Factors Associated With Nonattendance at Scheduled Outpatient Appointments in a University General Hospital

Overview

Our objectives were to estimate the prevalence of nonattendance at outpatient offices, to identify the characteristics of appointments for which nonattendance was more likely to occur, and to generate a predictive model that could be applied to each appointment to estimate the probability of nonattendance.

Nonattendance at scheduled appointments at outpatient clinics is a common problem in general medical practice, representing a significant cost to the health care system, and resulting in disruption of daily work planning.Nonattendance at medical appointments has consequences not only for doctors (as it requires a greater use of resources and time), but also for patients, because there may be deterioration in the quality of care, and dissatisfaction associated with delays in obtaining a new appointment. Previous studies have reported that nonattendance at scheduled appointments is most frequently associated with those patients attending follow-up appointments, generally those assigned to another professional, those with appointments on Fridays, and those with appointments assigned 1 - 2 weeks in advance. Nonattendance was also associated with younger patient age, greater psychological problems, and lower socioeconomic status.Furthermore, in patients with chronic diseases, the nonattendance rate was also reported to be lower. Clinical patient characteristics can be important predictors of nonattendance, but they required high quality electronic health records to predict nonattendance accurately In the United States and Europe, the nonattendance rate is estimated to be between 6.5%-55%; there is little evidence regarding nonattendance in Latin America. Many studies have described the prevalence and impact of nonattendance at scheduled medical appointments, and possible strategies to decrease the nonattendance rate. However, few studies have utilized the characteristics associated with nonattendance for building predictive models, which might better identify those patients who may not attend a scheduled appointment

Conditions

Eligibility

Locations (1)

Outcomes