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Record W3184090280 · doi:10.1111/1742-6723.13777

Should <scp>the Australasian College for Emergency Medicine</scp> advocate for <scp>time‐based targets</scp> in our emergency departments?

2021· editorial· en· W3184090280 on OpenAlexaboutno aff
Peter Jones, Katie Walker

Bibliographic record

VenueEmergency Medicine Australasia · 2021
Typeeditorial
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical emergencyFamily medicineEmergency medicine

Abstract

fetched live from OpenAlex

Time-based targets (TBTs) for ED length of stay (LOS) in Australia and New Zealand (NZ) are based on the English model known as the ‘Four Hour Rule’, which arose in response to concerns that prolonged ED LOS was associated with poor patient outcomes. There is a large body of evidence that suggests that long waits to assessment and admission are associated with an increased rate of adverse events, including excess mortality.1 By 2011, every jurisdiction in Australia and NZ had a policy that a proportion of patients presenting to the ED should be discharged home or admitted to an inpatient ward within 4 h (Australia) or 6 h (NZ).2 The Australasian College for Emergency Medicine (ACEM) has taken the position that TBTs are a useful tool to drive systematic changes in care and improve patient journeys. Clinicians, however, have expressed concerns about the potential negative impacts of TBTs on patients and staff. In 2018, ACEM asked a team of experts to convene to review all available research literature on TBTs. The aim was to work out whether targets were effective in improving patient quality of care and whether ACEM should be advocating for targets when lobbying governments and health departments regarding reducing access block. The team who undertook this analysis consisted of emergency physicians from NZ, and from most states and territories of Australia (including rural, regional and urban representatives); as well as non-FACEM health service researchers; consumers; and inpatient specialists. Since TBTs were initially introduced, multiple studies have been published. The research team were able to review data from nearly 50 studies, including over 34 million patients from Australia, Canada, England, Ireland and NZ. Despite massive patient numbers in studies, the quantitative evidence in the literature informing this review was mostly low and, in some cases, very low quality. This is typical for evaluations of complex health service interventions. The strongest and most consistent finding was that targets were associated with a reduction in ED LOS for admitted patients and reduced access block. Other positive findings include a significant reduction in the proportion of patients who ‘did-not-wait’.3 Targets may also save lives. High quality evidence from NZ demonstrated a significant reduction in mortality for patients in ED after the introduction of targets, but no effect on mortality for inpatients or post-discharge. Some studies from Australia and Ireland showed that targets were associated with small to moderate reductions in mortality for in-patients but results were inconsistent and some studies were considered to be of very low quality. There was not a clear, consistent association between TBTs and reduction in mortality for patients after discharge across different sites in Australia, NZ and Ireland. Although there were some studies showing a strong positive association between targets and reduction in mortality, differing findings between settings indicates that there is variability in how targets have been implemented and the impact of targets. Where TBTs are used as a goal to drive whole-of-hospital changes, such as improving inpatient bed capacity, ED crowding is reduced and patients experience better outcomes. The qualitative data supports the interpretation that targets have a positive impact on quality of care when they are used to drive whole-of-hospital systems reforms and are supported by adequate resourcing including funding and staffing. Potential negative impacts of the targets occur when achieving the target is prioritised over patient care activities, and staff suffer increased workload and reduced morale.2 After undertaking the review, physician members of the research team individually undertook a structured appraisal process to determine whether TBTs were something that ACEM should pursue. A consensus was reached that targets should be used. This was conditional on appropriate safeguards being built into the performance measurement regimen, both at local and regional levels, to reduce gaming.4 We hope that the evidence provided in the systematic reviews2, 3 will redirect conversations about targets towards how to improve patient care rather than arguing about the usefulness of targets. There is considerable heterogeneity in the detail of targets and the regimes implemented around them in different jurisdictions. This raises the questions: which targets? and which thresholds should be used? ED LOS is most associated with safety, effectiveness of care and equity compared to alternate measures of patient flow and is likely to be the best performing metric.1 Unachievable targets that have short time frames and high thresholds, coupled with excessive top-down pressure and financial incentives will lead to a high risk of target gaming.5 If these traps are avoided, then the exact details of the target become less important. The key factor is that all actors within the system understand the rationale for the target (to improve patient outcomes by improving acute care systems) and work together to improve their system of care. This requires state and/or national government support, local health authority attention and most importantly engagement of acute care clinicians in the hospital and the ED. Get this right and whichever target is chosen will look after itself. Targets fail when there is insufficient understanding of the rationale for them and there is too much focus on the number rather than the process of system improvement. It is important to remember that TBTs were never intended to be the only way to measure quality of care in an ED. They should be used in conjunction with a suite of acute care quality indicators chosen for local relevance, preferably using a quality indicator appraisal tool designed for that purpose.6 On the basis of this body of work, ACEM reviewed its policy position on TBTs for Australian and NZ EDs. The weight of the evidence suggests that TBTs stand to enhance the safety and quality of emergency care, but must be implemented using sufficiently resourced, whole-of-hospital approaches to systems improvement. PJ has received funding for TBTs research from the Health Research Council of New Zealand. ACEM provided infrastructure and administrative support for the review of TBTs. We would like to acknowledge Nicola Ballenden, Allison Roper, Helena Mayer, Frances Sutherland, Lee Moskwa and Sarah Smith for their help to enable the successful completion of this work. PJ and KW have published research on TBTs and are emergency physicians working in Australian and NZ EDs. PJ and KW are section editors for Emergency Medicine Australasia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.280
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.034
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.365
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2021
Admission routes1
Has abstractyes

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