Active patient queue dynamics in a district hospital emergency centre: a cross-sectional analysis
- 1Corresponding author at: 7 Burn Street Waverley, Johannesburg, Gauteng, 2090, South Africa.
- 2Division of Emergency Medicine, University of Cape Town, Cape Town, South Africa
Abstract
Introduction: Emergency Centre crowding is a persistent challenge, particularly in low- and middle-income countries, where resource constraints exacerbate mismatches between demand and capacity. Clinician productivity is central to EC flow and is typically measured using patients per hour (PPH), but this does not capture concurrent workload. The active queue (AQ), operationally defined as unresolved clinician-attributed patient episodes at any point during a shift, may complement PPH by capturing the concurrent nature of EC clinical work. This study aimed to describe AQ dynamics at a district-level EC and identify the clinician, shift, and flow factors associated with variation in queue size. Methods: A retrospective cross-sectional secondary analysis was conducted using data from 1 289 clinician shifts across three four-week periods in 2019 at a South African district-level EC. AQ was derived from timestamped patient flow data. Associations with clinician, shift, and system-level factors were assessed using one-way ANOVA and Spearman's rank correlation. Results: Median AQ was 1.50 (IQR 1.00–2.25), with a median peak AQ of 3.00 (IQR 2.00–4.00). AQ varied significantly by clinician category, employment type, and shift type (all p < 0.001), with higher values among senior clinicians and during night shifts. AQ remained stable at a median of 2.00 during the active working period before declining in the final shift quarter, while PPH declined progressively throughout. AQ and PPH were moderately correlated (rho = 0.43, p < 0.001). AQ was positively associated with patients waiting at shift start (rho = 0.19, p < 0.001) but not with patient acuity, despite acuity strongly suppressing PPH (rho = −0.44, p < 0.001). Conclusions: Emergency clinicians routinely manage concurrent patient loads, with AQ demonstrating a consistent mid-shift plateau. AQ complements PPH by capturing the concurrent dimension of clinician workload that throughput measures alone cannot reflect, with relevance to staffing and operational planning in district-level EC settings.
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BibTeX
@article{hoffe2026active,
title = {{Active patient queue dynamics in a district hospital emergency centre: a cross-sectional analysis}},
author = {Mary Elizabeth Hoffe and Clint Hendrikse},
journal = {African Journal of Emergency Medicine},
year = {2026},
month = oct,
doi = {10.1016/j.afjem.2026.101021},
url = {https://doi.org/10.1016/j.afjem.2026.101021},
note = {CC BY},
}
RIS
TY - JOUR
TI - Active patient queue dynamics in a district hospital emergency centre: a cross-sectional analysis
AU - Mary Elizabeth Hoffe
AU - Clint Hendrikse
PY - 2026
DA - 2026/10/24
JO - African Journal of Emergency Medicine
DO - 10.1016/j.afjem.2026.101021
UR - https://doi.org/10.1016/j.afjem.2026.101021
AB - Introduction: Emergency Centre crowding is a persistent challenge, particularly in low- and middle-income countries, where resource constraints exacerbate mismatches between demand and capacity. Clinician productivity is central to EC flow and is typically measured using patients per hour (PPH), but this does not capture concurrent workload. The active queue (AQ), operationally defined as unresolved clinician-attributed patient episodes at any point during a shift, may complement PPH by capturing the concurrent nature of EC clinical work. This study aimed to describe AQ dynamics at a district-level EC and identify the clinician, shift, and flow factors associated with variation in queue size. Methods: A retrospective cross-sectional secondary analysis was conducted using data from 1 289 clinician shifts across three four-week periods in 2019 at a South African district-level EC. AQ was derived from timestamped patient flow data. Associations with clinician, shift, and system-level factors were assessed using one-way ANOVA and Spearman's rank correlation. Results: Median AQ was 1.50 (IQR 1.00–2.25), with a median peak AQ of 3.00 (IQR 2.00–4.00). AQ varied significantly by clinician category, employment type, and shift type (all p < 0.001), with higher values among senior clinicians and during night shifts. AQ remained stable at a median of 2.00 during the active working period before declining in the final shift quarter, while PPH declined progressively throughout. AQ and PPH were moderately correlated (rho = 0.43, p < 0.001). AQ was positively associated with patients waiting at shift start (rho = 0.19, p < 0.001) but not with patient acuity, despite acuity strongly suppressing PPH (rho = −0.44, p < 0.001). Conclusions: Emergency clinicians routinely manage concurrent patient loads, with AQ demonstrating a consistent mid-shift plateau. AQ complements PPH by capturing the concurrent dimension of clinician workload that throughput measures alone cannot reflect, with relevance to staffing and operational planning in district-level EC settings.
SN - 2211-419X
SN - 2211-4203
ER -