ArticleArchives of public health = Archives belges de sante publique2025
Reducing patient waiting times in humanitarian settings: a data-driven approach to improving healthcare access for vulnerable populations.
Article in Archives of public health = Archives belges de sante publique, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundProlonged 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.
methodsThis 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.
resultsA 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.
conclusionPatient 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.