The performance limits of traditional triage

National Early Warning Score in Predicting Severe Adverse Outcomes of Emergency Medicine Patients: A Retrospective Cohort Study

Background

For emergency triage, it is very important to identify patient severity according to their vital signs and chief complaint. Several studies have examined the predictive value of the National Early Warning Score (NEWS) for specific emergency patients and have shown it to be effective. However, few have studied the utility of NEWS in emergency triage for general emergency medicine patients. The aim of this research was to investigate the performance of NEWS in emergency triage with regard to predicting adverse outcomes.

Methods

This was a retrospective cohort study carried out at a tertiary care center hospital in Jinhua, China. A total of 62,403 patients attending the emergency department (ED) from January to December 2018 were included. The NEWS, Modified Early Warning Score (MEWS), and quick Sepsis Related Organ Failure Assessment (qSOFA) score were obtained from emergency triage. Multivariate logistic regression analysis was performed to evaluate the associations between the NEWS, MEWS, and qSOFA, as well as those between other parameters with ED mortality. The predictive performances for emergency observation, death, and intensive care unit (ICU) admission of NEWS, MEWS and qSOFA were compared to the area under the receiver operating characteristic curve (AUROC).

Results

Of the total participants, 6502 were placed under emergency observation, 106 died in the ED, 638 were admitted to the ICU, and 324 died in-hospital. The NEWS, qSOFA, age, and gender were significantly associated with ED mortality. NEWS was significantly better at discriminating all outcomes, and the area under the curve and 95% confidence intervals for ED mortality, observation in ED, composite of ED mortality and ICU admission, and in-hospital mortality were 0.862 (0.859-0.865), 0.691 (0.687-0.695), 0.859 (0.856-0.861), and 0.805 (0.802-0.808), respectively.

Conclusion

NEWS shows good performance in discriminating critical emergency patients in ED triage for emergency medicine patients.

Decreasing triage time: effects of implementing a step-wise ESI algorithm in an EHR

Objectives

To determine if adapting a widely-used triage scale into a computerized algorithm in an electronic health record (EHR) shortens emergency department (ED) triage time.

Design

Before-and-after quasi-experimental study.

Setting

Urban, tertiary care hospital ED.

Participants

Consecutive adult patient visits between July 2011 and June 2013.

Intervention

A step-wise algorithm, based on the Emergency Severity Index (ESI-5) was programmed into the triage module of a commercial EHR.

Main Outcome Measures

Duration of triage (triage interval) for all patients and change in percentage of high acuity patients (ESI 1 and 2) completing triage within 15 min, 12 months before-and-after implementation of the algorithm. Multivariable analysis adjusted for confounders; interrupted time series demonstrated effects over time. Secondary outcomes examined quality metrics and patient flow.

Results

About 32 546 patient visits before and 33 032 after the intervention were included. Post-intervention patients were slightly older, census was higher and admission rate slightly increased. Median triage interval was 5.92 min (interquartile ranges, IQR 4.2-8.73) before and 2.8 min (IQR 1.88-4.23) after the intervention (P < 0.001). Adjusted mean triage interval decreased 3.4 min (95% CI: -3.6, -3.2). The proportion of high acuity patients completing triage within 15 min increased from 63.9% (95% CI 62.5, 65.2%) to 75.0% (95% CI 73.8, 76.1). Monthly time series demonstrated immediate and sustained improvement following the intervention. Return visits within 72 h and door-to-balloon time were unchanged. Total length of stay was similar.

Conclusion

The computerized triage scale improved speed of triage, allowing more high acuity patients to be seen within recommended timeframes, without notable impact on quality.

Validation of deep-learning-based triage and acuity score using a large national dataset

AIM

Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Score (DTAS) identifies high-risk patients more accurately than existing triage and acuity scores using a large national dataset.

METHODS

We conducted a retrospective observational cohort study using data from the Korean National Emergency Department Information System (NEDIS), which collected data on visits in real time from 151 EDs. The NEDIS data was split into derivation data (January 2014-June 2016) and validation data (July-December 2016). We also used data from the Sejong General Hospital (SGH) for external validation (January-December 2017). We predicted in-hospital mortality, critical care, and hospitalization using initial information of ED patients (age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs and mental status as predictor variables).

RESULTS

A total of 11,656,559 patients were included in this study. The primary outcome was in-hospital mortality. The Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC) of DTAS were 0.935 and 0.264. It significantly outperformed Korean triage and acuity score (AUROC:0.785, AUPRC:0.192), modified early warning score (AUROC:0.810, AUPRC:0.116), logistic regression (AUROC:0.903, AUPRC:0.209), and random forest (AUROC:0.910, AUPRC:0.179).

CONCLUSION

Deep-learning-based Triage and Acuity Score predicted in-hospital mortality, critical care, and hospitalization more accurately than existing triages and acuity, and it was validated using a large, multicenter dataset.

Using Data to Drive Emergency Department Design: A Metasynthesis

Objective:

There has been an uptick in the field of emergency department (ED) operations research and data gathering, both published and unpublished. This new information has implications for ED design. The specialty suffers from an inability to have these innovations reach frontline practitioners, let alone design professionals and architects. This paper is an attempt to synthesize for design professionals the growing data regarding ED operations.

Methods:

The following sources were used to capture and summarize the research and data collections available regarding ED operations: the Emergency Department Benchmarking Alliance database; a literature search using both PubMed and Google Scholar search engines; and data presented at conferences and proceedings.

Results:

Critical information that affects ED design strategies is summarized, organized, and presented. Data suggest an optimal size for ED functional units. The now-recognized arrival and census curves for the ED suggest a department that expands and contracts in response to changing census. Operational improvements have been clearly identified and are grouped into three categories: input, throughput, and outflow. Applications of this information are suggested.

Conclusion:

The sentinel premise of this meta-synthesis is that data derived from improvement work in the area of ED operations has applications for ED design. EDs can optimize their functioning by marrying good processes and operations to good design. This review paper is an attempt to bring this new information to the attention of the multidisciplinary team of architects, designers, and clinicians.