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Background <p>Prolonged patient waiting times in healthcare settings, particularly within humanitarian contexts, pose significant challenges to the provision and quality of care received. This study examines the persistent issue of extended waiting periods among patients, including Syrian and Palestinian refugees and Lebanese nationals seeking care at humanitarian clinics in Lebanon. The study aims to highlight the challenges faced by these populations and their impact on care and health outcomes.</p> Methods <p>This study analyzed secondary data for 111,998 patient records receiving care at Multi Aid Programs clinic between December 2018 and June 2023. Descriptive statistics were performed on the compiled data. Additionally, an Auto-Regressive Integrated Moving Average (ARIMA) model was developed to forecast patient waiting times over the next 14 days, providing a data-driven approach to understanding and optimizing healthcare service delivery.</p> Results <p>A total of 442,332 patient visits were analyzed. The vast majority of patients treated at the clinic were Syrian refugees (84.6%), Lebanese patients (15.3%), and Palestinian refugees(0.1%). Females accounted for 55% of visits, slightly outnumbering males (45%). Approximately 21.4% of patients left the clinic without receiving medical care, likely due to extended waiting times. The average waiting time was 1.4&#xa0;h with an average of a 1-hour wait for emergencies and a 2-hour wait for orthopedic appointments. The study team, along with clinic staff and administration, observed patients waiting in overcrowded rooms. Little improvement has been reported in waiting times since 2019. The predictive ARIMA model for patient waiting time forecasting yielded a notably low RMSE of 0.18, indicating excellent fidelity between the modeled and true wait times.</p> Conclusion <p>Patient waiting time represents a significant issue in the humanitarian setting, jeopardizing patient access to medical care and often resulting in patients leaving clinics before being seen by a physician. Future research should explore the underlying causes and broader consequences of prolonged wait times, including their impact on patient satisfaction, healthcare provider burnout, and overall system efficiency.</p>

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Reducing patient waiting times in humanitarian settings: a data-driven approach to improving healthcare access for vulnerable populations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月11日

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  • Eslam Abo Alhawa,
  • Elise Presser,
  • Amin Zahwe,
  • Sary Faraj,
  • Samar Al-Hajj,
  • Marwa Fares,
  • Mohammad Fadi AlHalbi

摘要

Background

Prolonged patient waiting times in healthcare settings, particularly within humanitarian contexts, pose significant challenges to the provision and quality of care received. This study examines the persistent issue of extended waiting periods among patients, including Syrian and Palestinian refugees and Lebanese nationals seeking care at humanitarian clinics in Lebanon. The study aims to highlight the challenges faced by these populations and their impact on care and health outcomes.

Methods

This study analyzed secondary data for 111,998 patient records receiving care at Multi Aid Programs clinic between December 2018 and June 2023. Descriptive statistics were performed on the compiled data. Additionally, an Auto-Regressive Integrated Moving Average (ARIMA) model was developed to forecast patient waiting times over the next 14 days, providing a data-driven approach to understanding and optimizing healthcare service delivery.

Results

A total of 442,332 patient visits were analyzed. The vast majority of patients treated at the clinic were Syrian refugees (84.6%), Lebanese patients (15.3%), and Palestinian refugees(0.1%). Females accounted for 55% of visits, slightly outnumbering males (45%). Approximately 21.4% of patients left the clinic without receiving medical care, likely due to extended waiting times. The average waiting time was 1.4 h with an average of a 1-hour wait for emergencies and a 2-hour wait for orthopedic appointments. The study team, along with clinic staff and administration, observed patients waiting in overcrowded rooms. Little improvement has been reported in waiting times since 2019. The predictive ARIMA model for patient waiting time forecasting yielded a notably low RMSE of 0.18, indicating excellent fidelity between the modeled and true wait times.

Conclusion

Patient waiting time represents a significant issue in the humanitarian setting, jeopardizing patient access to medical care and often resulting in patients leaving clinics before being seen by a physician. Future research should explore the underlying causes and broader consequences of prolonged wait times, including their impact on patient satisfaction, healthcare provider burnout, and overall system efficiency.