Deconstructing the 4-h rule for access to emergency care and putting patients first

Length of Stay in Emergency Department by ICD-10 Specific and Non-Specific Diagnoses: A Single-Centre Retrospective Study

The definition of non-specific presentation at a hospital emergency department (ED) has not yet been formally established. The purpose of this study was to assess the relationships between primary ED diagnoses identified by ICD-10 codes and ED length of stay (LOS). Over the course of three years, we examined 134,675 visits at a tertiary hospital. LOS was examined in groups with specific (internal, surgical, neurological, and traumatic diseases) and non-specific diagnoses. Our secondary objective was to measure LOS by age, day of the week, time of day, and season. The median LOS was 182 min (interquartile range: 99-264 min). LOS was 99 min in the traumatic group, while it was 132 min in the surgical group, 141 min in the non-specific group, 228 min in the internal medicine group, and 237 min in the neurological group. Other determinants of LOS were age, revisits, day of the week, and time of arrival-but not a season of the year. In the non-specific group (21% of all diagnoses), the percentage of hospitalizations was higher than in the specific groups. Our results suggest that in clinical practice, the non-specific group should be redefined to also encompass diagnoses from ICD-10 Chapter XXI (block Z00-Z99).

Intensive care unit admission from the emergency department in the setting of National Emergency Access Targets

Purpose

Since the introduction of National Emergency Access Targets (NEATs) in 2012 there has been little research examining patients admitted to the intensive care unit (ICU).We assessed differences in baseline characteristics and outcomes of patients admitted from the Emergency Department (ED) to the ICU within 4 hours compared with patients who were not.

Methods

This retrospective observational study included all adults (≥18 years old) admitted to the ICU from the ED of Austin Hospital, Melbourne, Australia, between 1 January 2017 and 31st December 2019 inclusive.

Results

1544 patients were admitted from the ED to the ICU and 65% had an ED length of stay (EDLOS) > 4 hour. Such patients were more likely to be older, female, with less urgent triage category scores and lower illness severity. Sepsis and respiratory admission diagnoses, and winter presentations were significantly more prevalent in this group.After adjustment for confounders, patients with an EDLOS > 4 hours had lower hospital mortality; 8% v 21% (p = 0.029; OR, 1.62), shorter ICU length of stay 2.2 v 2.4 days (p = 0.043), but a longer hospital length of stay 6.2 v 6.8 days (p = < 0.001).

Conclusion

Almost two thirds of patients breached the NEAT of 4 hours. These patients were more likely to be older, female, admitted in winter with sepsis and respiratory diagnoses, and have lower illness severity and less urgent triage categories. NEAT breach was associated with reduced hospital mortality but an increased hospital length of stay.

Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

Background

Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding.

Objective

The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding.

Methods

We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies.

Results

Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments.

Conclusions

There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

The impact of Australian healthcare reforms on emergency department time-based process outcomes: An interrupted time series study

Background

In 2011, the Australian government introduced national healthcare reforms aimed at increasing the timeliness and quality of hospital care. The healthcare reforms included, but were not limited to, emergency department (ED) time-based targets, financial incentives, and public performance reporting of hospital data. We sought to evaluate the impact of the national healthcare reforms on ED time-based process outcomes.

Methods

A quasi-experimental study of ED presentations from 2006 to 2016 in the state of Victoria, Australia. Uncontrolled, interrupted time-series analyses were used to evaluate, by hospital peer groups, the effect of national healthcare reforms on: patient wait times for treatment; treatment within recommended time; and patient departure within four hours of arrival in ED.

Results

There were small improvements in ED time-based process outcomes following the introduction of the national healthcare reforms. These occurred in most hospital peer groups immediately and over the longer term, across the various triage categories. The largest improvements occurred in small hospitals and smallest improvements in medium sized hospitals. ED time-based targets, now abolished by the Australian government, were not achieved in any hospital peer groups.

Conclusions

Our findings suggest that national healthcare reforms had the potential to prompt fundamental changes in ED processes leading to significant improvements in ED performances across most hospital peer groups but were generally unable to reach the ED targets imposed nationally. ED performances also varied by hospital peer groups. Attention to ED time-based process outcomes within hospital peer groups may provide insights into hospital practices that could improve the quality and efficiency of ED care.