Sociodemographic determinants of appointment no-show in primary care: a cross - sectional study in Renca Commune, Chile

Background: No show in Primary Healthcare (PHC) is a growing challenge for health system management, as they result in wasted resources, increased burden on healthcare teams, and disrupted continuity of care. In general, no show has been explored in the context of specialty care, there is limited evidence addressing this issue within PHC settings. This study aimed to examine the association between sociodemographic and administrative characteristics of appointments at primary health care centers in one big commune into Santiago, Chile. Methods: A cross-sectional observational study with multivariate analysis was conducted. Data were obtained from the IRIS platform, which consolidates clinical and administrative records from the Electronic Health Records. Appointments scheduled between 2022 and 2023 were extracted, with one appointment per person randomly selected. Appointments with a status of scheduled, canceled, pending, or in progress were excluded, as were overbookings, procedures, minor surgeries, and specialty consultations. The dependent variable was appointment status (completed or no-show). Multivariate logistic regression was performed using R, estimating odds ratios (OR), 95% confidence intervals (95% CI), and p-values. Results : A total of 95,288 appointments were analyzed, with a non-attendance rate of 21.8%. Variables significantly associated with higher non-attendance included young adults (18–64 years) (OR: 1.06; 95% CI: 1.02–1.11), some variables under cultural context like month September (OR: 1.12; 95% CI: 1.02–1.23), Saturdays (OR: 1.44; 95% CI: 1.30–1.59), appointments scheduled during the afternoon over 17:00 (OR: 1.19; 95% CI: 1.12–1.27), and type of healthcare provider appointments with nutritionists and psychologists had substantially higher odds of non-attendance (OR: 2.04; 95% CI: 1.80–2.31 and OR: 2.12; 95% CI: 1.87–2.41, respectively). Conclusions: No show is associated with various sociodemographic and administrative factors. Enhancing reminder systems and evaluating targeted strategies may help reduce missed appointments. Further research is needed in the context of primary health care. The findings of this study may serve as a foundation for developing predictive models aimed at improving appointment management and enhancing the efficiency of care delivery.