The prevalence of quality issues and adverse outcomes among 72-hour return admissions in the emergency department

Value-based comparison of ambulatory children with respiratory diseases in an emergency department and a walk-in clinic: a retrospective cohort study in Québec, Canada

OBJECTIVE

To compare health outcomes and costs given in the emergency department (ED) and walk-in clinics for ambulatory children presenting with acute respiratory diseases.

DESIGN

A retrospective cohort study.

SETTING

This study was conducted from April 2016 to March 2017 in one ED and one walk-in clinic. The ED is a paediatric tertiary care centre, and the clinic has access to lab tests and X-rays.

PARTICIPANTS

Inclusion criteria were children: (1) aged from 2 to 17 years old and (2) discharged home with a diagnosis of upper respiratory tract infection (URTI), pneumonia or acute asthma.

MAIN OUTCOME MEASURES

The primary outcome measure was the proportion of patients returning to any ED or clinic within 3 and 7 days of the index visit. The secondary outcome measures were the mean cost of care estimated using time-driven activity-based costing and the incidence of antibiotic prescription for URTI patients.

RESULTS

We included 532 children seen in the ED and 201 seen in the walk-in clinic. The incidence of return visits at 3 and 7 days was 20.7% and 27.3% in the ED vs 6.5% and 11.4% in the clinic (adjusted relative risk at 3 days (aRR) (95% CI) 3.17 (1.77 to 5.66) and aRR at 7 days 2.24 (1.46 to 3.44)). The mean cost (95% CI) of care (CAD) at the index visit was $C96.68 (92.62 to 100.74) in the ED vs $C48.82 (45.47 to 52.16) in the clinic (mean difference (95% CI): 46.15 (41.29 to 51.02)). Antibiotic prescription for URTI was less common in the ED than in the clinic (1.5% vs 16.4%; aRR 0.10 (95% CI 0.03 to 0.32)).

CONCLUSIONS

The incidence of return visits and cost of care were significantly higher in the ED, while antibiotic use for URTI was more frequent in the walk-in clinic. These data may help determine which setting offers the highest value to ambulatory children with acute respiratory conditions.

Evaluation of a Structured Review Process for Emergency Department Return Visits with Admission

BACKGROUND

Review of emergency department (ED) revisits with admission allows the identification of improvement opportunities. Applying a health equity lens to revisits may highlight potential disparities in care transitions. Universal definitions or practicable frameworks for these assessments are lacking. The authors aimed to develop a structured methodology for this quality assurance (QA) process, with a layered equity analysis.

METHODS

The authors developed a classification instrument to identify potentially preventable 72-hour returns with admission (PPRA-72), accounting for directed, unrelated, unanticipated, or disease progression returns. A second review team assessed the instrument reliability. A self-reported race/ethnicity (R/E) and language algorithm was developed to minimize uncategorizable data. Disposition distribution, return rates, and PPRA-72 classifications were analyzed for disparities using Pearson chi-square and Fisher's exact tests.

RESULTS

The PPRA-72 rate was 4.8% for 2022 ED return visits requiring admission. Review teams achieved 93% agreement (κ = 0.51) for the binary determination of PPRA-72 vs. nonpreventable returns. There were significant differences between R/E and language in ED dispositions (p < 0.001), with more frequent admissions for the R/E White at the index visit and Other at the 72-hour return visit. Rates of return visits within 72 hours differed significantly by R/E (p < 0.001) but not by language (p = 0.156), with the R/E Black most frequent to have a 72-hour return. There were no differences between R/E (p = 0.446) or language (p = 0.248) in PPRA-72 rates. The initiative led to system improvements through informatics optimizations, triage protocols, provider feedback, and education.

CONCLUSION

The authors developed a review methodology for identifying improvement opportunities across ED 72-hour returns. This QA process enabled the identification of areas of disparity, with the continuous aim to develop next steps in ensuring health equity in care transitions.

Machine learning models for predicting unscheduled return visits of patients with abdominal pain at emergency department and validation during COVID-19 pandemic: A retrospective cohort study

Machine learning (ML) models for predicting 72-hour unscheduled return visits (URVs) for patients with abdominal pain in the emergency department (ED) were developed in a previous study. This study refined the data to adjust previous prediction models and evaluated the model performance in future data validation during the COVID-19 era. We aimed to evaluate the practicality of the ML models and compare the URVs before and during the COVID-19 pandemic. We used electronic health records from Chang Gung Memorial Hospital from 2018 to 2019 as a training dataset, and various machine learning models, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC) were developed and subsequently used to validate against the 2020 to 2021 data. The models highlighted several determinants for 72-hour URVs, including patient age, prior ER visits, specific vital signs, and medical interventions. The LR, XGB, and VC models exhibited the same AUC of 0.71 in the testing set, whereas the VC model displayed a higher F1 score (0.21). The XGB model demonstrated the highest specificity (0.99) and precision (0.64) but the lowest sensitivity (0.01). Among these models, the VC model showed the most favorable, balanced, and comprehensive performance. Despite the promising results, the study illuminated challenges in predictive modeling, such as the unforeseen influences of global events, such as the COVID-19 pandemic. These findings not only highlight the significant potential of machine learning in augmenting emergency care but also underline the importance of iterative refinement in response to changing real-world conditions.

Strategies to Measure and Improve Emergency Department Performance: A Review

Emergency Departments (EDs) globally face escalating challenges such as overcrowding, resource limitations, and increased patient demand. This study aims to identify and analyze strategies to enhance the structural performance of EDs, with a focus on reducing overcrowding, optimizing resource allocation, and improving patient outcomes. Through a comprehensive review of the literature and observational studies, the research highlights the effectiveness of various approaches, including triage optimization, dynamic staffing, technological integration, and strategic resource management. Key findings indicate that tailored strategies, such as implementing advanced triage protocols and leveraging telemedicine, can significantly reduce wait times and enhance patient throughput. Furthermore, evidence suggests that dynamic staffing models and the integration of cutting-edge diagnostic tools contribute to operational efficiency and improved quality of care. These strategies, when combined, offer a multifaceted solution to the complex challenges faced by EDs, promising better patient care and satisfaction. The study underscores the need for a comprehensive approach, incorporating both organizational and technological innovations, to address the evolving needs of emergency healthcare.

Predictors of 72-h unscheduled return visits with admission in patients presenting to the emergency department with abdominal pain

BACKGROUND

Unscheduled return visits (URVs) to the emergency department (ED) constitute a crucial indicator of patient care quality.

OBJECTIVE

We aimed to analyze the clinical characteristics of patients who visited the ED with abdominal pain and to identify the risk of URVs with admission (URVAs) from URVs without admission (URVNAs).

METHODS

This retrospective study included adult patients who visited the ED of Taipei Medical University Hospital because of abdominal pain and revisited in 72 h over a 5-year period (January 1, 2014, to December 31, 2018). Multivariable logistic regression analysis was employed to identify risk factors for URVAs and receiver operating characteristic (ROC) curve analysis was performed to determine the efficacy of variables predicting URVAs and the optimal cut-off points for the variables. In addition, a classification and regression tree (CART)-based scoring system was used for predicting risk of URVA.

RESULTS

Of 702 eligible patients with URVs related to abdominal pain, 249 had URVAs (35.5%). In multivariable analysis, risk factors for URVAs during the index visit included execution of laboratory tests (yes vs no: adjusted odds ratio [AOR], 4.32; 95% CI 2.99-6.23), older age (≥ 40 vs < 40 years: AOR, 2.10; 95% CI 1.10-1.34), Level 1-2 triage scores (Levels 1-2 vs Levels 3-5: AOR, 2.30; 95% CI 1.26-4.19), and use of ≥ 2 analgesics (≥ 2 vs < 2: AOR, 2.90; 95% CI 1.58-5.30). ROC curve analysis results revealed the combination of these 4 above variables resulted in acceptable performance (area under curve: 0.716). The above 4 variables were used in the CART model to evaluate URVA propensity.

CONCLUSIONS

Elder patients with abdominal pain who needed laboratory workup, had Level 1-2 triage scores, and received ≥ 2 doses of analgesics during their index visits to the ED had higher risk of URVAs.

Types of diagnostic errors reported by paediatric emergency providers in a global paediatric emergency care research network

Background

Diagnostic errors, reframed as missed opportunities for improving diagnosis (MOIDs), are poorly understood in the paediatric emergency department (ED) setting. We investigated the clinical experience, harm and contributing factors related to MOIDs reported by physicians working in paediatric EDs.

Methods

We developed a web-based survey in which physicians participating in the international Paediatric Emergency Research Network representing five out of six WHO regions, described examples of MOIDs involving their own or a colleague’s patients. Respondents provided case summaries and answered questions regarding harm and factors contributing to the event.

Results

Of 1594 physicians surveyed, 412 (25.8%) responded (mean age=43 years (SD=9.2), 42.0% female, mean years in practice=12 (SD=9.0)). Patient presentations involving MOIDs had common undifferentiated symptoms at initial presentation, including abdominal pain (21.1%), fever (17.2%) and vomiting (16.5%). Patients were discharged from the ED with commonly reported diagnoses, including acute gastroenteritis (16.7%), viral syndrome (10.2%) and constipation (7.0%). Most reported MOIDs (65%) were detected on ED return visits (46% within 24 hours and 76% within 72 hours). The most common reported MOID was appendicitis (11.4%), followed by brain tumour (4.4%), meningitis (4.4%) and non-accidental trauma (4.1%). More than half (59.1%) of the reported MOIDs involved the patient/parent–provider encounter (eg, misinterpreted/ignored history or an incomplete/inadequate physical examination). Types of MOIDs and contributing factors did not differ significantly between countries. More than half of patients had either moderate (48.7%) or major (10%) harm due to the MOID.

Conclusions

An international cohort of paediatric ED physicians reported several MOIDs, often in children who presented to the ED with common undifferentiated symptoms. Many of these were related to patient/parent–provider interaction factors such as suboptimal history and physical examination. Physicians’ personal experiences offer an underexplored source for investigating and mitigating diagnostic errors in the paediatric ED.

Clinical narrative-aware deep neural network for emergency department critical outcome prediction

Since early identification of potential critical patients in the Emergency Department (ED) can lower mortality and morbidity, this study seeks to develop a machine learning model capable of predicting possible critical outcomes based on the history and vital signs routinely collected at triage. We compare emergency physicians and the predictive performance of the machine learning model. Predictors including patients' chief complaints, present illness, past medical history, vital signs, and demographic data of adult patients (aged ≥ 18 years) visiting the ED at Shuang-Ho Hospital in New Taipei City, Taiwan, are extracted from the hospital's electronic health records. Critical outcomes are defined as in-hospital cardiac arrest (IHCA) or intensive care unit (ICU) admission. A clinical narrative-aware deep neural network was developed to handle the text-intensive data and standardized numerical data, which is compared against other machine learning models. After this, emergency physicians were asked to predict possible clinical outcomes of thirty visits that were extracted randomly from our dataset, and their results were further compared to our machine learning model. A total of 4,308 (2.5 %) out of the 171,275 adult visits to the ED included in this study resulted in critical outcomes. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) of our proposed prediction model is 0.874 and 0.207, respectively, which not only outperforms the other machine learning models, but even has better sensitivity (0.95 vs 0.41) and accuracy (0.90 vs 0.67) as compared to the emergency physicians. This model is sensitive and accurate in predicting critical outcomes and highlights the potential to use predictive analytics to support post-triage decision-making.

Reducing 48-h emergency department revisits and subsequent admissions: a retrospective study of increased emergency medicine resident floor coverage

BACKGROUND

Early unexpected hospital admission after emergency department (ED) discharge is an important topic regarding effective preventive measures. Reducing avoidable return visits can improve ED effectiveness and emergency care. This study evaluated the effects of an increase in the number of physicians and the 24-h coverage of emergency physicians on 48-h ED revisits with subsequent hospital admission. The characteristics and risk factors of the patients were also investigated.

RESULTS

This was a retrospective analysis performed 2 years before and 2 years after the implementation of an intervention in a tertiary care hospital in Thailand. The medical records of adult patients who revisited the ED within 48 h for related complaints were reviewed. The effect of the intervention was analyzed, and a prediction model was developed based on logistic regression. After implementing the intervention, the hospital admission rate at the second ED visit decreased from 44.5 to 41.1%; no significant difference was found (95% confidence interval (CI) - 5.05 to 11.78). Patients who required hospital admission had a significantly higher comorbidity score, more ED visits, and more hospitalizations within the past 12 months. A significantly higher hospital admission rate was also observed among patients older than 60 years, those who had an initial infectious diagnosis, and those who had a higher triage severity level (ESI II) at their first visit. The odds ratio (OR) showed lower odds of hospital admission at the second visit in the postintervention period; this difference was not significant (OR 0.87; 95% CI 0.61 to 1.23).

CONCLUSION

Our intervention did not significantly decrease the incidence of admission at an ED revisit. However, some factors identified in this study seem to have some benefits and might be helpful for preventing errors and constructing a standard discharge care plan for patients with these risk factors.

High-risk Return Visits to United States Emergency Departments, 2010–2018

Introduction: Although factors related to a return visit to the emergency department (ED) have been reported, only a few studies have examined “high-risk” ED revisits with serious adverse outcomes. In this study we aimed to describe the incidence and trend of high-risk ED revisits in United States EDs and to investigate factors associated with these revisits. Methods: We obtained data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), 2010–2018. Adult ED revisits within 72 hours of a previous discharge were identified using a mark on the patient record form. We defined high-risk revisits as revisits with serious adverse outcomes, including intensive care unit admissions, emergency surgery, cardiac catheterization, or cardiopulmonary resuscitation (CPR) during the return visit. We performed analyses using descriptive statistics and multivariable logistic regression, accounting for NHAMCS’s complex survey design. Results: Over the nine-year study period, there were an estimated 37,700,000 revisits, and the proportion of revisits in the entire ED population decreased slightly from 5.1% in 2010 to 4.5% in 2018 (P for trend = 0.02). By contrast, there were an estimated 827,000 high-risk ED revisits, and the proportion of high-risk revisits in the entire ED population remained stable at approximately 0.1%. The mean age of these high-risk revisit patients was 57 years, and 43% were men. Approximately 6% of the patients were intubated, and 13% received CPR. Most of them were hospitalized, and 2% died in the ED. Multivariable analysis showed that older age (65+ years), Hispanic ethnicity, daytime visits, and arrival by ambulance during the revisit were independent predictors of high-risk revisits. Conclusion: High-risk revisits accounted for a relatively small fraction (0.1%) of ED visits. Over the period of the NHAMCS survey between 2010-2018, this fraction remained stable. We identified factors during the return visit that could be used to label high-risk revisits for timely intervention.

Use of e-triggers to identify diagnostic errors in the paediatric ED

BACKGROUND

Diagnostic errors (DxEs) are an understudied source of patient harm in children rarely captured in current adverse event reporting systems. Applying electronic triggers (e-triggers) to electronic health records shows promise in identifying DxEs but has not been used in the emergency department (ED) setting.

OBJECTIVES

To assess the performance of an e-trigger and subsequent manual screening for identifying probable DxEs among children with unplanned admission following a prior ED visit and to compare performance to existing incident reporting systems.

DESIGN/METHODS

Retrospective single-centre cohort study of children ages 0-22 admitted within 14 days of a previous ED visit between 1 January 2018 and 31 December 2019. Subjects were identified by e-trigger, screened to identify cases where index visit and hospital discharge diagnoses were potentially related but pathophysiologically distinct, and then these screened-in cases were reviewed for DxE using the SaferDx Instrument. Cases of DxE identified by e-trigger were cross-referenced against existing institutional incident reporting systems.

RESULTS

An e-trigger identified 1915 unplanned admissions (7.7% of 24 849 total admissions) with a preceding index visit. 453 (23.7%) were screened in and underwent review using SaferDx. 92 cases were classified as likely DxEs, representing 0.4% of all hospital admissions, 4.8% among those selected by e-trigger and 20.3% among those screened in for review. Half of cases were reviewed by two reviewers using SaferDx with substantial inter-rater reliability (Cohen's κ=0.65 (95% CI 0.54 to 0.75)). Six (6.5%) cases had been reported elsewhere: two to the hospital's incident reporting system and five to the ED case review team (one reported to both).

CONCLUSION

An e-trigger coupled with manual screening enriched a cohort of patients at risk for DxEs. Fewer than 10% of DxEs were identified through existing surveillance systems, suggesting that they miss a large proportion of DxEs. Further study is required to identify specific clinical presentations at risk of DxEs.

Comparison of outcomes in emergency department revisiting patients before and after coronavirus disease 2019 epidemic

BACKGROUND AND IMPORTANCE

The outbreak of COVID-19 challenged the global health system and specifically impacted the emergency departments (EDs). Studying the quality indicators of ED care under COVID-19 has been a necessary task, and ED revisits have been used as an indicator to monitor ED performance.

OBJECTIVES

The study investigated whether discrepancies existed among ED revisiting cases before and after COVID-19 and whether the COVID-19 epidemic was a predictor of poor outcomes of ED revisits.

DESIGN

Retrospective study.

SETTINGS AND PARTICIPANTS

We used electronic health records data from a tertiary medical center. Data of patients with 72-h ED revisit after the COVID-19 epidemic were collected from February 2020 to June 2020 and compared with those of patients before COVID-19, from February 2019 to June 2019.

OUTCOME MEASURES AND ANALYSIS

The investigated outcomes included hospital admission, ICU admission, out-of-hospital cardiac arrest, and subsequent inhospital mortality. Univariate and multivariate logistic regression models were used to identify independent predictors of 72-h ED revisit outcomes.

MAIN RESULTS

In total, 1786 patients were enrolled in our study - 765 in the COVID group and 1021 in the non-COVID group. Compared with the non-COVID group, patients in the COVID group were younger (53.9 vs. 56.1 years old; P = 0.002) and more often female (66.1% vs. 47.3%; P < 0.001) and had less escalation of triage level (11.6% vs. 15.0%; P = 0.041). The hospital admission and inhospital mortality rates in the COVID and non-COVID groups were 33.9% vs. 32.0% and 2.7% vs. 1.5%, respectively. In the logistic regression model, the COVID-19 period was significantly associated with inhospital mortality (adjusted odds ratio, 2.289; 95% confidence interval, 1.059-4.948; P = 0.035).

CONCLUSION

Patients with 72-h ED revisits showed distinct demographic and clinical patterns before and after the COVID-19 epidemic; the COVID-19 period was an independent predictor of increased inhospital mortality.

Effects of Maryland's global budget revenue model on emergency department utilization and revisits

BACKGROUND

In 2014, Maryland (MD) implemented a "global budget revenue" (GBR) program that prospectively sets hospital budgets. This program introduced incentives for hospitals to tightly control volume and meet budget targets. We examine GBR's effects on emergency department (ED) visits, admissions, and returns.

METHODS

We performed an interrupted time-series analysis with difference-in-differences comparisons using 2012 to 2015 Healthcare Cost Utilization and Project data from MD, New York (NY), and New Jersey (NJ). We examined GBR's effects on ED visits/1,000 population, admissions from the ED, and ED returns at 72 h and 9 days. We also examined rates of admission, intensive care unit (ICU) stay, and in-hospital mortality among returns. To evaluate racial/ethnic and payer outcome disparities among ED returns, we performed a triple differences analysis.

RESULTS

ED visits decreased with GBR adoption in MD relative to NY and NJ, by five and six visits/1,000 population, respectively. ED admissions declined relative to NY and NJ, by 0.6% and 1.8%, respectively. There was also a post-GBR decline in ED returns by 0.7%. Admissions among returns declined by 2%, while ICU and in-hospital mortality among returns remained relatively stable. ED return outcomes varied by racial/ethnic and payer group. Non-Hispanic Whites and non-Hispanic Blacks experienced a similar decline in returns, while returns remained unchanged among Hispanics/Latinos, widening the disparity gap. Payer group disparities between privately insured and Medicare, Medicaid, and uninsured individuals improved, with the disparity reduction most pronounced among the uninsured.

CONCLUSIONS

GBR adoption was associated with lower ED utilization and admissions. ED returns and admissions among returns also decreased, while mortality and ICU stays among returns remained stable, suggesting that GBR has not led to adverse patient outcomes from fewer admissions. However, changes in ED return disparities varied by subgroup, indicating that improvements in care transitions may be uneven across patient populations.

How to fix the Merit-based Incentive Payment System (MIPS) in emergency medicine

In this issue of Academic Emergency Medicine, Gettel et al. report participation and performance of emergency clinicians in 2018's Merit-based Incentive Payment System (MIPS). MIPS is an enormous government program that assesses U.S. clinicians on several factors and makes payment adjustments for Medicare fee for service patients based on those results. MIPS performance is assessed in variable ways based on clinical specialty.

A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

Factors associated with a high-risk return visit to the emergency department: a case-crossover study

BACKGROUND AND IMPORTANCE

Although factors related to a return emergency department (ED) visit have been reported, few studies have examined 'high-risk' return ED visits with serious adverse outcomes. Understanding factors associated with high-risk return ED visits may help with early recognition and prevention of these catastrophic events.

OBJECTIVES

We aimed to (1) estimate the incidence of high-risk return ED visits, and (2) to investigate time-varying factors associated with these revisits.

DESIGN

Case-crossover study.

SETTINGS AND PARTICIPANTS

We used electronic clinical warehouse data from a tertiary medical center. We retrieved data from 651 815 ED visits over a 6-year period. Patient demographics and computerized triage information were extracted.

OUTCOME MEASURE AND ANALYSIS

A high-risk return ED visit was defined as a revisit within 72 h of the index visit with ICU admission, receiving emergency surgery, or with in-hospital cardiac arrest during the return ED visit. Time-varying factors associated with a return visit were identified.

MAIN RESULTS

There were 440 281 adult index visits, of which 19 675 (4.5%) return visits occurred within 72 h. Of them, 417 (0.1%) were high-risk revisits. Multivariable analysis showed that time-varying factors associated with an increased risk of high-risk revisits included the following: arrival by ambulance, dyspnea, or chest pain on ED presentation, triage level 1 or 2, acute change in levels of consciousness, tachycardia (>90/min), and high fever (>39°C).

CONCLUSIONS

We found a relatively small fraction of discharges (0.1%) developed serious adverse events during the return ED visits. We identified symptom-based and vital sign-based warning signs that may be used for patient self-monitoring at home, as well as new-onset signs during the return visit to alert healthcare providers for timely management of these high-risk revisits.

Inpatient Outcomes Following a Return Visit to the Emergency Department: A Nationwide Cohort Study

Introduction

Emergency department (ED) revisits are traditionally used to measure potential lapses in emergency care. However, recent studies on in-hospital outcomes following ED revisits have begun to challenge this notion. We aimed to examine inpatient outcomes and resource use among patients who were hospitalized following a return visit to the ED using a national database.

Methods

This was a retrospective cohort study using the National Health Insurance Research Database in Taiwan. One-third of ED visits from 2012–2013 were randomly selected and their subsequent hospitalizations included. We analyzed the inpatient outcomes (mortality and intensive care unit [ICU] admission) and resource use (length of stay [LOS] and costs). Comparisons were made between patients who were hospitalized after a return visit to the ED and those who were hospitalized during the index ED visit.

Results

Of the 3,019,416 index ED visits, 477,326 patients (16%) were directly admitted to the hospital. Among the 2,504,972 patients who were discharged during the index ED visit, 229,059 (9.1%) returned to the ED within three days. Of them, 37,118 (16%) were hospitalized. In multivariable analyses, the inpatient mortality rates and hospital LOS were similar between the two groups. Compared with the direct-admission group, the return-admission group had a lower ICU admission rate (adjusted odds ratio, 0.78; 95% confidence interval [CI], 0.72–0.84), and lower costs (adjusted difference, −5,198 New Taiwan dollars, 95% CI, −6,224 to −4,172).

Conclusion

Patients who were hospitalized after a return visit to the ED had a lower ICU admission rate and lower costs, compared to those who were directly admitted. Our findings suggest that ED revisits do not necessarily translate to poor initial care and that subsequent inpatient outcomes should also be considered for better assessment.

Predictors and outcomes of patients that return unplanned to the Emergency Department and require critical care admission: A multicenter study

OBJECTIVE

To determine the incidence, characteristics (including timeframe) and predictors of patients discharged from the Emergency Department (ED) that later return and require admission.

METHODS

A retrospective cross-sectional study examining all return visits to three EDs in Sydney, Australia, over a 12-month period. Patients returning within 28 days from ED discharge with the same or similar complaint were classified as a return visit to ensure capture of all return visits. Descriptive and inferential statistics were used to analyse the data and logistic regression was performed to predict factors associated with return visits with general admission, and return visits admitted to critical care.

RESULTS

There were 1,798 (30%) return visits which resulted in admission, mostly to a non-critical care area (1,679, 93%). The current NSW 48 -h time frame used to define a return visit in NSW captured half of all admitted returns (49.5%) and just over half (59.2%) of critical care admissions. Variables associated with an admission to critical care were age (OR 1.02, 95% CI 1.01, 1.03), initial presentation (index visit) made to a lower level ED (OR 3.76 95% CI 2.06, 6.86), Triage Category 2 (OR 3.67 95% CI 2.04, 6.60) and a cardiac diagnosis (OR 5.76, 95% CI 3.01, 11.01). This model had adequate discriminant ability with AUROC = 0.825.

CONCLUSION

A small number of return visits result in admission, especially to critical care. These patients are at risk of poor outcomes. As such, clinicians should have increased index of suspicion for patients who return that are older, present with cardiac problems, or have previously presented to a lower level ED. Revision of the current timeframe that defines a return visit ought to be considered by policy makers to improve the accuracy of this widely used key performance indicator.

Qualitative evaluation of a mandatory provincial programme auditing emergency department return visits

OBJECTIVE

The objective of this qualitative study was to evaluate the perceived impact and value of the Return Visit Quality Programme (RVQP), a mandatory province-wide emergency department audit programme.

DESIGN

We employed an interpretive descriptive qualitative approach with maximum variation sampling to ensure diverse representation across several geographical and institutional factors. RVQP programme leads were invited to participate in semistructured interviews and snowball sampling was used to reach non-lead physicians to capture the perspectives of those working within the programme.

SETTING

In Ontario's RVQP, participating emergency departments must audit their return visits resulting in admission to identify issues that can be addressed through quality improvement initiatives.

PARTICIPANTS

Between June and August 2018, we interviewed 32 participants (local programme leads and non-lead physicians) from 23 out of the 86 participating centres.

RESULTS

Participants' perceived impact and value of the programme was associated with the existence (or absence) and nature of the local quality improvement culture, the implementation approach of the programme within their emergency departments, and key aspects of the programme pertaining to medicolegal concerns and resource availability.

CONCLUSIONS

This study of an innovative, large-scale programme aimed at promoting continuous quality improvement in emergency departments showed that while its perceived impact has been meaningful, there are key structural and operational elements that support and hinder this aim. Healthcare leaders should consider these findings when looking to implement large-scale audit or quality improvement programmes.

Elevated Heart Rate and Risk of Revisit With Admission in Pediatric Emergency Patients

OBJECTIVE

The aim of this study was to identify emergency department (ED) heart rate (HR) values that identify children at elevated risk of ED revisit with admission.

METHODS

We performed a retrospective cohort study of patients 0 to 18 years old discharged from a tertiary-care pediatric ED from January 2013 to December 2014. We created percentile curves for the last recorded HR for age using data from calendar year 2013 and used receiver operating characteristic (ROC) curves to characterize the performance of the percentiles for predicting ED revisit with admission within 72 hours. In a held-out validation data set (calendar year 2014 data), we evaluated test characteristics of last-recorded HR-for-age cut points identified as promising on the ROC curves, as well as those identifying the highest 5% and 1% of last recorded HRs for age.

RESULTS

We evaluated 183,433 eligible ED visits. Last recorded HR for age had poor discrimination for predicting revisit with admission (area under the curve, 0.61; 95% confidence interval, 0.58-0.63). No promising cut points were identified on the ROC curves. Cut points identifying the highest 5% and 1% of last recorded HRs for age showed low sensitivity (10.1% and 2.5%) with numbers needed to evaluate of 62 and 50, respectively, to potentially prevent 1 revisit with admission.

CONCLUSIONS

Last recorded ED HR discriminates poorly between children who are and are not at risk of revisit with admission in a pediatric ED. The use of single-parameter HR in isolation as an automated trigger for mandatory reevaluation prior to discharge may not improve revisit outcomes.

Impact of an Emergency Department-to-Home Transitional Care Intervention on Health Service Use in Medicare Beneficiaries: A Mixed Methods Study

BACKGROUND

Hospital-based acute care [emergency department (ED) visits and hospitalizations] that is preventable with high-quality outpatient care contributes to health care system waste and patient harm.

OBJECTIVE

To test the hypothesis that an ED-to-home transitional care intervention reduces hospital-based acute care in chronically ill, older ED visitors.

RESEARCH DESIGN

Convergent, parallel, mixed-methods design including a randomized controlled trial.

SETTING

Two diverse Florida EDs.

SUBJECTS

Medicare fee-for-service beneficiaries with chronic illness presenting to the ED.

INTERVENTION

The Coleman Care Transition Intervention adapted for ED visitors.

MEASURES

The main outcome was hospital-based acute care within 60 days of index ED visit. We also assessed office-based outpatient visits during the same period.

RESULTS

The Intervention did not significantly reduce return ED visits or hospitalizations or increase outpatient visits. In those with return ED visits, the Intervention Group was less likely to be hospitalized than the Usual Care Group. Interview themes describe a cycle of hospital-based acute care largely outside patients' control that may be difficult to interrupt with a coaching intervention.

CONCLUSIONS AND RELEVANCE

Structural features of the health care system, including lack of access to timely outpatient care, funnel patients into the ED and hospital admission. Reducing hospital-based acute care requires increased focus on the health care system rather than patients' care-seeking decisions.

Risk factors of admission in 72-h return visits to emergency department

Objective:

Return visit to emergency department (ED) is a common phenomenon and has been a clinical indicator of quality of care in ED. Most of previous articles focused on the characteristics of the patients returning within 72 h after ED discharge, while those on subsequent admission are numbered. This study's purpose is to identify risk factors for admission among 72-h return visit in the ED adult population.

Materials and Methods:

This retrospective cohort study was conducted at a medical center in Eastern Taiwan. The study period was from January 1, 2013, to December 31, 2013. We excluded patients who left against medical advice or without being seen, who was admitted or transferred at the index ED visit, whose medical records were incomplete, and whose age was below 18 years old. Significant variables were selected based on univariate analysis and later entered into multivariate logistic regression analysis to identify risk factors for 72-h return admission.

Results:

We identified 1575 eligible visits, and there were 1,119 visits entering into the final analysis. Male gender (odds ratio [OR] = 1.44), ambulance-transport at return visit (OR = 3.68), senior staff (OR = 1.52), work-up (OR = 3.03), and longer length of stay (LOS) were associated with higher risks of admission among ED 72-h return visits. Age, comorbidity, mode of transport at index visit, consultation, triage, type of illness, outpatient department visit between ED visits, and interval between index and return visits were not significantly associated with return admission.

Conclusion:

Gender, mode of transportation, staff experience, check-up, and LOS are associated with ED return admission.

Frequency, Causes, and Outcomes of Return Visits to the Emergency Department Within 72 Hours: A Retrospective Observational Study

Background

Emergency departments (EDs) serve as an accessible gateway to healthcare system wherein numerous patients consider it a prime choice for medical complaints. Frequency of ED revisits, causes, and its burden are necessary to assess quality of care provided to patients and identify factors that leads to revisit.

Patients and Methods

Electronic and printed medical records of all patients who revisited ED from January to May 2016 within 72 hours of initial visit were reviewed. Patients’ cause of revisit were classified to three categories: patient-, physician- and system-related factors. Common complaints that require revisits were also collected. Descriptive analysis was performed and categorical variables were represented by the frequency; percentages and continuous variables were presented as median, and range if data did not follow normal distribution.

Results

Of the 79,279 patients who visited ED during the study period, 1.3% (1000) patients revisited within 72 hours; 51.3% (n=513) were males, with a mean age of 31.5 years (SD=17.7 years) where majority (57.1%) had no comorbidity recorded. The most attributed factors for revisit were as follows: patient-related causes 635 patients (63.5%), physician-related factors 167 patients (16.7%), and system-related factors 42 patients (4.2%); 15.6% were found not related to the initial visit. Recurrence of the same complaint was the highest among patient-related factors (80.5%), inadequate management and no improvement of symptoms in 71.3% among the physician-related factors. The most common ED revisit complaint was fever 29.1% (n=291). Outcomes of the revisit were mainly patient discharge 96.7% (n=967), admission 1.2% (n=12) and death in 0.2% (n=2).

Conclusion

Recurrence of the same complaint with no symptoms improvement and suboptimal management of physicians contributed to most of the ED revisits within 72 hours. Encouraging physicians to provide clear instructions in educating patients on discharge regarding disease progression and its red flags as to when a return to ED, might help in reducing revisit rate.

Pediatric Emergency Department Return Visits: An Innovative and Systematic Approach to Promote Quality Improvement and Patient Safety

OBJECTIVES

Emergency department (ED) return visits (RVs) leading to hospital admission are a quality measure that can potentially signal gaps in patient care. Systematic capture and investigation of RVs at a case level can provide an understanding of patient- and visit-level factors associated with RVs, and thus inform system-level quality improvement (QI) opportunities. Our objective is to describe the development of a database that enables tracking and analyzing of all pediatric ED RVs, to understand recurring themes and inform QI initiatives.

METHODS

A single-center retrospective cohort study was conducted at a quaternary care children's hospital during a 3-year period (December 2013 to November 2016). All 72-hour RVs were audited for patient- and visit-level variables and clinicians completed root-cause analyses of their RVs. Using descriptive statistics, variables associated with RVs and system-level quality themes were identified.

RESULTS

Of 214,047 ED patient visits, 1546 (0.7%) patients returned within 72 hours and were admitted. The RV patients had higher acuity scores on both visits compared with all ED visits, and the RV group had a higher proportion of children younger than 12 months than the overall ED visit group (25.0% vs 16.2%). The underlying cause for the majority of RVs was determined to be natural disease progression (63%), whereas 9% were callbacks for positive blood cultures or discrepant radiology results, and 6% were categorized as misdiagnoses. Several successful QI initiatives were completed as a result of the program.

CONCLUSIONS

Systematic monitoring and investigation of all ED RVs provides an innovative and effective approach to seeking provider- and system-level improvement opportunities.

Improving Quality of Care Through a Mandatory Provincial Audit Program: Ontario's Emergency Department Return Visit Quality Program

The Emergency Department Return Visit Quality Program was launched in Ontario, Canada, to promote a culture of quality. It mandates the province's largest-volume emergency departments (EDs) to audit charts of patients who had a return visit leading to hospital admission, including some of their 72-hour all-cause return visits with admission and all of their 7-day ones with sentinel diagnoses (ie, acute myocardial infarction, subarachnoid hemorrhage, and pediatric sepsis), and submit their findings to a governmental agency. This provides an opportunity to identify possible adverse events and quality issues, which hospitals can then address through quality improvement initiatives. A group of emergency physicians with quality improvement expertise analyzed the submitted audits and accompanying narrative templates, using a general inductive approach to develop a novel classification of recurrent quality themes. Since the Return Visit Quality Program launched in 2016, 125,698 return visits with admission have been identified, representing 0.93% of the 86 participating EDs' 13,559,664 visits. Overall, participating hospitals have conducted 12,852 detailed chart audits, uncovering 3,010 (23.4%) adverse events/quality issues and undertaking hundreds of quality improvement provincewide projects as a result. The inductive analysis revealed 11 recurrent themes, classified into 3 groupings: patient characteristics (ie, patient risk profile and elder care), ED team actions or processes (ie, physician cognitive lapses, documentation, handover/communication between providers, radiology, vital signs, and high-risk medications or medication interactions), and health care system issues (ie, discharge planning/community follow-up, left against medical advice/left without being seen, and imaging/testing availability). The Return Visit Quality Program is the largest mandatory audit program for EDs and provides a novel approach to identify local adverse events/quality issues to target for improved patient safety and quality of care. It provides a blueprint for health system leaders to enable clinicians to develop an approach to organizational quality, as well as for teams to construct an audit system that yields defined issues amenable to improvement.

The Impact of Advanced Practice Provider Staffing on Emergency Department Care: Productivity, Flow, Safety, and Experience

OBJECTIVES

We examined emergency department (ED) advanced practice provider (APP) productivity and how APP staffing impacted ED productivity, safety, flow, and experience.

METHODS

We used 2014 to 2018 data from a national emergency medicine group. The exposure was APP coverage: APP hours as a percentage of total clinician hours at the ED-day level. Multivariable regression was used to assess the relationship between APP coverage and productivity outcomes (patients/clinician hour, relative value units [RVUs]/clinician hour, RVUs/visit, and RVUs/salary-adjusted hour), flow outcomes (length of stay and left without treatment), safety (72-hour returns, incident reports), and experience (Press-Ganey scores), adjusting for patient and facility characteristics.

RESULTS

In 13.02 million patient visits in 105,863 ED-days across 94 EDs from 2014 to 2018, nurse practitioners and physician assistants managed 5.4 and 18.6% of visits independently, 74.6% by emergency physicians alone, and 1.4% jointly. APP visits had lower RVUs/visit (2.8 vs. 3.7) and lower patients/hour (1.1 vs. 2.2) compared to physician visits. Higher APP coverage (by 10%) at the ED-day level was associated with lower patients/clinician hour by 0.12 (95% confidence interval [CI] = -0.15 to -0.10) and lower RVUs/clinician hour by 0.4 (95% CI = -0.5 to -0.3). There was no impact of increasing APP coverage on RVUs/salary-adjusted hour or RVUs/visit. There was also no effect of increasing APP coverage on flow, safety, or patient experience.

CONCLUSION

In this group, APPs treated less complex visits and half as many patients/hour compared to physicians. Higher APP coverage allowed physicians to treat higher-acuity cases. We found no economies of scale for APP coverage, suggesting that increasing APP staffing may not lower staffing costs. However, there were also no adverse observed effects of APP coverage on ED flow, clinical safety, or patient experience, suggesting little risk of increased APP coverage on clinical care delivery.

Virtual Urgent Care Quality and Safety in the Time of Coronavirus

Background

Telemedicine use rapidly increased during the COVID-19 pandemic. This study assessed quality aspects of rapid expansion of a virtual urgent care (VUC) telehealth system and the effects of a secondary telephonic screening initiative during the pandemic.

Methods

A retrospective cohort analysis was performed in a single health care network of VUC patients from March 1, 2020, through April 20, 2020. Researchers abstracted demographic data, comorbidities, VUC return visits, emergency department (ED) referrals and ED visits, dispositions, intubations, and deaths. The team also reviewed incomplete visits. For comparison, the study evaluated outcomes of non-admission dispositions from the ED: return visits with and without admission and deaths. We separately analyzed the effects of enhanced callback system targeting higher-risk patients with COVID-like illness during the last two weeks of the study period.

Results

A total of 18,278 unique adult patients completed 22,413 VUC visits. Separately, 718 patient-scheduled visits were incomplete; the majority were no-shows. The study found that 50.9% of all patients and 74.1% of patients aged 60 years or older had comorbidities. Of VUC visits, 6.8% had a subsequent VUC encounter within 72 hours; 1.8% had a subsequent ED visit. Of patients with enhanced follow-up, 4.3% were referred for ED evaluation. Mortality was 0.20% overall; 0.21% initially and 0.16% with enhanced follow-up (p = 0.59). Males and black patients were significantly overrepresented in decedents.

Conclusion

Appropriately deployed VUC services can provide a pragmatic strategy to care for large numbers of patients. Ongoing surveillance of operational, technical, and clinical factors is critical for patient quality and safety with this modality.

Application of Machine Learning Techniques to Analyze Patient Returns to the Emergency Department

The study of the quality of hospital emergency services is based on analyzing a set of indicators such as the average time of first medical attention, the average time spent in the emergency department, degree of completion of the medical report and others. In this paper, an analysis is presented of one of the quality indicators: the rate of return of patients to the emergency service less than 72 h from their discharge. The objective of the analysis was to know the variables that influence the rate of return and which prediction model is the best. In order to do this, the data of the activity of the emergency service of a hospital of a reference population of 290,000 inhabitants were analyzed, and prediction models were created for the binary objective variable (rate of return to emergencies) using the logistic regression techniques, neural networks, random forest, gradient boosting and assembly models. Each of the models was analyzed and the result shows that the best model is achieved through a neural network with activation function tanh, algorithm levmar and three nodes in the hidden layer. This model obtains the lowest mean squared error (MSE) and the best area under the curve (AUC) with respect to the rest of the models used.

Comparison of admission rates among patients treated by male and female emergency physicians: a multicenter study

BACKGROUND

No study to date has looked at the gender of emergency medicine (EM) physicians in the United States in relation to admission rates. This study seeks to investigate admission rates of adult patients treated by female vs male EM physicians, to identify whether a practice pattern bias exists.

METHODS

This was a multicenter retrospective study of four community hospitals.

POPULATION

All patient encounters between July 1, 2016 and June 30, 2017.

OUTCOME

We compared multiple benchmarks, including admission rates, patient acuity, length of stay, return visits, patient age, and years of practice using descriptive statistics and Pearson Correlation Coefficients.

RESULTS

171,762 encounters by 71 EM physicians; 29 females, 42 males. Average admission rates: female 30.1%, male 28.0%, p = .188. Average encounters: female 2456, male 2394, p = 0.77. Acuity: female 149.3, male 146.9, p = .227. Average length of stay (minutes): female 294.4, male 277.4, p = .137. Average patient age: female 50.9, male 50.2, p = .457. Median time of encounter: female 12.8, male 12.7, p = .964. Years of practice: female 16.2, male 19.1, p = .274. Average return visits per one thousand: female 8.5, male 8.5, p = .864. Secondary analysis of Pearson Correlation Coefficient of Significance; admission rate and length of stay: female 0.53, p = .0026; male 0.76, p < .0001. Admission rate and acuity: female 0.56, p = .0012; male 0.76, p < .0001. Admission rate and patient age: female 0.54, p = 0.0018; male 0.50, p = 0.0003.

CONCLUSION

No statistically significant difference exists between the admission rates of male and female emergency medicine physicians. The admission rate in both groups had the highest correlation with patients' age, acuity, and length of stay.

"Analysis of readmissions to the emergency department among patients presenting with abdominal pain"

BACKGROUND

Abdominal pain is one of the most common complaints among patients admitted to the Emergency Department (ED). Diagnosis and management of abdominal pain may be a challenge and there are patients who require admission to the ED more than once in a short period of time. Our purpose was to assess the incidence of readmissions among patients treated in the ED due to abdominal pain and to investigate the impact of readmission on the further course of treatment.

METHODS

We conducted a prospective observational study, which included patients admitted to the ED in one academic, teaching hospital presenting with non-traumatic abdominal pain in a three-month period. Analyzed factors included demographic data, details related to first and subsequent visits in the ED and the course of hospitalization.

RESULTS

Overall, 928 patients were included to the study and 101 (10.88%) patients were admitted to the ED more than once during three-month period. Patients visiting ED repeatedly were older (p = 0.03) and more likely to be hospitalized (p < 0.01) compared to single-visit patients. Patients during their subsequent visits spent more time in the ED (p = 0.01), had greater chance to repeat their appointment (p = 0.04), be admitted to the hospital (p < 0.01) and were more likely diagnosed with cholelithiasis (p = 0.03) compared to patients on their initial visit. If admitted to the surgical department they were also more often qualified for surgical procedure than patients on their first visit (p < 0.01). In a group of patients admitted to the surgical department there were no significant differences in rates of conversion, postoperative complications and mortality between subgroups.

CONCLUSIONS

Readmissions among patients presenting with abdominal pain are a common phenomenon with prevalence of 10.88%. They are most commonly associated with cholelithiasis and occur more frequently among older patients, which suggests, that elderly require more attention during ED managements.

Influence of Overcrowding in the Emergency Department on Return Visit within 72 Hours

This study was conducted to determine whether overcrowding in the emergency department (ED) affects the occurrence of a return visit (RV) within 72 h. The crowding indicator of index visit was the average number of total patients, patients under observation, and boarding patients during the first 1 and 4 h from ED arrival time and the last 1 h before ED departure. Logistic regression analysis was conducted to determine whether each indicator affects the occurrence of RV and post-RV admission. Of the 87,360 discharged patients, 3743 (4.3%) returned to the ED within 72 h. Of the crowding indicators pertaining to total patients, the last 1 h significantly affected decrease in RV (p = 0.0046). Boarding patients were found to increase RV occurrence during the first 1 h (p = 0.0146) and 4 h (p = 0.0326). Crowding indicators that increased the likelihood of admission post-RV were total number of patients during the first 1 h (p = 0.0166) and 4 h (p = 0.0335) and evaluating patients during the first 1 h (p = 0.0059). Overcrowding in the ED increased the incidence of RV and likelihood of post-RV admission. However, overcrowding at the time of ED departure was related to reduced RV.

Analysis of emergency dental revisits in Taiwan (1999-2012) from Taiwanese National Health Insurance Research Database (NHIRD)

BACKGROUND/PURPOSE

Inadequate diagnosis, treatment, and palliative care provided in the emergency department could let the patients revisit shortly after being seen. The aim of this study was to evaluate the emergency dental revisits by using Taiwanese National Health Insurance Research Database (NHIRD).

MATERIALS AND METHODS

The Dental dataset from NHIRD was used to analyze the demographic characteristics of emergency dental revisits in Taiwanese population from 1999 to 2012. In addition, the conditions of emergency dental revisits were also identified based on the International Classification of Diseases, Ninth Revision (ICD-9).

RESULTS

Total 169,437 individuals had sought for emergency dental treatment from 1999 to 2012. In addition, 9624 individuals (5.68%) had repeated emergency dental visits within 1 year. The top 3 dental conditions were diseases of pulp and periapical tissues (ICD-9: 522), gingival and periodontal diseases (ICD-9: 523), and diseases of the oral soft tissues, excluding lesions specific for gingiva and tongue (ICD-9: 528). Males paid emergency dental revisits more than twice a year outnumber female counterparts (aOR = 1.252, 95% CI: 1.116-1.404). The higher odds ratio for emergency dental revisits over twice a year were found in 0-6 years, 35-49 years, 50-69 years, and ≧ 70 years subgroup as compared the reference of 20-34 years group. However, there were no significant differences for the stratification by year, day, region, and payroll bracket.

CONCLUSION

Taken together, this data demonstrated the current situation of emergency dental revisits in Taiwan. It would raise the important issue about how to reduce the unnecessary repeated emergency visits.

Predicting Emergency Department "Bouncebacks": A Retrospective Cohort Analysis

INTRODUCTION

The short-term return visit rate among patients discharged from emergency departments (ED) is a quality metric and target for interventions. The ability to accurately identify which patients are more likely to revisit the ED could allow EDs and health systems to develop more focused interventions, but efforts to reduce revisits have not yet found success. Whether patients with a high number of ED visits are at increased risk of a return visit remains underexplored.

METHODS

This was a population-based, retrospective, cohort study using administrative data from a large physician partnership. We included patients discharged from EDs from 80 hospitals in seven states from July 2014 - June 2016. We performed multivariable logistic regression of short-term return visits on patient, visit, hospital, and community characteristics. The primary outcome was the proportion of patients who had a return visit within 14 days of an index ED visit.

RESULTS

Among 6,699,717 index visits, the overall risk of 14-day revisit was 12.6%. Frequent visitors accounted for 18.7% of all visits and 40.2% of all 14-day revisits. Frequent visitor status was associated with the highest odds of a revisit (odds ratio [OR] 3.06; 95% confidence interval [CI], 3.041 - 3.073). Other predictors of revisits were cellulitis (OR 2.131; 95% CI, 2.106 - 2.156), alcohol-related disorders (OR 1.579; 95%CI, 1.548 - 1.610), congestive heart failure (OR 1.175; 95% CI, 1.126 - 1.226), and public insurance (Medicaid OR 1.514; 95% CI, 1.501 - 1.528; Medicare OR 1.601; 95% CI, 1.583 - 1.620).

CONCLUSION

Previous ED use - even a single previous visit - was a stronger predictor of a return visit than any other patient, hospital, or community characteristic. Clinicians should consider previous ED use when considering treatment decisions and risk of return visit, as should stakeholders targeting patients at risk of a return visit.

Unscheduled return visits to the emergency department with ICU admission: A trigger tool for diagnostic error

BACKGROUND

It is believed that patients who return to the Emergency Department (ED) and require admission are thought to represent failures in diagnosis, treatment or discharge planning. Screening readmission rates or patients who return within 72 h have been used in ED Quality Assurance efforts. These metrics require significant effort in chart review and only rarely identify care deviations.

OBJECTIVE

This study was conducted to evaluate the yield of reviewing ED return visits that resulted in an ICU admission. This study was conducted to evaluate the yield of reviewing ED return visits that resulted in an ICU admission. We planned to assess if the return visits with ICU admission were associated with deviations in care, and secondarily, to understand the common causes of error in this group.

METHODS

Retrospective review of patients presenting to a university affiliated ED between January 1, 2005 and December 31, 2015 and returned within 14 days requiring ICU admission.

RESULTS

From 1,106,606 ED visits, 511 patients returned within 14 days and were admitted to an ICU. 223 patients returned for a reason related to the index visit (43.6%). Of these related returns, 31 (13.9%) had a deviation in care on the index visit. When a standard diagnostic process of care framework was applied to these 31 cases, 47.3% represented failures in the initial diagnostic pathway.

CONCLUSION

Reviewing 14-day returns leading to ICU admission, while an uncommon event, has a higher yield in the understanding of quality issues involving diagnostic as well as systems errors.

Predicting 72-hour and 9-day return to the emergency department using machine learning

Objectives

To predict 72-h and 9-day emergency department (ED) return by using gradient boosting on an expansive set of clinical variables from the electronic health record.

Methods

This retrospective study included all adult discharges from a level 1 trauma center ED and a community hospital ED covering the period of March 2013 to July 2017. A total of 1500 variables were extracted for each visit, and samples split randomly into training, validation, and test sets (80%, 10%, and 10%). Gradient boosting models were fit on 3 selections of the data: administrative data (demographics, prior hospital usage, and comorbidity categories), data available at triage, and the full set of data available at discharge. A logistic regression (LR) model built on administrative data was used for baseline comparison. Finally, the top 20 most informative variables identified from the full gradient boosting models were used to build a reduced model for each outcome.

Results

A total of 330 631 discharges were available for analysis, with 29 058 discharges (8.8%) resulting in 72-h return and 52 748 discharges (16.0%) resulting in 9-day return to either ED. LR models using administrative data yielded test AUCs of 0.69 (95% confidence interval [CI] 0.68–0.70) and 0.71(95% CI 0.70–0.72), while gradient boosting models using administrative data yielded test AUCs of 0.73 (95% CI 0.72–0.74) and 0.74 (95% CI 0.73–0.74) for 72-h and 9-day return, respectively. Gradient boosting models using variables available at triage yielded test AUCs of 0.75 (95% CI 0.74–0.76) and 0.75 (95% CI 0.74–0.75), while those using the full set of variables yielded test AUCs of 0.76 (95% CI 0.75–0.77) and 0.75 (95% CI 0.75–0.76). Reduced models using the top 20 variables yielded test AUCs of 0.73 (95% CI 0.71–0.74) and 0.73 (95% CI 0.72–0.74).

Discussion and Conclusion

Gradient boosting models leveraging clinical data are superior to LR models built on administrative data at predicting 72-h and 9-day returns.

Usefulness of bounce-back admission in monitoring the quality of practice in the emergency department

Background: Recently, unscheduled readmissions after discharge from the emergency department (ED) (bounce-back admissions, BBAs) have been monitored as a hospital performance measure in countries other than Japan. It has been suggested that BBAs may be caused by errors in diagnoses or treatments.

Purpose: This retrospective cohort study aimed to evaluate BBAs and improve the quality of medical care in the ED of Showa University Hospital by analyzing the data of adult patients (≥18 years) with index visits to the ED of Showa University Hospital between June 2011 and May 2013 (n=15,069).

Patients and methods: Patients were registered and followed up for unscheduled admissions to this hospital within 7 days. In order to understand the reasons for BBAs, individual diagnoses upon BBA were compared to the corresponding diagnoses upon discharge.

Results: Among the 11,669 discharged patients, 180 patients were admitted within 3 days after discharge (3-day BBAs), and 257 were admitted within 7 days after discharge (7-day BBAs). The main diagnoses upon admission (BBA) were pneumonia or exacerbation of chronic obstructive pulmonary disease (COPD) or asthma (n=40, 16%), cholecystitis or cholangitis (n=21, 8.2%), and urinary tract infection (n=16, 6.2%). Among the 7-day BBA cases, 117 patients had similar and 110 patients had different diagnoses upon discharge and admission; in the remaining 30 cases, the results could not be ascertained owing to incomplete diagnostic data. In the cases of pneumonia, exacerbation of COPD or asthma, and colitis or enterocolitis, there was a significantly higher “similar” diagnoses than “different”, while the reverse was true for cases of stroke, ileus or bowel obstruction, and meningitis. These results were shared with the ED staff, and similar surveillances were periodically conducted. The frequency of admission within 7 days after discharge continuously declined from 2013 to 2016.

Conclusion: Analyzing the discharge and admission diagnoses may help ED staff to understand the reasons for common errors in order to follow the plan-do-check-act cycle of medical care in the ED.

An Innovative Model to Predict Pediatric Emergency Department Return Visits

OBJECTIVES

Return visit (RV) to the emergency department (ED) is considered a benchmarking clinical indicator for health care quality. The purpose of this study was to develop a predictive model for early readmission risk in pediatric EDs comparing the performances of 2 learning machine algorithms.

METHODS

A retrospective study based on all children younger than 15 years spontaneously returning within 120 hours after discharge was conducted in an Italian university children's hospital between October 2012 and April 2013. Two predictive models, artificial neural network (ANN) and classification tree (CT), were used. Accuracy, specificity, and sensitivity were assessed.

RESULTS

A total of 28,341 patient records were evaluated. Among them, 626 patients returned to the ED within 120 hours after their initial visit. Comparing ANN and CT, our analysis has shown that CT is the best model to predict RVs. The CT model showed an overall accuracy of 81%, slightly lower than the one achieved by the ANN (91.3%), but CT outperformed ANN with regard to sensitivity (79.8% vs 6.9%, respectively). The specificity was similar for the 2 models (CT, 97% vs ANN, 98.3%). In addition, the time of arrival and discharge along with the priority code assigned in triage, age, and diagnosis play a pivotal role to identify patients at high risk of RVs.

CONCLUSIONS

These models provide a promising predictive tool for supporting the ED staff in preventing unnecessary RVs.

Incidence, characteristics and outcomes of patients that return to Emergency Departments. An integrative review

BACKGROUND

Unplanned return visits account for up to 5% of Emergency Department presentations in Australia and have been associated with adverse events and increased costs. A large number of studies examine the incidence, characteristics and outcomes of unplanned return visits but few studies examine the reasons for return from a patient perspective. The objective of this integrative review was to determine the incidence, characteristics, outcomes and reasons for unplanned return visits to Emergency Departments.

METHOD

An integrative literature review design was employed to conduct a structured search of the literature using the databases CINAHL, MEDLINE, PubMed, ProQuest and EMBASE (inception to June 2018). Results were screened using predefined criteria and final studies collated and appraised using a quality assessment tool.

RESULTS

Fifty-two primary research articles were included in the review. The timeframe used to capture unplanned return visits varied and the incidence ranged between 0.07% and 33%. The majority of patients who return unplanned to the Emergency Department are subsequently discharged (51% and 90%) without an adverse event.

CONCLUSION

There is no consensus on the timeframe employed to classify unplanned return visits to the Emergency Department and the commonly used 72h lacks evidence. Routine statewide data linkage to capture return visits to other facilities is needed to ensure accurate data about this vulnerable patient group. Further research that focuses on patient and clinician perspectives is required to facilitate the development of local strategies to reduce the incidence of avoidable unplanned return visits.

Predictors of Admission in Adult Unscheduled Return Visits to the Emergency Department

Introduction

The 72-hour unscheduled return visit (URV) of an emergency department (ED) patient is often used as a key performance indicator in emergency medicine. We sought to determine if URVs with admission to hospital (URVA) represent a distinct subgroup compared to unscheduled return visits with no admission (URVNA).

Methods

We performed a retrospective cohort study of all 72-hour URVs in adults across 10 EDs in the Edmonton Zone (EZ) over a one-year period (January 1, 2015 - December 31, 2015) using ED information-system data. URVA and URVNA populations were compared, and a multivariable analysis identified predictors of URVA.

Results

Analysis of 40,870 total URV records, including 3,363 URVAs, revealed predictors of URVA on the index visit including older age (>65 yrs, odds ratio [OR] 3.6), higher disease acuity (Canadian Emergency Department Triage and Acuity Scale [CTAS] 2, OR 2.6), gastrointestinal presenting complaint (OR 2.2), presenting to a referral hospital (OR 1.4), fewer annual ED visits (<4 visits, OR 2.0), and more hours spent in the ED (>12 hours, OR 2.0). A decrease in CTAS score (increase in disease acuity) upon return visit also increased the risk of admission (-1 CTAS level, OR 2.6). ED crowding at the index visit, as indicated by occupancy level, was not a predictor.

Conclusion

We demonstrate that URVA patients comprise a distinct subgroup of 72-hour URV patients. Risk factors for URVA are present at the index visit suggesting that patients at high risk for URVA may be identifiable prior to admission.

Seventy-two-hour emergency department revisits among adults with chronic diseases: a Saudi Arabian study

Background

Despite the increase in adult emergency department (ED) utilization in Saudi Arabia, no studies have evaluated the 72-hour revisits. This study estimates the rate of 72-hour ED revisits and identifies its reasons and predictive factors among adults with chronic diseases.

Patients and methods

A hospital-based retrospective study that included 24,206 ED discharges for adults with chronic diseases at the adult ED of King Abdulaziz Medical City, Riyadh between September 13, 2015 and July 29, 2017 was performed. We extracted data on demographic information, reasons for ED visits/revisits, health insurance coverage, weekend ED arrival, and mortality.

Results

A sample of 24,206 ED discharges for 19,697 adults with at least one chronic disease was included in the analysis. The rate of 72-hour revisits in this study population was high: 3,144/24,206 (13%) had the first revisit and 319/3,144 (10.1%) had the second ED revisit within 72 hours. Diseases of the circulatory (19%) and genitourinary (15.8%) systems were the major reasons for the first ED revisit. The adjusted relative rate (aRR) of 72-hour ED revisits was higher in adults with chronic diseases and aged ≥60 years (aRR=1.360, 95% CI: 1.41–1.83; P=0.001), patients of female gender (aRR=1.24, 95% CI: 1.09–1.41; P=0.001), patients with health insurance coverage (aRR=4.23, 95% CI: 2.60–6.90; P=0.001), patients arriving to ED on a weekend (aRR=2.13, 95% CI: 1.03–4.41; P=0.041), and new patients (aRR=1.47, 95% CI: 1.25–1.73; P=0.001).

Conclusion

The rate of 72-hour revisits is high among adults with chronic diseases. Advancing age, female gender, health insurance coverage, weekend ED arrival, and new patients are the important predictive factors of the high rate of 72-hour revisits. Continuous quality assessment and monitoring of factors related to patients are needed to reduce the frequency of early ED revisits after discharge.

Return Visit Admissions May Not Indicate Quality of Emergency Department Care for Children

OBJECTIVE

The objective was to test the hypothesis that in-hospital outcomes are worse among children admitted during a return ED visit than among those admitted during an index ED visit.

METHODS

This was a retrospective analysis of ED visits by children age 0 to 17 to hospitals in Florida and New York in 2013. Children hospitalized during an ED return visit within 7 days were classified as "ED return admissions" (discharged at ED index visit and admitted at return visit) or "readmissions" (admission at both ED index and return visits). In-hospital outcomes for ED return admissions and readmissions were compared to "index admissions without return admission" (admitted at ED index visit without 7-day return visit admission).

RESULTS

Among 1,886,053 index ED visits to 321 hospitals, 75,437 were index admissions without return admission, 7,561 were ED return admissions, and 1,333 were readmissions. ED return admissions had lower intensive care unit admission rates (11.0% vs. 13.6%; adjusted odds ratio = 0.78; 95% confidence interval [CI] = 0.71 to 0.85), longer length of stay (3.51 days vs. 3.38 days; difference = 0.13 days; incidence rate ratio = 1.04; 95% CI = 1.02 to 1.07), but no difference in mean hospital costs (($7,138 vs. $7,331; difference = -$193; 95% CI = -$479 to $93) compared to index admissions without return admission.

CONCLUSIONS

Compared with children who experienced index admissions without return admission, children who are initially discharged from the ED who then have a return visit admission had lower severity and similar cost, suggesting that ED return visit admissions do not involve worse outcomes than do index admissions.

Risk Factors Associated With Emergency Department Return Visits Following Trauma System Discharge

OBJECTIVES

Little evidence exists in the pediatric trauma literature regarding what factors are associated with re-presentation to the hospital for patients discharged from the emergency department (ED).

METHODS

This was a retrospective cohort study of trauma system activations at a pediatric trauma center from June 30, 2007, through June 30, 2013, who were subsequently discharged from the ED or after a brief inpatient stay. Returns within 30 days were reviewed. χ, Student t test, and univariate logistical regression were used to compare predictive factors for those returning and not.

RESULTS

One thousand eight hundred sixty-three patient encounters were included in the cohort. Seventy-two patients (3.9%) had at least 1 return visit that was related to the original trauma activation. Age, sex, language, race/ethnicity, ED length of stay, arrival mode, level of trauma activation, and transfer from an outside hospital did not vary significantly between the groups. Patients with public insurance were almost 2 times more likely to return compared with those with private insurance (odds ratio, 1.92; 95% confidence interval, 1.11-3.35). Income by zip code was associated with the risk of a return visit, with patients in neighborhoods at less than the 50th percentile income twice as likely to return to the ED (odds ratio, 2.15; 95% confidence interval, 1.30-3.54).

CONCLUSIONS

Patients with public insurance and those from low-income neighborhoods were significantly more likely to return to the ED after trauma system activation. These data can be used to target interventions to decrease returns in high-risk trauma patients.

Interphysician Differences in Emergency Department Length of Stay

BACKGROUND

Emergency physicians differ in many ways with respect to practice. One area in which interphysician practice differences are not well characterized is emergency department (ED) length of stay (LOS).

OBJECTIVE

To describe how ED LOS differs among physicians.

METHODS

We performed a 3-year, five-ED retrospective study of non-fast-track visits evaluated primarily by physicians. We report each provider's observed LOS, as well as each provider's ratio of observed LOS/expected LOS (LOS_O/E); we determined expected LOS based on site average adjusted for the patient characteristics of age, gender, acuity, and disposition status, as well as the time characteristics of shift, day of week, season, and calendar year.

RESULTS

Three hundred twenty-seven thousand, seven hundred fifty-three visits seen by 92 physicians were eligible for analysis. For the five sites, the average shortest observed LOS was 151 min (range 106-184 min), and the average longest observed LOS was 232 min (range 196-270 min); the average difference was 81 min (range 69-90 min). For LOS_O/E, the average lowest LOS_O/E was 0.801 (range 0.702-0.887), and the average highest LOS_O/E was 1.210 (range 1.186-1.275); the average difference between the lowest LOS_O/E and the highest LOS_O/E was 0.409 (range 0.305-0.493).

CONCLUSION

There are significant differences in ED LOS at the level of the individual physician, even after accounting for multiple confounders. We found that the LOS_O/E for physicians with the lowest LOS_O/E at each site averaged approximately 20% less than predicted, and that the LOS_O/E for physicians with the highest LOS_O/E at each site averaged approximately 20% more than predicted.

The durability of operational improvements with rotational patient assignment

INTRODUCTION

Previous work has suggested that Emergency Department rotational patient assignment (a system in which patients are algorithmically assigned to physicians) is associated with immediate (first-year) improvements in operational metrics. We sought to determine if these improvements persisted over a longer follow-up period.

METHODS

Single-site, retrospective analysis focused on years 2-4 post-implementation (follow-up) of a rotational patient assignment system. We compared operational data for these years with previously published data from the last year of physician self-assignment and the first year of rotational patient assignment. We report data for patient characteristics, departmental characteristics and facility characteristics, as well as outcomes of length of stay (LOS), arrival to provider time (APT), and rate of patients who left before being seen (LBBS).

RESULTS

There were 140,673 patient visits during the five year period; 138,501 (98.7%) were eligible for analysis. LOS, APT, and LBBS during follow-up remained improved vs. physician self-assignment, with improvements similar to those noted in the first year of implementation. Compared with the last year of physician self-assignment, approximate yearly average improvements during follow-up were a decrease in median LOS of 18min (8% improvement), a decrease in median APT of 21min (54% improvement), and a decrease in LBBS of 0.69% (72% improvement).

CONCLUSION

In a single facility study, rotational patient assignment was associated with sustained operational improvements several years after implementation. These findings provide further evidence that rotational patient assignment is a viable strategy in front-end process redesign.