Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
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Volume 6 Number 1 2025
RESEARCH ARTICLE (Open Access)
Bridging Queueing Theory and Data-Driven Analytics to Reduce Patient Wait Times: A Cross-Sectional Survey of Healthcare Users in the United States
Md Samiul Alam Mazumder1*, Mithra Rani Hur2
Journal of Primeasia 6 (1) 1-11 https://doi.org/10.25163/primeasia.6110837
Submitted: 27 January 2025 Revised: 08 April 2025 Accepted: 11 April 2025 Published: 14 April 2025
Abstract
Background: Long waits, registration bottlenecks, and diagnostic delays remain stubborn features of everyday hospital life, and they wear on patients and staff alike. Whether queueing theory and data-driven analytics are actually understood, together, as a workable remedy by the people who use these systems is less well documented.
Methods: We carried out a cross-sectional survey of 185 healthcare service users recruited through purposive and convenience sampling across outpatient and diagnostic settings in the United States. A structured, five-point Likert questionnaire captured perceived causes of flow inefficiency, waiting-time experience, and attitudes toward queueing-based and analytics-based interventions; descriptive statistics and Pearson correlation analysis were used to examine relationships among flow delay, waiting time, and adoption of these two approaches.
Results: Registration delay (76.2%) and diagnostic delay (75.2%) emerged as the most frequently cited drivers of inefficiency, followed closely by staff shortages (74.6%) and scheduling weaknesses (74.0%). Flow delay correlated positively with waiting time (r = 0.68), while both queueing theory and analytics adoption correlated negatively with delay and waiting time (r = -0.61 to -0.64, and r = -0.57 to -0.59, respectively); the two approaches were themselves strongly associated (r = 0.72).
Conclusion: Respondents' experience suggests that queueing theory and data-driven analytics are not competing tools but complementary ones, and that hospitals stand to gain more from combining them than from pursuing either in isolation.
Keywords: Patient Flow Optimization; Queueing Theory; Data-Driven Analytics; Healthcare Efficiency; Cross-Sectional Survey
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