Prevalence and Predictors of No-Show in Internal Medicine Outpatient Clinics: A Cross-Sectional Study in Saudi Arabia.

المقال يركز على انتشار وتنبؤات عدم حضور المرضى في عيادات الطب الباطني الخارجية في المملكة العربية السعودية. أظهرت دراسة مقطعية أجريت في المركز الطبي الجامعي بجامعة الإمام محمد بن سعود الإسلامية أن حوالي 51.7% من المرضى قد فاتتهم مواعيد في الشهرين الماضيين، ويرجع ذلك بشكل أساسي إلى النسيان (57.3%)، والالتزامات العملية أو المنزلية (42.7%)، ونقص وسائل النقل (9.5%). حددت الدراسة أن المرضى الذين لديهم مواعيد متابعة كانوا أكثر عرضة لعدم الحضور مقارنة بالزوار لأول مرة. وتؤكد على الحاجة إلى تدخلات مستهدفة، مثل تحسين أنظمة تذكير المواعيد وتوفير جداول مرنة، لتقليل معدلات عدم الحضور وتعزيز عمليات العيادة. [Extracted from the article]

This study aimed to examine the primary causes of patient no-shows in internal medicine (IM) clinics and identify their associated predictors. This cross-sectional observational study was conducted at IM outpatient clinics of the University Medical Center at Imam Mohammad Ibn Saud Islamic University in Riyadh, Saudi Arabia from January to March 2021. Predictors of no-shows were identified using binary logistic regression analysis. The data of 211 patients were identified during the study period. Around 51.7% (n = 109) of the patients had been no-shows in the past two months, with forgetfulness (57.3%), work or house commitments (42.7%), and lack of transportation (9.5%) being the main reasons. Patients who had follow-up visits were more likely to be no-shows than those who were coming to IM clinics for the first time (odds ratio: 1.86; 95% confidence interval: 1.03-3.37; p = 0.040). This study highlights the need for targeted interventions, including enhanced appointment reminder systems, flexible scheduling alternatives, and patient education, to decrease instances of no-shows and optimize clinic operations. [ABSTRACT FROM AUTHOR]

Copyright of Bahrain Medical Bulletin is the property of Bahrain Medical Bulletin and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)