Factors associated with nonattendance at clinical medicine scheduled outpatient appointments in a university general hospital

Introduction: Nonattendance at scheduled outpatient appointments for primary care is a major health care problem worldwide. Our aim was to estimate the prevalence of nonattendance at scheduled appointments for outpatients seeking primary care, to identify associated factors and build a model that predicts nonattendance at scheduled appointments.

Methods: A cohort study of adult patients, who had a scheduled outpatient appointment for primary care, was conducted between January 2010 and July 2011, at the Italian Hospital of Buenos Aires. We evaluated the history and characteristics of these patients, and their scheduling and attendance at appointments. Patients were divided into two groups: those who attended their scheduled appointments, and those who did not. We estimated the odds ratios (OR) and corresponding 95% confidence intervals (95% CI), and generated a predictive model for nonattendance, with logistic regression, using factors associated with lack of attendance, and those considered clinically relevant. Alternative models were compared using Akaike's Information Criterion. A generation cohort and a validation cohort were assigned randomly.

Results: Of 113,716 appointments included in the study, 25,687 were missed (22.7%; 95% CI: 22.34%-22.83%). We found a statistically significant association between nonattendance and age (OR: 0.99; 95% CI: 0.99-0.99), number of issues in the personal health record (OR: 0.98; 95% CI: 0.98-0.99), time between the request for and date of appointment (OR: 1; 95% CI: 1-1), history of nonattendance (OR: 1.07; 95% CI: 1.07-1.07), appointment scheduled later than 4 pm (OR: 1.30; 95% CI: 1.24-1.35), and specific days of the week (OR: 1.00; 95% CI: 1.06-1.1). The predictive model for nonattendance included characteristics of the patient requesting the appointment, the appointment request, and the actual appointment date. The area under the receiver operating characteristic curve of the predictive model in the generation cohort was 0.892 (95% CI: 0.890-0.894).

Conclusion: Evidence related to patient characteristics, and the identification of appointments with a higher likelihood of nonattendance, should promote guided strategies to reduce the rate of nonattendance, as well as to future research on this topic. The use of predictive models could further guide management strategies to reduce the rate of nonattendance.