Establishing Research Priorities for the Emergency Severity Index Using a Modified Delphi Approach

Triage Accuracy in Pediatrics Using the Emergency Severity Index

INTRODUCTION

Although the Emergency Severity Index is the most widely used tool in the United States to prioritize care for patients who seek emergency care, including children, there are significant deficiencies in the tool's performance. Inaccurate triage has been associated with delayed treatment, unnecessary diagnostic testing, and bias in clinical care. We evaluated the accuracy of the Emergency Severity Index to stratify patient priority based on predicted resource utilization in pediatric emergency department patients and identified covariates influencing performance.

METHODS

This cross-sectional, retrospective study used a data platform that links clinical and research data sets from a single freestanding pediatric hospital in the United States. Chi-square analysis was used to describes rates of over- and undertriage. Mixed effects ordinal logistic regression identified associations between Emergency Severity Index categories assigned at triage and key emergency department resources using discrete data elements and natural language processing of text notes.

RESULTS

We analyzed 304,422 emergency department visits by 153,984 unique individuals in the final analysis; 80% of visits were triaged as lower acuity Emergency Severity Index levels 3 to 5, with the most common level being Emergency Severity Index 4 (43%). Emergency department visits scored Emergency Severity Index levels 3 and 4 were triaged accurately 46% and 38%, respectively. We noted racial differences in overall triage accuracy.

DISCUSSION

Although the plurality of patients was scored as Emergency Severity Index 4, 50% were mistriaged, and there were disparities based on race indicating Emergency Severity Index mistriages pediatric patients. Further study is needed to elucidate the application of the Emergency Severity Indices in pediatrics using a multicenter emergency department population with diverse clinical and demographic characteristics.

Cognitive biases regarding utilization of emergency severity index among emergency nurses

AIM

The study aimed to measure emergency nurses' prevalence of cognitive biases when utilizing Emergency Severity Index (ESI). Moreover, the study aimed to measure the differences between cognitive biases and demographic variables.

BACKGROUND

Nurses use Emergency Severity Index (ESI) to prioritize the patients. Cognitive biases could compromise the clinical decisions of nurses in triage. Consequently, this hinders the delivery of safe and quality patient care.

METHODS

A cross-sectional analytical approach invited 208 emergency nurses from four tertiary care hospitals. Institutional review board approval and permission from institutional heads were obtained. Informed consent was attained before data collection. Data was collected through a structured scenario-based questionnaire to measure cognitive biases at five levels of ESI. Descriptive and inferential statistics were obtained through v25.0 of SPSS.

RESULTS

Among the 86.6% response rate, 56.2% of nurses were male. 62.90% had nursing diplomas. Cognitive biases were present at all ESI levels one to five, in order 51%, 45%, 90%, 89%, and 91% among nurses. Premature closure 22%, tolerance to risk 12%, satisfying bias 25%, framing effect 22%, and blind obedience 34% from level one to five consecutively. Demographic variables, including males, experience between 2 and 5 years, general nursing as qualification, and without emergency severity index certification, were identified to encounter more cognitive biases when making triage decisions.

CONCLUSION

Numerous cognitive biases are considerably existing among emergency nurses when prioritizing patients. Cognitive de-biasing measures can improve triage decisions among nurses that could enhance quality care and patient safety.

Triage: A Global Perspective

Triage is a process by which patients are assessed, classified, and sorted based on their presenting complaint and clinical urgency, providing assurance for timely access to emergency care. The goal is to get the right person to the right place, in the right amount of time, for the right reason, and within the context of resource availability. In many countries, a standardized triage system, underpinned through the use of guidelines, is used to provide clinicians with support and guidance. Triage is a globally adopted principle, and although triage guidelines are used in many countries, no single system has been internationally adopted. This paper discusses the importance of how triage process standardization improves patient care, resource management, and benchmarking at local, national, and international levels by applying 5 internationally recognized triage systems to fictional case studies. Evaluation of similarities and differences in severity scores, with a gap analysis, occurs.

Perception Bias Effects on Healthcare Management in COVID-19 Pandemic: An Application of Cumulative Prospect Theory

Coronavirus disease 2019 (COVID-19) has posed severe threats to human safety in the healthcare sector, particularly in residents in long-term care facilities (LTCFs) at a higher risk of morbidity and mortality. This study aims to draw on cumulative prospect theory (CPT) to develop a decision model to explore LTCF administrators’ risk perceptions and management decisions toward this pandemic. This study employed the policy Delphi method and survey data to examine managers’ perceptions and attitudes and explore the effects of sociodemographic characteristics on healthcare decisions. The findings show that participants exhibited risk aversion for small losses but became risk-neutral when considering devastating damages. LTCF managers exhibited perception bias that led to over- and under-estimation of the occurrence of infection risk. The contextual determinants, including LTCF type, scale, and strategy, simultaneously affect leaders’ risk perception toward consequences and probabilities. Specifically, cost-leadership facilities behave in a loss-averse way, whereas hybrid-strategy LTCFs appear biased in measuring probabilities. This study is the first research that proposes a CPT model to predict administrators’ risk perception under varying mixed gain–loss circumstances involving considerations of healthcare and society in the pandemic context. This study extends the application of CPT into organizational-level decisions. The results highlight that managers counteract their perception bias and subjective estimation to avoid inappropriate decisions in healthcare operations and risk governance for a future health emergency.