ABSTRACT
Objectives
To evaluate the impact of an embedded Ultra‐Low Acuity Pathway (ULAP) on emergency department performance indicators for low‐acuity patients in a rural referral hospital.
Methods
This pragmatic, single‐centre, quasi‐experimental study included Australian Triage Scale Categories 4 and 5 patients presenting to Dubbo Health Service between 10:00 and 20:00 from 29 May to 1 October 2023 (n = 4798). Workforce‐dependent ULAP availability created a comparison between ULAP (n = 2178) and standard care periods (n = 2620). Primary outcomes were Did Not Wait (DNW), Seen Within Benchmark (SWB), Emergency Treatment Performance (ETP) and length of stay (LOS). Multivariable analyses adjusted for patient characteristics, temporal factors and resource state.
Results
ULAP was associated with improvements across all outcomes. DNW decreased from 14.2% to 6.9% (adjusted OR 0.43, 95% CI 0.35–0.53), SWB increased from 72.6% to 82.0% (adjusted OR 1.90, 95% CI 1.64–2.20) and ETP increased from 73.1% to 83.3% (adjusted OR 1.96, 95% CI 1.69–2.28). Mean LOS was 51.7 min shorter after adjustment (95% CI −65.1 to −38.4). All associations remained statistically significant (p < 0.001).
Conclusions
An embedded ULAP was associated with substantial improvements in emergency department performance that were independent of patient characteristics, temporal factors and resource state. Embedding urgent care within existing emergency departments may provide a practical alternative to stand‐alone urgent care services for rural communities where separate services are not feasible.
1. Introduction
Emergency Departments (EDs) across Australia are experiencing sustained growth in presentations, contributing to overcrowding and access block [1]. Access block, whereby admitted patients remain in the ED awaiting inpatient beds, is the principal driver of overcrowding and is associated with increased length of stay, morbidity and mortality [2]. Although access block underpins systemic congestion, the contribution of lower‐acuity presentations to overall departmental performance remains incompletely understood [3, 4].
Although individual low‐acuity presentations consume relatively few resources, their high volume in rural and regional Australia can place substantial demands on staff, clinical space and patient flow [5, 6]. Departments managing large numbers of low‐acuity patients may experience poorer operational performance, particularly where dedicated low‐acuity capacity is limited [7]. Rural EDs consistently report higher proportions of Australian Triage Scale (ATS) Categories 4 and 5 presentations than metropolitan services, reflecting differing patterns of healthcare access and demand [5, 8].
Limited access to primary care is frequently proposed as a driver of low‐acuity ED presentations, although estimates of the proportion suitable for primary care vary widely and these patients contribute little to access block [9, 10, 11]. Nevertheless, concern regarding low‐acuity demand has driven investment in stand‐alone Urgent Care Services (UCS) intended to reduce pressure on EDs [12, 13, 14, 15].
Although some UCS reduce ED presentations, their overall effects are modest and may be offset by induced demand and increased healthcare utilisation [16, 17, 18]. In rural settings, workforce shortages and lower population density further limit the viability of stand‐alone UCS [19]. This creates a paradox: rural EDs carry the greatest relative burden of low‐acuity presentations, yet many rural communities cannot sustain separate urgent care services because of workforce and population constraints [19, 20].
To address this gap, we evaluated the Ultra‐Low Acuity Pathway (ULAP), an embedded model of care designed for settings where stand‐alone urgent care services are unfeasible, and its impact on key emergency department performance indicators.
2. Methods
2.1. Study Design and Setting
This pragmatic, single‐centre, quasi‐experimental study was conducted in the ED at Dubbo Health Service, a Rural Referral Hospital in Western New South Wales. The ED manages approximately 40,000 presentations annually; 25% of patients are admitted, 25% are under 16 years of age, 32% identify as Aboriginal or Torres Strait Islander and approximately 54% are allocated ATS categories 4 and 5.
The ULAP operated between 29 May and 1 October 2023 as an additional clinical stream alongside existing acute and fast‐track streams.
2.2. ULAP Model of Care
The ULAP intervention is described using the TIDieR framework [21]. Patients meeting standardised criteria (Figure A1) were identified at triage, flagged in the electronic medical record, and allocated directly to ULAP. A dedicated consulting room within the ED was allocated to ULAP. Rural Generalists or General Practitioners with advanced emergency medicine skills staffed ULAP and managed patients independently, with escalation to the supervising emergency physician as required.
Unlike Fast Track models, which flexibly manage a broad range of lower‐acuity presentations but may progressively expand to manage higher‐acuity conditions according to overall departmental demand, ULAP was anchored to a predefined ultra‐low‐acuity cohort. This ensured that senior decision‐making capacity remained continuously available for this patient cohort rather than being progressively redirected towards higher‐acuity work during periods of departmental congestion. The operational relationship between ULAP, Fast Track and Acute/Resuscitation streams is illustrated in Figure 1.
FIGURE 1.
Operational relationship between the Ultra‐Low Acuity Pathway (ULAP), Fast Track and acute/resuscitation streams.
ULAP operated daily between 10:00 and 20:00 whenever a clinician was available. Due to workforce shortages, ULAP was not operational every day, creating a natural comparison between ULAP‐on and standard care days. ULAP operational status was determined by retrospective review of the medical officer roster.
2.3. Participant Selection and Data Collection
Unit record data for all ED presentations were extracted from the ED Data Collection (EDDC). Inclusion criteria were presentations triaged ATS Category 4 or 5, arriving 10:00–20:00, with mode of separation recorded as treatment complete or did not wait. Presentations were classified as ULAP or standard care. Additional operational data, including ULAP status, were obtained from departmental records and merged with the EDDC dataset. All data were de‐identified and stored securely in accordance with NSW Health data governance policies.
2.4. Outcome Measures
Primary outcomes were operational performance indicators routinely reported for EDs in New South Wales:
Did Not Wait (DNW): proportion of patients who departed before commencement of clinical care.
Seen Within Benchmark (SWB): proportion seen within ATS benchmark time.
Emergency Treatment Performance (ETP): proportion with total ED time ≤ 4 h.
Length of stay (LOS): total ED time in minutes.
DNW, SWB and ETP were treated as binary outcomes; LOS as continuous.
2.5. Resource State
To explore whether the observed effects of ULAP could be explained by overall departmental staffing rather than the model of care itself, a resource state variable was created. The Dubbo ED has an intended consultant and clinical staffing profile; however, workforce vacancies, unplanned leave and consultant shifts covered by registrars mean the planned staffing profile is not consistently achieved. Resource state was therefore developed to reflect the overall medical staffing available during ULAP operating hours (10:00–20:00). High resource days comprised four FACEMs with no vacant clinical shifts, Low resource days comprised two FACEMs with one or more vacant clinical shifts and all other staffing combinations were classified as Medium resource.
2.6. Statistical Analysis
Categorical outcomes were compared using chi‐square tests. Effect estimates were reported as absolute risk reduction, relative risk and number needed to treat with 95% confidence intervals.
Multivariable logistic regression models adjusted for age, Aboriginal or Torres Strait Islander status, ATS category, arrival time and day of week. Sensitivity analyses additionally adjusted for resource state. Interaction between ULAP and resource state was tested for all outcomes.
Length of stay was analysed using multivariable linear regression with adjustment for the same covariates. Both parametric (Welch's t‐test) and non‐parametric (Mann–Whitney U test) approaches were used for unadjusted comparisons.
Unadjusted analyses were conducted using Microsoft Excel. Multivariable analyses used IBM SPSS Statistics (version 32). Statistical significance was defined as α = 0.05.
2.7. Use of Artificial Intelligence
Large language models (ChatGPT‐4, OpenAI; and OpenEvidence) provided editorial assistance, literature searching support, and guidance on statistical test selection. All statistical analyses were performed by the authors. All outputs were interpreted, verified and reported by the authors.
2.8. Ethics Approval
The project was reviewed by the Executive Officer of the Greater Western Human Research Ethics Committee, who advised that no ethical risks requiring HREC submission were identified in accordance with NSW Health Policy GL2007_020 (GWAHS 2024‐092). The study was conducted as a retrospective service evaluation within existing governance processes. All data were de‐identified prior to analysis.
3. Results
3.1. Baseline Characteristics
A total of 4798 eligible presentations were included as follows: 2178 ULAP encounters and 2620 standard care encounters. The groups were similar with respect to age, sex, Aboriginal or Torres Strait Islander status and ATS category (Table 1). Day‐of‐week and resource state distributions differed between groups (both p < 0.001), reflecting workforce‐dependent ULAP availability.
TABLE 1.
Baseline demographic and clinical characteristics of eligible presentations during ULAP and standard care periods.
| Characteristic | ULAP On | Standard care | p |
|---|---|---|---|
| Participants, n | 2178 | 2620 | |
| Age, mean (SD), years | 33.2 (23.8) | 32.8 (23.9) | 0.56 |
| Age, median (IQR), years | 29 (14–50) | 28 (14–50) | |
| Sex, n (%) | 0.26 | ||
| Male | 1128 (51.8) | 1314 (50.2) | |
| Female | 1050 (48.2) | 1306 (49.8) | |
| Aboriginal or Torres Strait Islander, n (%) | 709 (32.6) | 829 (31.7) | 0.50 |
| ATS category, n (%) | 0.45 | ||
| ATS 4 | 1476 (67.8) | 1802 (68.8) | |
| ATS 5 | 702 (32.2) | 818 (31.2) | |
| Day of week, n (%) | < 0.001 | ||
| Monday | 352 (16.2) | 469 (17.9) | |
| Tuesday | 317 (14.6) | 360 (13.7) | |
| Wednesday | 328 (15.1) | 318 (12.1) | |
| Thursday | 333 (15.3) | 334 (12.7) | |
| Friday | 324 (14.9) | 351 (13.4) | |
| Saturday | 436 (20.0) | 213 (8.1) | |
| Sunday | 88 (4.0) | 575 (21.9) | |
| Arrival time, median (IQR) | 14 (12–17) | 14 (12–16) | 0.08 |
| Resource state, n (%) | < 0.001 | ||
| High resource | 189 (8.7) | 435 (16.6) | |
| Medium resource | 1593 (73.1) | 1558 (59.5) | |
| Low resource | 396 (18.2) | 627 (23.9) |
Note: Resource state: High = 4 FACEMs + 0 vacancies; Medium = all other combinations; Low = 2 FACEMs + ≥ 1 vacancy.
Abbreviations: ATS, Australian Triage Scale; IQR, interquartile range; SD, standard deviation.
3.2. Primary Outcomes
ULAP was associated with improvements across all four primary outcomes (Table 2). Did Not Wait rates were halved, from 14.2% to 6.9% (absolute risk reduction 7.3%, NNT 14). Approximately 10 percentage points more patients were seen within benchmark times and met the 4‐h emergency treatment performance target under ULAP. Mean length of stay was 44 min shorter. All differences were statistically significant (p < 0.001).
TABLE 2.
Unadjusted comparisons of operational outcomes between ULAP and standard care.
| Outcome | ULAP n/N (%) or mean | Standard care n/N (%) or mean | Effect estimate (95% CI) | p |
|---|---|---|---|---|
| DNW | 150/2178 (6.9) | 371/2620 (14.2) | RR 0.49 (0.41–0.58) | < 0.001 |
| SWB | 1786/2178 (82.0) | 1902/2620 (72.6) | RR 1.13 (1.09–1.17) | < 0.001 |
| ETP a | 1814/2178 (83.3) | 1916/2620 (73.1) | RR 1.14 (1.10–1.18) | < 0.001 |
| LOS (mean, minutes) | 181.8 (191.3) | 225.8 (199.3) | MD −44.0 (−55.1 to −32.9) | < 0.001 |
| LOS (median, minutes) | 137 (82–223) | 190 (121–274) | z = −13.61 | < 0.001 |
Abbreviations: CI, confidence interval; DNW, did not wait; ETP, emergency treatment performance; LOS, length of stay; MD, mean difference; RR, relative risk.
SWB, seen within benchmark.
ETP comparison includes 4783 presentations after exclusion of 15 encounters with missing or invalid time‐stamp data.
3.3. Adjusted Analyses
After adjustment for age, Aboriginal or Torres Strait Islander status, ATS category, arrival time and day of week, ULAP remained independently associated with improved performance across all outcomes (Table 3). All associations remained statistically significant (p < 0.001). Further adjustment for resource state produced minimal change in the estimated associations, suggesting that differences in departmental staffing did not account for the observed effects.
TABLE 3.
Unadjusted and sequentially adjusted associations between ULAP implementation and emergency department outcomes.
| Outcome | Crude effect (95% CI) | Adjusted model 1 a (95% CI) | Adjusted model 2 b (95% CI) |
|---|---|---|---|
| DNW | OR 0.45 (0.37–0.55) | OR 0.43 (0.35–0.53) | OR 0.44 (0.36–0.55) |
| SWB | OR 1.71 (1.50–1.95) | OR 1.90 (1.64–2.20) | OR 1.88 (1.62–2.18) |
| ETP | OR 1.84 (1.60–2.12) | OR 1.96 (1.69–2.28) | OR 1.90 (1.63–2.21) |
| LOS | ‐44.0 (−55.1 to −32.9) | −51.7 (−65.1 to −38.4) | −40.9 (−59.7 to −22.2) |
Note: All associations p < 0.001.
Abbreviations: CI, confidence interval; DNW, did not wait; ETP, emergency treatment performance; LOS, length of stay; OR, odds ratio; SWB, seen within benchmark.
Adjusted for age, ATS category (4 vs. 5), Aboriginal or Torres Strait Islander status, arrival time and day of week.
Model 1 additionally adjusted for resource state.
3.4. Sensitivity Analysis: Operational Resource State
Across all resource states, ULAP demonstrated superior performance compared with standard care (Table 4). The greatest separation occurred during low‐resource periods, when standard care performance deteriorated substantially while ULAP performance remained comparatively stable.
TABLE 4.
ULAP performance across operational resource states.
| Outcome | Low resource | Medium resource | High resource |
|---|---|---|---|
| DNW | |||
| ULAP | 9.1% (36/396) | 6.5% (103/1593) | 5.8% (11/189) |
| Standard care | 23.5% (141/627) | 11.3% (176/1558) | 12.4% (54/435) |
| SWB | |||
| ULAP | 83.8% (332/396) | 80.3% (1279/1593) | 85.3% (161/189) |
| Standard care | 71.0% (445/627) | 73.3% (1142/1558) | 70.1% (305/435) |
| ETP | |||
| ULAP | 85.0% (337/396) | 82.5% (1314/1593) | 82.6% (156/189) |
| Standard care | 63.2% (396/627) | 75.9% (1182/1558) | 74.7% (325/435) |
| LOS (mean, min) | |||
| ULAP | 169 (SD 178) | 186 (SD 196) | 180 (SD 177) |
| Standard care | 248 (SD 204) | 217 (SD 196) | 240 (SD 207) |
Note: Resource state: High = 4 FACEMs +0 vacancies; Medium = all other combinations; Low = 2 FACEMs + ≥ 1 vacancy.
Abbreviations: DNW, did not wait; ETP, emergency treatment performance; LOS, length of stay; SD, standard deviation; SWB, seen within benchmark.
4. Discussion
This study found that an embedded ULAP was associated with clinically meaningful improvements across all four operational performance measures.
When the pathway was operational, patients were more likely to be seen within ATS benchmark times, more likely to meet Emergency Treatment Performance targets, and experienced substantially shorter ED stays. Most notably, did‐not‐wait rates were approximately halved. These findings demonstrate that a senior clinician‐led intervention embedded within existing ED infrastructure can improve patient flow and key emergency department performance measures.
The operational distinction was that ULAP maintained senior decision‐making capacity at the lowest‐acuity end of the presentation spectrum, whereas conventional lower‐acuity streams frequently expand to manage progressively higher‐acuity patients during periods of demand.
The observed associations remained significant after adjustment for age, Aboriginal or Torres Strait Islander status, ATS category, arrival time and day of week, suggesting that the improvements were not attributable to differences in patient mix or temporal factors. Although the standardised effect size for length of stay was statistically small (Cohen's d = 0.23), the absolute reduction of approximately 44 min per patient is operationally meaningful across large numbers of presentations.
Further adjustment for resource state produced minimal attenuation of the observed associations, suggesting that differences in departmental staffing did not account for the observed effects. The insulated ultra‐low acuity stream, operating alongside fast‐track and acute/resuscitation streams, maintained superior performance during staffing shortages, suggesting that urgent care‐equivalent services can be delivered within existing ED infrastructure without requiring separate facilities or governance arrangements.
For communities where stand‐alone urgent care services are unfeasible, an embedded ULAP model offers a practical alternative [20]. Urgent care services have had relatively poor penetration into rural healthcare settings because of insufficient patient volume and infrastructure limitations [20]. Embedded models leverage existing resources, including diagnostics, nursing support and specialist emergency physician oversight when needed [22, 23].
Embedded models improve ED capacity to manage lower‐acuity patients rather than relying on strategies to divert this cohort away from the ED, an approach that has shown mixed effectiveness and sustainability challenges [16, 24]. By improving the timeliness of care for all low‐acuity patients within existing emergency systems, the model may contribute to more equitable service delivery for groups who rely heavily on EDs for healthcare access while preserving the principles of clinical urgency embedded within the Australian Triage Scale [25].
Future studies should evaluate the model across multiple sites and healthcare settings to determine its generalisability and identify contextual factors associated with successful implementation.
4.1. Limitations
This study has several important limitations. As a quasi‐experimental study without randomisation, unmeasured confounding remains possible despite adjustment for patient demographics, ATS category and temporal factors. ULAP availability was determined by workforce availability rather than random assignment, which may have introduced calendar effects despite adjustment for day of week. The intervention was delivered by clinicians with varying levels of emergency medicine experience; the relative contribution of individual clinician factors versus the pathway itself cannot be determined. Finally, this was a single‐centre study in a rural referral hospital; metropolitan EDs may have different baseline flow capacity and patterns of patient presentation, which may limit generalisability to urban settings [26].
5. Conclusion
The ULAP demonstrated consistent improvements across all key performance measures for low‐acuity emergency presentations. The model achieved meaningful reductions in did‐not‐wait rates, improved timeliness of care and shortened length of stay without requiring separate infrastructure. For rural and regional communities where stand‐alone urgent care services are unfeasible, ULAP provides a practical alternative that strengthens care delivery within existing emergency systems.
Ethics Statement
The project was reviewed by the Executive Officer of the Greater Western Human Research Ethics Committee, who advised that no ethical risks requiring submission to an HREC were identified in accordance with NSW Health Policy GL2007_020 (GWAHS 2024–092). The study was therefore conducted as a retrospective service evaluation within existing governance processes. All data were de‐identified prior to analysis.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgements
The authors thank Qingsheng Zhou and Tom Stent from the Health Intelligence Unit of Western NSW LHD for their assistance in obtaining data used in this study.
Appendix A.
FIGURE A1.
Ultra‐low acuity pathway eligibility criteria.
Data Availability Statement
Research data available on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Research data available on request.