Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Abstract

Background: Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increas¿ ing waiting lists. We developed a model for predicting non-attendance and assessed the efectiveness of an interven¿ tion for reducing non-attendance based on the model. Methods: The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratifcation tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospec¿ tively with appointments scheduled between January 7 and February 8, 2019. The efectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We fnally conducted a pilot study in which all patients identifed by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results: Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specifcity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specifcity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respec¿ tively. The efectiveness of the intervention was assessed on 1,311 individuals identifed as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a signifcant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. Conclusions: The risk of non-attendance can be adequately estimated using patient information stored in medi¿ cal records. The patient stratifcation according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to efectively reduce non-attendance rates. Keywords: Non-attendance, No show, Clinical decision making, Delivery of healthcare, Access to healthcare, Decision trees