Predictors and Effectiveness of Reminder-Based Interventions for Reducing Patient No-Shows in Primary Care: A Systematic Review and Meta-Analysis Proposal with Implications for Saudi Family Medicine Practice

Authors

  • Tariq Dhaher Alanazi Al-Kharj Armed Forces Medical Services Institute for Community Health, Al-Kharj, Saudi Arabia. , Saudi Arabia Author
  • Tariq Fahad Bindaaj Al-Kharj Armed Forces Medical Services Institute for Community Health, Al-Kharj, Saudi Arabia. , Saudi Arabia Author
  • Ibrahim Ghoneim Al-Kharj Armed Forces Medical Services Institute for Community Health, Al-Kharj, Saudi Arabia. , Saudi Arabia Author
  • Abdelaziz Ahmed Elmalky Research assistant and volunteer, Riyadh, Saudi Arabia , Saudi Arabia Author
  • Miya Yustianingsih Universitas Qamarul Huda Badaruddin, Bagu, Lombok, Indonesia , Indonesia Author
  • Ahmed Elmalky King Saud University Medical City, Riyadh, Saudi Arabia , Saudi Arabia Author

DOI:

https://doi.org/10.64021/

Keywords:

Predictors and Effectiveness , Reminder-Based Interventions , Family Medicine

Abstract

Patient non-attendance remains a persistent challenge in primary care, causing inefficient use of clinical resources, longer waiting times, interrupted continuity of care, and delayed chronic disease management. Although multiple patient-, appointment-, and health-system-related predictors have been reported, evidence regarding the effectiveness of reminder-based interventions remains heterogeneous and has limited direct applicability to Saudi family medicine. To identify the most consistent predictors of patient no-shows in primary care and evaluate the effectiveness of reminder-based and access-enhancing interventions, including SMS reminders, telephone outreach, and telehealth. A systematic review was conducted in accordance with PRISMA guidance and the registered PROSPERO protocol (CRD420261425581). Primary studies involving patients attending primary care, family medicine, general practice, or community outpatient services were eligible. Studies examining demographic, socioeconomic, behavioral, clinical, appointment-related, or healthcare-system predictors and those evaluating reminder or communication interventions were included. Two reviewers independently performed study selection, data extraction, and quality assessment. Owing to substantial heterogeneity in study populations, outcome definitions, exposures, and reported effect measures, the findings were synthesized narratively. Fifteen observational studies were included. Previous non-attendance was the most consistent predictor of future no-shows. Other frequently reported predictors included longer appointment lead time, younger age, socioeconomic disadvantage, lack of insurance or public insurance, unmet social needs, transportation difficulty, competing employment or family responsibilities, and unsuccessful appointment confirmation. Missed appointments were also associated with increased healthcare utilization, hospitalization, and mortality, although these relationships could not establish causality. Reminder effectiveness varied according to timing, frequency, delivery channel, and patient risk. Timely and actionable SMS reminders and targeted live telephone outreach appeared more effective than uniform, non-interactive reminders. Evidence was insufficient to establish the overall superiority of SMS over telephone reminders. Telehealth generally improved appointment completion by removing transportation and time barriers, although video consultations and portal-dependent systems introduced digital-access inequalities. Saudi studies reported substantial non-attendance and identified forgetfulness, follow-up status, regional variation, appointment characteristics, and environmental factors as locally relevant predictors. Primary care no-shows are driven by interacting behavioral, socioeconomic, logistical, and organizational factors. The evidence supports a tiered strategy combining universal actionable reminders, risk-based telephone outreach, flexible rescheduling, and support for transportation and digital barriers. Locally validated prospective studies are required before widespread implementation in Saudi family medicine.

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Design: prospective cohort study of 46,710 primary-care appointments involving adults with diabetes.

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37. Shour AR, Jones GL, Anguzu R, Doi SA, Onitilo AA. Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system. BMC Health Serv Res. 2023;23:989. doi:10.1186/s12913-023-09969-5.

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49. Sumarsono A, Case M, Kassa S, Moran B. Telehealth as a tool to improve access and reduce no-show rates in a large safety-net population in the USA. J Urban Health. 2023;100(2):398-407. doi:10.1007/s11524-023-00721-2.

50. Chen K, Zhang C, Gurley A, Akkem S, Jackson H. Appointment non-attendance for telehealth versus in-person primary care visits at a large public healthcare system. J Gen Intern Med. 2023;38(4):922-928. doi:10.1007/s11606-022-07814-9.

51. Chen K, Zhang C, Gurley A, Akkem S, Jackson H. Patient characteristics associated with telehealth scheduling and completion in primary care at a large, urban public healthcare system. J Urban Health. 2023;100(3):468-477. doi:10.1007/s11524-023-00744-9.

52. Cummins MR, Tsalatsanis A, Chaphalkar C, Ivanova J, Ong T, Soni H, et al. Telemedicine appointments are more likely to be completed than in-person healthcare appointments: a retrospective cohort study. JAMIA Open. 2024;7(3):ooae059. doi:10.1093/jamiaopen/ooae059.

53. AlOmar RS, AlHarbi M, Alotaibi NS, AlShamlan NA, Al-Shammari MA, AlThumairi AA, et al. Pattern of virtual consultations in the Kingdom of Saudi Arabia: an epidemiological nationwide study. J Epidemiol Glob Health. 2024;14(3):817-826. doi:10.1007/s44197-024-00219-3.

54. Alammari YM. Prevalence and predictors of no-show in internal medicine outpatient clinics: a cross-sectional study in Saudi Arabia. Bahrain Med Bull. 2024;46(4):2377-2381.

55. Alshehri A, Saeed A, AlShafea A, Althubiany S, Alshehri M, Alzahrani A, et al. Machine learning approaches to predict no-shows in Saudi Arabian primary and general healthcare settings. Saudi J Health Syst Res. 2025;5(1):24-40. doi:10.1159/000542701.

56. AlSerkal YM, Ibrahim NM, Alsereidi AS, Ibrahim M, Kurakula S, Naqvi SA, et al. Real-time analytics and AI for managing no-show appointments in primary health care in the United Arab Emirates: before-and-after study. JMIR Form Res. 2025;9:e64936. doi:10.2196/64936.

57. Williamson AE, McQueenie R, Ellis DA, McConnachie A, Wilson P. 'Missingness' in health care: associations between hospital utilization and missed appointments in general practice. A retrospective cohort study. PLoS One. 2021;16(6):e0253163. doi:10.1371/journal.pone.0253163.

58. McQueenie R, Ellis DA, McConnachie A, Wilson P, Williamson AE. Morbidity, mortality and missed appointments in healthcare: a national retrospective data linkage study. BMC Med. 2019;17:2. doi:10.1186/s12916-018-1234-0.

59. McComb S, Tian Z, Sands L, Turkcan A, Zhang L, Frazier S, et al. Cancelled primary care appointments: a prospective cohort study of diabetic patients. J Med Syst. 2017;41(4):53. doi:10.1007/s10916-017-0700-0.

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2026-08-02

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