EM · Quality and ED metrics
Quality and ED metrics — performance measurement, the four-hour target and quality improvement
Also known as National Emergency Access Target · NEAT · Four-hour rule · Access block · Emergency department overcrowding · Left without being seen · Plan-Do-Study-Act cycle · Statistical process control · Run chart · Benchmarking · Donabedian framework · Door-to-doctor time · Quality improvement in the ED
Quality and emergency department metrics — the Donabedian structure-process-outcome framework; the National Emergency Access Target (NEAT), the four-hour rule requiring 90 per cent of ED presentations discharged or admitted within four hours; the core ED metrics of time-to-treatment (door-to-doctor, door-to-ECG, door-to-antibiotic, door-to-needle), admission rate, LWBS (left without being seen) rate and mortality; access block (length of stay greater than eight hours for an admitted patient) and overcrowding as the dominant drivers of metric failure and excess mortality (Sprivulis, Guttmann, Howlett, Nicolaidis); the differential of drivers of access block (input, throughput and output factors); quality improvement methodology — Plan-Do-Study-Act, run charts with the probability-based rules for special-cause variation, statistical process control (SPC) Shewhart charts distinguishing common-cause from special-cause variation, and benchmarking against peer departments; and the unintended consequences of the target — gaming, premature discharge and short-stay-unit inflation. ACEM-primary, globally tagged.
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8 MCQs with explanations
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Related topics
- The Australasian Triage Scale — categories, validity, reliability and the under-triaged patient
- ED flow and access block — the input-throughput-output model, queuing theory, and the operational intervention ladder
- Patient disposition and safety-netting in the emergency department
- Medical error and patient safety in the emergency department
- Team-based care and crisis resource management in the emergency department
- Research, evidence-based medicine and biostatistics — appraising and applying evidence at the bedside
Meet the patient — Tuesday morning on the floor
It is 09:40 on a Tuesday. There are 16 admitted patients boarding in your department, the ambulance bay holds three trolleys waiting to offload, and the consultant running the floor has not sat down since 07:00. Last quarter's National Emergency Access Target compliance was 64 per cent, down from 81 per cent a year ago. A 30-day mortality audit just flagged excess deaths among patients whose total emergency department length of stay exceeded eight hours. The hospital executive has emailed: "Why can't the emergency department deliver the target?"[2][11]
This is the scenario the Fellowship question is built from, and it plants the one exam question this whole topic answers: access block is a hospital problem that lives in the emergency department — so why do the measurement and the blame land on the emergency department floor? Hold that question and every framework below slots into place.[1]
You have read the opening of this topic. The complete unit — every section and its primary-source references — is part of the Emergency Medicine fellowship atlas.
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- [1]Guttmann A, Schull MJ, Vermeulen MJ, Stukel TA. Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada BMJ, 2011.PMID 21632665
- [2]Sprivulis PC, Da Silva JA, Jacobs IG, Frazer AR, Jelinek GA. The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments Med J Aust, 2006.PMID 16515429
- [3]Forero R, McCarthy S, Hillman K. Impact of the four-hour National Emergency Access Target on 30 day mortality, access block and chronic emergency department overcrowding in Australian emergency departments Emerg Med Australas, 2019.PMID 30062847
- [4]Forero R, Man N, McCarthy S, et al. Impact of the National Emergency Access Target policy on emergency departments' performance: A time-trend analysis for New South Wales, Australian Capital Territory and Queensland Emerg Med Australas, 2019.PMID 30043403
- [5]Ngo H, Forero R, Mountain D, et al. Impact of the Four-Hour Rule in Western Australian hospitals: Trend analysis of a large record linkage study 2002-2013 PLoS One, 2018.PMID 29538401
- [6]Forero R, Man N, Nahidi S, et al. When a health policy cuts both ways: Impact of the National Emergency Access Target policy on staff and emergency department performance Emerg Med Australas, 2020.PMID 31595671
- [7]Khanna S, Boyle J, Good N, Lind J. Analysing the emergency department patient journey: Discovery of bottlenecks to emergency department patient flow Emerg Med Australas, 2017.PMID 27862986
- [8]Vermeulen MJ, Stukel TA, Boozary AS, et al. The Effect of Pay for Performance in the Emergency Department on Patient Waiting Times and Quality of Care in Ontario, Canada: A Difference-in-Differences Analysis Ann Emerg Med, 2016.PMID 26215670
- [9]Hitti E, Hadid D, Tamim H, et al. Left without being seen in a hybrid point of service collection model emergency department Am J Emerg Med, 2020.PMID 31128935
- [10]Scala A, Trunfio TA, Majolo M, et al. Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department BMC Emerg Med, 2025.PMID 40660109
- [11]Howlett N, Cameron J, Wood R Medical patient boarding in the emergency department as a source of crowding and delay-related harm, impacting patient outcomes and the efficiency of urgent and emergency care Emerg Med J, 2026.PMID 41672875
- [12]Nicolaidis R, Bordini Ferro E, Martinho MAD, et al. Emergency department boarding and occupancy differ in their association with early in-hospital mortality: A multicenter cohort Am J Emerg Med, 2026.PMID 42167132
- [13]Taylor MJ, McNicholas C, Nicolay C, Darzi A, Bell D, Reed JE. Systematic review of the application of the plan-do-study-act method to improve quality in healthcare BMJ Qual Saf, 2014.PMID 24025320
- [14]Waqas M, Xu SH, Hussain S, et al. Control charts in healthcare quality monitoring: a systematic review and bibliometric analysis Int J Qual Health Care, 2024.PMID 39018022
- [15]Anhøj J Diagnostic value of run chart analysis: using likelihood ratios to compare run chart rules on simulated data series PLoS One, 2015.PMID 25799549