for these patients.

Managerial implications

This paper contributes new insights on how physician and patient behaviors under ED crowding impact a previously unstudied system performance measure: post-ED care utilization. Our findings suggest that prior studies estimating the cost of ED crowding underestimate the true effect, as they do not consider the "extra" post-ED care utilization.

Potentially avoidable interfacility transfers following reduced emergency department volumes due to COVID-19 "Safer-at-Home" orders

OBJECTIVES

We sought to assess if a state-wide lockdown implemented due to COVID-19 was associated with increased odds of being a potentially avoidable transfer (PAT).

METHODS

We conducted a retrospective observational analysis using hospital administrative data of interfacility ED-to-ED transfers to a single, quaternary care adult ED after "Safer at Home" orders were issued March 23rd, 2020 in [Blinded for submission]. Using the PAT classification to identify transfers rapidly discharged from the ED or hospital and may not require in-person care, we used a multivariable logistic regression model to examine the association of the lockdown order with odds of a transfer being a PAT. We compared the period January 1, 2018 to March 23, 2020 with March 24, 2020 to September 30, 2020, adjusting for seasonality, patient, and situational factors.

RESULTS

There were 20,978 ED-to-ED transfers from during this period that were eligible and 4806 (23%) that met PAT criteria. While the first month post-lockdown saw a decrease in PATs (28%), this was not sustained. In the multivariable model there was a significant seasonal effect; May through September had the highest number of transfers as well as PATs. After adjusting for seasonality, the lockdown was not associated with PATs (adjusted odds ratio [aOR] 0.99, 95% CI 0.2, 5.2) and PATs decreased over time.

CONCLUSIONS

We did not find an effect of the COVID-19 lockdown on PATs though there was a considerable seasonal effect and an overall downward trend in PATs over time.

Validation of a Simple Score for Mortality Prediction in a Cohort of Unselected Emergency Patients

BACKGROUND

Prognostication is an important component of medical decision-making. A patients' general prognosis can be difficult to measure. The Simple Prognostic Score (SPS) was designed to include patients' age, mobility, aggregated vital signs, and the treating physician's decision to admit to aid prognostication. Study Aim. Our study aim is to validate the SPS, compare it with the Emergency Severity Index (ESI) regarding its prognostic performance, and test the interrater reliability of the subjective variable of the decision to admit.

METHODS

Over a period of 9 weeks all patients presenting to the ED were included, routinely interviewed, final disposition registered, and followed up for one year. The C-statistics of discrimination was used to compare SPS and ESI predictions of 7-day, 30-day, and 1-year mortality. Youden J Statistics and Odds ratio, using logistical regression, were calculated for the Simple Prognostic Score. In a subset, a chart review was performed by senior physicians for a secondary assessment of the decision to admit. Interrater reliability was calculated using percentages and Cohens Kappa.

RESULTS

Out of 5648 patients, 3272 (57.9%) had a low SPS (i.e., ≤ 1); none of these patients died within 7 days, 2 (0.1%) died within 30 days after presentation and 19 (0.6%) died within a year. The area under the curve for 1-year mortality of the Simple Prognostic Score was 0.848. Secondary analysis of the interrater agreement for the decision to admit was 92%.

CONCLUSION

In a prospective study of unselected ED patients, the Simple Prognostic Score was validated as a reliable predictor of short- and long-term mortality.

The impact of emergency department crowding on admission decisions and patient outcomes

OBJECTIVES

The objective of this study is to evaluate the impact of emergency department (ED) crowding levels on patient admission decisions and outcomes.

METHODS

A retrospective study was performed based on 2-year electronic health record data from a tertiary care hospital ED in Alberta, Canada. Using modified Poisson regression models, we studied the association of patient admission decisions and 7-day revisit probability with ED crowding levels measured by: 1) the total number of patients waiting and in treatment (ED census), 2) the number of boarding patients (boarder census), and 3) the average physician workload, calculated by the total number of ED patients divided by the number of physicians on duty (physician workload census). The control variables included age, gender, treatment area, triage level, and chief complaint. A subgroup analysis was performed to evaluate the heterogeneous effects among patients of different acuity levels.

RESULTS

Our dataset included 141,035 patient visit records after cleaning from August 2013 to July 2015. The patient admission probability was positively correlated with ED census (relative risk [RR] = 1.006, 95% confidence interval [CI] = 1.005 to 1.007) and physician workload census (RR = 1.029, 95% CI = 1.027 to 1.032), but inversely correlated with boarder census (RR = 0.991, 95% CI = 0.989 to 0.993). We further found that the 7-day revisit probability of discharged patients was positively associated with boarder census (RR = 1.009, 95% CI = 1.004 to 1.014).

CONCLUSIONS

Patient admission probability was found to be directly associated with ED census and physician workload census, but inversely associated with the boarder census. The effects of boarder census and physician workload census were stronger for patients of triage levels 3-5. Our results suggested that (i) insufficient physician staffing may lead to unnecessary patient admissions; (ii) too many boarding patients in ED leads to an increase in unsafe discharges, and as a result, an increase in 7-day revisit probability.

Forecasting emergency department hourly occupancy using time series analysis

STUDY OBJECTIVE

To develop a novel predictive model for emergency department (ED) hourly occupancy using readily available data at time of prediction with a time series analysis methodology.

METHODS

We performed a retrospective analysis of all ED visits from a large academic center during calendar year 2012 to predict ED hourly occupancy. Due to the time-of-day and day-of-week effects, a seasonal autoregressive integrated moving average with external regressor (SARIMAX) model was selected. For each hour of a day, a SARIMAX model was built to predict ED occupancy up to 4-h ahead. We compared the resulting model forecast accuracy and prediction intervals with previously studied time series forecasting methods.

RESULTS

The study population included 65,132 ED visits at a large academic medical center during the year 2012. All adult ED visits during the first 265 days were used as a training dataset, while the remaining ED visits comprised the testing dataset. A SARIMAX model performed best with external regressors of current ED occupancy, average department-wide ESI, and ED boarding total at predicting up to 4-h-ahead ED occupancy (Mean Square Error (MSE) of 16.20, and 64.47 for 1-hr- and 4-h- ahead occupancy, respectively). Our 24-SARIMAX model outperformed other popular time series forecasting techniques, including a 60% improvement in MSE over the commonly used rolling average method, while maintaining similar prediction intervals.

CONCLUSION

Accounting for current ED occupancy, average department-wide ESI, and boarding total, a 24-SARIMAX model was able to provide up to 4 h ahead predictions of ED occupancy with improved performance characteristics compared to other forecasting methods, including the rolling average. The prediction intervals generated by this method used data readily available in most EDs and suggest a promising new technique to forecast ED occupancy in real time.

The effect of overcrowding in emergency departments on the admission rate according to the emergency triage level

Overcrowding in emergency departments is a serious public health issue. Recent studies have reported that overcrowding in emergency departments affects not only the quality of emergency care but also clinical decisions about admission. However, no studies have examined the characteristics of the patient groups whose admission rate is influenced by such overcrowding. This retrospective cohort study was conducted in a single emergency department between January 1 and December 31, 2018. Patients over 19 years old were enrolled and divided into three groups according to the degree of overcrowding-high, low, and non-based on the total number of patients in the emergency department. An emergency triage tool (the Korean Triage and Acuity Scale) was used, which categorizes patients into five different levels. We analyzed whether the degree of change in the admission rate according to the extent of overcrowding differed for each triage group. There were 73,776 patients in this study. In the analysis of all patient groups, the admission rate increased as the degree of overcrowding rose (the adjusted odds ratio for admission was 1.281 (1.225-1.339) in the high overcrowding group versus the non-overcrowding group). The analysis of the patients in each triage level showed an increase in the admission rate associated with the overcrowding, which was greater in the patient groups with a lower triage level (adjusted odds ratios for admission in the high overcrowding group versus non-overcrowding group: Korean Triage and Acuity Scale level 3 = 1.215 [1.120-1.317], level 4 = 1.294 [1.211-1.382], and level 5 = 1.954 [1.614-2.365]).

Predicting emergency department orders with multilabel machine learning techniques and simulating effects on length of stay

OBJECTIVE

Emergency departments (EDs) continue to pursue optimal patient flow without sacrificing quality of care. The speed with which a healthcare provider receives pertinent information, such as results from clinical orders, can impact flow. We seek to determine if clinical ordering behavior can be predicted at triage during an ED visit.

MATERIALS AND METHODS

Using data available during triage, we trained multilabel machine learning classifiers to predict clinical orders placed during an ED visit. We benchmarked 4 classifiers with 2 multilabel learning frameworks that predict orders independently (binary relevance) or simultaneously (random k-labelsets). We evaluated algorithm performance, calculated variable importance, and conducted a simple simulation study to examine the effects of algorithm implementation on length of stay and cost.

RESULTS

Aggregate performance across orders was highest when predicting orders independently with a multilayer perceptron (median F1 score = 0.56), but prediction frameworks that simultaneously predict orders for a visit enhanced predictive performance for correlated orders. Visit acuity was the most important predictor for most orders. Simulation results indicated that direct implementation of the model would increase ordering costs (from $21 to $45 per visit) but reduce length of stay (from 158 minutes to 151 minutes) over all visits.

DISCUSSION

Simulated implementations of the predictive algorithm decreased length of stay but increased ordering costs. Optimal implementation of these predictions to reduce patient length of stay without incurring additional costs requires more exploration.

CONCLUSIONS

It is possible to predict common clinical orders placed during an ED visit with data available at triage.

Effect of a split-flow physician in triage model on abdominal CT ordering rate and yield

OBJECTIVE

The objective of this study was to compare the rate and clinical yield of computed tomography (CT) imaging between patients presenting with abdominal pain initially seen by a physician in triage (PIT) versus those seen only by physicians working in the main emergency department (ED).

METHODS

A retrospective study was conducted of all self-arrivals >18 years old presenting to a single ED with abdominal pain. Nine-hundred patients were randomly selected from both the PIT and traditional patient flow groups and rates and yields of CT imaging were compared, both alone and in a model controlling for potential confounders. Predetermined criteria for CT significance included need for admission, consult, or targeted medications.

RESULTS

The overall rate of CT imaging (unadjusted) did not differ between the PIT and traditional groups, 48.7% (95% CI 45.4-51.9) vs. 45.1% (95% CI 41.8-48.4), respectively (p = .13). The CT yield for patients seen in in the PIT group was also similar to that of the traditional group: 49.1% (95% CI 44.4-53.8) vs. 50.5% (95% CI 45.6-55.4) (p = .68). In the logistic regression model, when controlling for age, gender, ESI-acuity, race and insurance payor, PIT vs. traditional was not a predictor of CT ordering (OR 1.14, 95% CI 0.94-1.38).

CONCLUSIONS

For patients with abdominal pain, we found no significant differences in rates of CT ordering or CT yield for patients seen in a PIT vs. traditional models, suggesting the increased efficiencies offered by PIT models do not come at the cost of increased or decreased imaging utilization.

Making Decisions in the Era of the Clinical Decision Rule: How Emergency Physicians Use Clinical Decision Rules

PURPOSE

Physicians are often asked to integrate clinical decision rules (CDRs) with their own cognitive processes to reach a diagnosis. Clinicians, researchers, and educators must understand these cognitive processes to evaluate and improve the diagnostic process. The authors sought to explore emergency physicians' diagnostic processes and to examine how they integrated CDRs into their reasoning using simulated cases (with chest pain or leg pain).

METHOD

From August 2015 to July 2016, 16 practicing emergency physicians from 3 teaching hospitals associated with McMaster University, Ontario, Canada, were interviewed via a novel "teach aloud" protocol. Six videos of simulated patients with chest pain, breathlessness, or leg discomfort were used as prompts for the physicians to demonstrate their diagnostic thinking. Using a constructivist grounded theory analysis, 3 investigators independently reviewed the interview transcripts, meeting regularly to discuss identified themes and subthemes until sufficiency was reached.

RESULTS

A model to describe how clinicians integrate their own decision making with CDRs was developed, showing that physicians engage in an iterative diagnostic process that repeatedly refines the differential diagnosis list. The steps in the diagnostic process were: refinement of the differential diagnosis, ordering a hierarchy of risk, the decision to test, choosing the tests, and interpreting test results. Physicians applied CDRs when they had already decided to test.

CONCLUSIONS

To date, CDRs assume a static, linear model of clinical decision making. Findings demonstrate that participants engaged in iterative and dynamic decision-making processes that changed throughout their patient encounter, contingent on multiple contextual features. Understanding these processes could inform future development of CDRs and educational strategies around these decision aids.

Boarding is Associated with Reduced Emergency Department Efficiency that is not Mitigated by a Provider in Triage

INTRODUCTION

Boarding of patients in the emergency department (ED) is associated with decreased ED efficiency. The provider-in-triage (PIT) model has been shown to improve ED throughput, but it is unclear how these improvements are affected by boarding. We sought to assess the effects of boarding on ED throughput and whether implementation of a PIT model mitigated those effects.

METHODS

We performed a multi-site retrospective review of 955 days of ED operations data at a tertiary care academic ED (AED) and a high-volume community ED (CED) before and after implementation of PIT. Key outcome variables were door to provider time (D2P), total length of stay of discharged patients (LOSD), and boarding time (admit request to ED departure [A2D]).

RESULTS

Implementation of PIT was associated with a decrease in median D2P by 22 minutes or 43% at the AED (p < 0.01), and 18 minutes (31%) at the CED (p < 0.01). LOSD also decreased by 19 minutes (5.9%) at the AED and 8 minutes (3.3%) at the CED (p<0.01). After adjusting for variations in daily census, the effect of boarding (A2D) on D2P and LOSD was unchanged, despite the implementation of PIT. At the AED, 7.7 minutes of boarding increased median D2P by one additional minute (p < 0.01), and every four minutes of boarding increased median LOSD by one minute (p < 0.01). At the CED, 7.1 minutes of boarding added one additional minute to D2P (p < 0.01), and 4.8 minutes of boarding added one minute to median LOSD (p < 0.01).

CONCLUSION

In this retrospective, observational multicenter study, ED operational efficiency was improved with the implementation of a PIT model but worsened with boarding. The PIT model was unable to mitigate any of the effects of boarding. This suggests that PIT is associated with increased efficiency of ED intake and throughput, but boarding continues to have the same effect on ED efficiency regardless of upstream efficiency measures that may be designed to minimize its impact.

To Admit or Not to Admit: Emergency Department Discharges After Request for Medicine Admission

BACKGROUND

The decision to discharge versus admit a patient from the emergency department (ED) carries significant consequences to the patient and healthcare system.

METHODS

We evaluated all ED visits at a single facility from January 1-December 31, 2015, where the ED provider initially requested admission to medicine; however, following medicine evaluation, the patient was discharged from the ED.

RESULTS

8.1% of medicine referrals resulted in discharge from the ED after referral for admission. 62.6% lacked documentation by medicine or another consulting service. Patients completed clinic follow-up within 7 or 30 days, 52.8% and 76.0% respectively. Emergency department revisit rates were similar for patients not referred versus referred for admission (8.0% vs. 8.1%, 13.3% vs. 14.6%, and 29.9% vs. 28.9% at 3, 7, and 30 days, respectively p-value > .05). Hospital admission during the follow-up period was also similar for these two groups (1.8% vs. 2.8%, 3.9% vs. 5.7%, and 11.3% vs. 15.0% at 3, 7, and 30 days, respectively p-value > .05).

CONCLUSIONS

Patients discharged from the ED after referral for medicine admission were not at significantly increased risk of subsequent ED revisit or hospital admission compared with nonreferred patients. This study illustrates the opportunity for collaboration between ED and medicine providers to refine disposition plans for patients who may fall into the "gray zone."

Later emergency provider shift hour is associated with increased risk of admission: a retrospective cohort study

BACKGROUND

Understanding factors that drive admissions is critical to containing cost and optimising hospital operations. We hypothesised that, due to multiple factors, emergency physicians would be more likely to admit a patient seen later in their shift.

METHODS

Retrospective study examining all patient visits at a large academic hospital from July 2010 to July 2016. Patients with missing data (n=191) were excluded. 294 031 emergency department (ED) visits were included in the final analysis. The exposure of interest was the time during the shift at which a patient was first evaluated by the clinician, and outcome was hospital admission. We used a generalised estimating equation with physician as the clustering level to adjust for patient age, gender, Emergency Severity Index (ESI, 1=most severe illness, 5=least severe illness) and 24 hours clock time. We also conducted a stratified analysis by three ESI categories.

RESULTS

From the 294 031 ED visits, 5977 were seen in the last hour of the shift. Of patients seen in the last shift hour, 43% were admitted versus 39% seen at any other time during the shift. There was a significant association between being evaluated in the last hour (RR 1.03, 95% CI 1.01 to 1.06) and last quarter (RR 1.02, 1.01 to 1.03) of shift and the likelihood of admission. Patients with an ESI Score of 4-5 saw the largest effect sizes (RR 1.62, 0.996-2.635 for last hour and RR 1.24, 0.996-1.535 for last quarter) but these were not statistically significant. Additionally, there was a trend towards increased likelihood of admission later in shift; the relative risk of admission was 1.04 in hour 6, (1.02-1.05), 1.03 in hour 7 (1.01-1.05), 1.04 in hour 8 (1.01-1.06) and 1.06 in hour 9 (1.013-1.101).

CONCLUSIONS

There is a small but significant association between a patient being evaluated later in an emergency physician's shift and their likelihood of being admitted to the hospital.

A latent variable approach to potential outcomes for emergency department admission decisions

In emergency departments (EDs), care providers continuously weigh admissions against continued monitoring and treatment often without knowing their condition and health needs. To understand the decision process and its causal effect on outcomes, an observational study must contend with unobserved/missing information and a lack of exchangeability between admitted and discharged patients. Our goal was to provide a general framework to evaluate admission decisions from electronic healthcare records (EHRs). We describe admission decisions as a decision-making process in which the patient's health needs is a binary latent variable. We estimate latent health needs from EHR with only partial knowledge of the decision process (ie, initial evaluation, admission decision, length of stay). Estimated latent health needs are then used to understand the admission decision and the decision's causal impact on outcomes. For the latter, we assume potential outcomes are stochastically independent from the admission decision conditional on latent health needs. As a case study, we apply our approach to over 150 000 patient encounters with the ED from the University of Michigan Health System collected from August 2012 through July 2015. We estimate that while admitting a patient with higher latent needs reduces the 30-day risk of revisiting the ED or later being admitted through the ED by over 79%, admitting a patient with lower latent needs actually increases these 30-day risks by 3.0% and 7.6%, respectively.

A Scoping Review of Physicians' Clinical Reasoning in Emergency Departments

STUDY OBJECTIVE

Clinical reasoning is considered a core competency of physicians. Yet there is a paucity of research on clinical reasoning specifically in emergency medicine, as highlighted in the literature.

METHODS

We conducted a scoping review to examine the state of research on clinical reasoning in this specialty. Our team, composed of content and methodological experts, identified 3,763 articles in the literature, 95 of which were included.

RESULTS

Most studies were published after 2000. Few studies focused on the cognitive processes involved in decisionmaking (ie, clinical reasoning). Of these, many confirmed findings from the general literature on clinical reasoning; specifically, the role of both intuitive and analytic processes. We categorized factors that influence decisionmaking into contextual, patient, and physician factors. Many studies focused on decisions in regard to investigations and admission. Test ordering is influenced by physicians' experience, fear of litigation, and concerns about malpractice. Fear of litigation and malpractice also increases physicians' propensity to admit patients. Context influences reasoning but findings pertaining to specific factors, such as patient flow and workload, were inconsistent.

CONCLUSION

Many studies used designs such as descriptive or correlational methods, limiting the strength of findings. Many gray areas persist, in which studies are either scarce or yield conflicting results. The findings of this scoping review should encourage us to intensify research in the field of emergency physicians' clinical reasoning, particularly on the cognitive processes at play and the factors influencing them, using appropriate theoretical frameworks and more robust methods.

Do Financial Incentives Change Length-of-stay Performance in Emergency Departments? A Retrospective Study of the Pay-for-performance Program in Metro Vancouver

BACKGROUND

Pay-for-performance (P4P) programs have been implemented in various forms to reduce emergency department (ED) patient length of stay (LOS). This retrospective study investigated to what extent the timing of patient disposition in Metro Vancouver EDs was influenced by a LOS-based P4P program.

METHODS

We analyzed ED visit records of four major hospitals in Metro Vancouver, Canada. For each ED, we individually tested whether LOS was distributed discontinuously at the LOS target before and after the P4P program was terminated. For the P4P effective period, we examined whether patients discharged just prior to the LOS target had a higher 7-day return-and-admission (RA) rate-the probability that a patient, after being discharged home, returned to any ED within 7 days and was admitted to an inpatient unit-than patients discharged just after the target.

RESULTS

Prior to the termination of the P4P program, in all four EDs, the LOS density of admitted patients was discontinuous and had a significant drop at the P4P 10-hours admission LOS target; a similar phenomenon was observed among discharged patients at the 4-hours discharge LOS target, but only in the two lower-volume EDs. Furthermore, in a lower-volume ED, patients who were discharged right before the 4-hours P4P LOS target had a higher 7-day RA rate than patients discharged right after the LOS target. After the termination of the discharge incentive, the discontinuity at the discharge LOS target became less evident, but patients were still more frequently admitted just before 10 hours in three of the four EDs as the local health authority continued to support the admission incentive scheme after the government terminated the P4P program.

CONCLUSIONS

The LOS-based financial incentive scheme appears to have influenced the timing of ED patient dispositions. The results suggest mixed consequences of the P4P program-it can reduce access block for admitted patients but may also lead to discharges associated with return visits and admissions.

The effects of emergency department crowding on triage and hospital admission decisions

BACKGROUND

Emergency department (ED) crowding is a recognized issue and it has been suggested that it can affect clinician decision-making.

OBJECTIVES

Our objective was to determine whether ED census was associated with changes in triage or disposition decisions made by ED nurses and physicians.

METHODS

We performed a retrospective study using one year of data obtained from a US academic center ED (65,065 patient encounters after cleaning). Using a cumulative logit model, we investigated the association between a patient's acuity group (low, medium, and high) and ED census at triage time. We also used multivariate logistic regression to investigate the association between the disposition decision for a patient (admit or discharge) and the ED census at the disposition decision time. In both studies, control variables included census, age, gender, race, place of treatment, chief complaint, and certain interaction terms.

RESULTS

We found statistically significant correlation between ED census and triage/disposition decisions. For each additional patient in the ED, the odds of being assigned a high acuity versus medium or low acuity at triage is 1.011 times higher (95% confidence interval [CI] for Odds Ratio [OR] = [1.009,1.012]), and the odds of being assigned medium or high acuity versus low acuity at triage is 1.009 times higher (95% CI for OR = [1.008,1.010]). Similarly, the odds of being admitted versus discharged increases by 1.007 times (95% CI for OR = [1.006,1.008]) per additional patient in the ED at the time of disposition decision.

CONCLUSION

Increased ED occupancy was found to be associated with more patients being classified as higher acuity as well as higher hospital admission rates. As an example, for a commonly observed patient category, our model predicts that as the ED occupancy increases from 25 to 75 patients, the probability of a patient being triaged as high acuity increases by about 50% and the probability of a patient being categorized as admit increases by around 25%.

Characteristics of Emergency Department Visits and Select Predictors of Hospitalization for Adults With Newly Diagnosed Cancer in a Safety-Net Health System

PURPOSE

There is little description of emergency department (ED) visits and subsequent hospitalizations among a safety-net cancer population. We characterized patterns of ED visits and explored nonclinical predictors of subsequent hospitalization, including time of ED arrival.

PATIENTS AND METHODS

This was a retrospective cohort study of patients with cancer (excluding leukemia and nonmelanoma skin cancer) between 2012 and 2016 at a large county urban safety-net health system. We identified ED visits occurring within 180 days after a cancer diagnosis, along with subsequent hospitalizations (observation stay or inpatient admission). We used mixed-effects multivariable logistic regression to model hospitalization at ED disposition, accounting for variability across patients and emergency physicians.

RESULTS

The 9,050 adults with cancer were 77.2% nonwhite and 55.0% female. Nearly one-quarter (24.7%) of patients had advanced-stage cancer at diagnosis, and 9.7% died within 180 days of diagnosis. These patients accrued 11,282 ED visits within 180 days of diagnosis. Most patients had at least one ED visit (57.7%); half (49.9%) occurred during business hours (Monday through Friday, 8:00 am to 4:59 pm), and half (50.4%) resulted in hospitalization. More than half (57.5%) of ED visits were for complaints that included: pain/headache, nausea/vomiting/dehydration, fever, swelling, shortness of breath/cough, and medication refill. Patients were most often discharged home when they arrived between 8:00 am and 11:59 am (adjusted odds ratio for hospitalization, 0.69; 95% CI, 0.56 to 0.84).

CONCLUSION

ED visits are common among safety-net patients with newly diagnosed cancer, and hospitalizations may be influenced by nonclinical factors. The majority of ED visits made by adults with newly diagnosed cancer in a safety-net health system could potentially be routed to an alternate site of care, such as a cancer urgent care clinic.

Emergency department provider and facility variation in opioid prescriptions for discharged patients

STUDY OBJECTIVE

To study the variation in opioid prescribing among emergency physicians and facilities for discharged adult ED patients.

METHODS

We conducted a retrospective analysis of ED visits from five U.S. hospitals between January and May 2014 using records from Data to Intelligence (D2i). We examined physician- and facility-level variation in opioid prescription rates for discharged ED patients. We calculated unadjusted opioid prescription rates at the physician and facility levels and used a multivariable mixed-effect logistic regression model to examine within-facility physician variation in opioid prescription adjusting for patient and situational factors including time of presentation, ED census, and physician workload.

RESULTS

In 47,304 visits across five EDs, median patient age was 40 years old (IQR 28,55), and 89% had some form of insurance. There were 17,098 (36%) ED discharges with at least one opioid prescription. The unadjusted facility-level opioid prescription rate ranged from 24%-46%. Among 253 ED physicians, the adjusted opioid prescription rate varied from 22%-76%. Increased physician workload is related to decreased odds of opioid prescription at ED discharge for the lowest (<3 patients) and moderate (6-9 patients) physician workload levels, while the association weakened with increasing levels of workload.

CONCLUSION

There was substantial physician and facility variation in opioid prescription for discharged adult ED patients. Emergency physicians were less likely to prescribe opioids when their workload was lower, and this effect diminished at high workload levels. Understanding situational and other factors that explain this variation is important given the rising U.S. opioid epidemic and the need for urgent intervention.

Managing Multiplicity: Conceptualizing Physician Cognition in Multipatient Environments

PURPOSE

Emergency physicians (EPs) regularly manage multiple patients simultaneously, often making time-sensitive decisions around priorities for multiple patients. Few studies have explored physician cognition in multipatient scenarios. The authors sought to develop a conceptual framework to describe how EPs think in busy, multipatient environments.

METHOD

From July 2014 to May 2015, a qualitative study was conducted at McMaster University, using a think-aloud protocol to examine how 10 attending EPs and 10 junior residents made decisions in multipatient environments. Participants engaged in the think-aloud exercise for five different simulated multipatient scenarios. Transcripts from recorded interviews were analyzed inductively, with an iterative process involving two independent coders, and compared between attendings and residents.

RESULTS

The attending EPs and junior residents used similar processes to prioritize patients in these multipatient scenarios. The think-aloud processes demonstrated a similar process used by almost all participants. The cognitive task of patient prioritization consisted of three components: a brief overview of the entire cohort of patients to determine a general strategy; an individual chart review, whereby the participant created a functional patient story from information available in a file (i.e., vitals, brief clinical history); and creation of a relative priority list. Compared with residents, the attendings were better able to construct deeper and more complex patient stories.

CONCLUSIONS

The authors propose a conceptual framework for how EPs prioritize care for multiple patients in complex environments. This study may be useful to teachers who train physicians to function more efficiently in busy clinical environments.