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

Descriptive analysis of emergency medical services 72-hour repeat patient encounters in a single, Urban Agency

INTRODUCTION

Emergency department unscheduled return visits within 72-h of discharge, called a "bounceback", have been used as a metric of quality of care. We hypothesize that specific demographics and dispositions may be associated with Emergency Medical Services (EMS) 72-h bouncebacks.

METHODS

For all patient encounters within one calendar year from a large, urban EMS agency, we recorded demographics (name, date of birth, race, gender), primary impression, disposition, and vital signs for EMS encounters. A bounceback was defined as a patient, identified by matching first name, last name and date of birth, with more than one EMS encounter within 72 h. We performed descriptive statistics for patients that did and did not have a subsequent bounceback using median (interquartile range) and Wilcoxon Rank Sum test for age and frequency (percent) and chi square test for gender, race and run disposition. For patients with a bounceback, we describe the frequency and percentage of EMS professional primary impressions on initial encounter.

RESULTS

98,043 encounters from January 1, 2021 to December 31, 2021, were analyzed. The median age was 50 years (IQR 32-65); 49.4% (46,147) were female and 50.7% (47,376) were White patients. 3951 encounters had a subsequent bounceback, and compared to those without bouncebacks, they were more often male patients (58.7% versus 50.2%, p < 0.001) and more commonly not transported (22.3% versus 15.5%, p < 0.001). A multivariable logistic regression model estimated the odds of bounceback were lower for females [OR 0.64 (95% CI 0.61-0.68)], Asian and Latino patients compared to White patients [OR 0.33 (95% CI 0.21-0.53) and 0.42 (95% CI 0.34-0.51)], respectively, no significant difference for Black patients compared to White patients, and higher for non-transported patients [OR 1.25 (95% CI 1.16-1.34)]. The The most common EMS primary impression for initial and subsequent encounters was mental health [576 (14.7%) and 944 (17.0%), respectively]. For subsequent encounters, the primary impression was cardiac arrest or death in 67 (1.2%) of cases.

CONCLUSION

Bouncebacks were common in this single year study of a high-volume urban EMS agency. Male and non-transported patients most often experienced bouncebacks. The most common primary impression for encounters with bounceback was mental health related. Out-of-hospital cardiac arrest occurred in 1 % of bounceback cases. Further study is necessary to understand the effect on patient-centered outcomes.

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.

Incidence of diagnostic errors in unplanned hospitalized patients using an automated medical history-taking system with differential diagnosis generator: retrospective observational study (Preprint)

Background

Automated medical history–taking systems that generate differential diagnosis lists have been suggested to contribute to improved diagnostic accuracy. However, the effect of these systems on diagnostic errors in clinical practice remains unknown.

Objective

This study aimed to assess the incidence of diagnostic errors in an outpatient department, where an artificial intelligence (AI)–driven automated medical history–taking system that generates differential diagnosis lists was implemented in clinical practice.

Methods

We conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 years and older who used an AI-driven, automated medical history–taking system that generates differential diagnosis lists in the outpatient department of internal medicine for whom the index visit was between July 1, 2019, and June 30, 2020, followed by unplanned hospitalization within 14 days. The primary endpoint was the incidence of diagnostic errors, which were detected using the Revised Safer Dx Instrument by at least two independent reviewers. To evaluate the effect of differential diagnosis lists from the AI system on the incidence of diagnostic errors, we compared the incidence of these errors between a group where the AI system generated the final diagnosis in the differential diagnosis list and a group where the AI system did not generate the final diagnosis in the list; the Fisher exact test was used for comparison between these groups. For cases with confirmed diagnostic errors, further review was conducted to identify the contributing factors of these errors via discussion among three reviewers, using the Safer Dx Process Breakdown Supplement as a reference.

Results

A total of 146 patients were analyzed. A final diagnosis was confirmed for 138 patients and was observed in the differential diagnosis list from the AI system for 69 patients. Diagnostic errors occurred in 16 out of 146 patients (11.0%, 95% CI 6.4%-17.2%). Although statistically insignificant, the incidence of diagnostic errors was lower in cases where the final diagnosis was included in the differential diagnosis list from the AI system than in cases where the final diagnosis was not included in the list (7.2% vs 15.9%, P=.18).

Conclusions

The incidence of diagnostic errors among patients in the outpatient department of internal medicine who used an automated medical history–taking system that generates differential diagnosis lists seemed to be lower than the previously reported incidence of diagnostic errors. This result suggests that the implementation of an automated medical history–taking system that generates differential diagnosis lists could be beneficial for diagnostic safety in the outpatient department of internal medicine.

Diagnostic Error in the Critically Ill: A Hidden Epidemic?

Epidemiologic studies of diagnostic error in the intensive care unit (ICU) consist mostly of descriptive autopsy series. In these studies, rates of diagnostic errors are approximately 5% to 10%. Recently validated methods for retrospectively measuring error have expanded our understanding of the scope of the problem. These alternative measurement strategies have yielded similar estimates for the frequency of diagnostic error in the ICU. Although there is a fair understanding of the frequency of errors, further research is needed to better define the risk factors for diagnostic error in the ICU.

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.

In-Hospital Outcomes in Patients Admitted to the Intensive Care Unit after a Return Visit to the Emergency Department

Background: Intensive care unit (ICU) admission following a short-term emergency department (ED) revisit has been considered a particularly undesirable outcome among return-visit patients, although their in-hospital prognosis has not been discussed. We aimed to compare clinical outcomes between adult patients admitted to the ICU after unscheduled ED revisits and those admitted during index ED visits. Method: This retrospective study was conducted at two tertiary medical centers in Taiwan from 1 January 2016 to 31 December 2017. All adult non-trauma patients admitted to the ICU directly via the ED during the study period were included and divided into two comparison groups: patients admitted to the ICU during index ED visits and those admitted to the ICU during return ED visits. The outcomes of interest included in-hospital mortality, mechanical ventilation (MV) support, profound shock, hospital length of stay (HLOS), and total medical cost. Results: Altogether, 12,075 patients with a mean (standard deviation) age of 64.6 (15.7) years were included. Among these, 5.3% were admitted to the ICU following a return ED visit within 14 days and 3.1% were admitted following a return ED visit within 7 days. After adjusting for confounding factors for multivariate regression analysis, ICU admission following an ED revisit within 14 days was not associated with an increased mortality rate (adjusted odds ratio (aOR): 1.08, 95% confidence interval (CI): 0.89 to 1.32), MV support (aOR: 1.06, 95% CI: 0.89 to 1.26), profound shock (aOR: 0.99, 95% CI: 0.84 to 1.18), prolonged HLOS (difference: 0.04 days, 95% CI: −1.02 to 1.09), and increased total medical cost (difference: USD 361, 95% CI: −303 to 1025). Similar results were observed after the regression analysis in patients that had a 7-day return visit. Conclusion: ICU admission following a return ED visit was not associated with major in-hospital outcomes including mortality, MV support, shock, increased HLOS, or medical cost. Although ICU admissions following ED revisits are considered serious adverse events, they may not indicate poor prognosis in ED practice.

Feasibility and Reliability Testing of Manual Electronic Health Record Reviews as a Tool for Timely Identification of Diagnostic Error in Patients at Risk

BACKGROUND

Although diagnostic error (DE) is a significant problem, it remains challenging for clinicians to identify it reliably and to recognize its contribution to the clinical trajectory of their patients. The purpose of this work was to evaluate the reliability of real-time electronic health record (EHR) reviews using a search strategy for the identification of DE as a contributor to the rapid response team (RRT) activation.

OBJECTIVES

Early and accurate recognition of critical illness is of paramount importance. The objective of this study was to test the feasibility and reliability of prospective, real-time EHR reviews as a means of identification of DE.

METHODS

We conducted this prospective observational study in June 2019 and included consecutive adult patients experiencing their first RRT activation. An EHR search strategy and a standard operating procedure were refined based on the literature and expert clinician inputs. Two physician-investigators independently reviewed eligible patient EHRs for the evidence of DE within 24 hours after RRT activation. In cases of disagreement, a secondary review of the EHR using a taxonomy approach was applied. The reviewers categorized patient experience of DE as Yes/No/Uncertain.

RESULTS

We reviewed 112 patient records. DE was identified in 15% of cases by both reviewers. Kappa agreement with the initial review was 0.23 and with the secondary review 0.65. No evidence of DE was detected in 60% of patients. In 25% of cases, the reviewers could not determine whether DE was present or absent.

CONCLUSION

EHR review is of limited value in the real-time identification of DE in hospitalized patients. Alternative approaches are needed for research and quality improvement efforts in this field.