STUDY OBJECTIVES: Attendance to sleep clinic appointments is imperative to diagnose sleep-related disorders and to offer appropriate treatment. As part of our quality assurance program, we assessed predictors of no-show rates at our sleep clinic. We hypothesize that no-show rates can be predicted by demographics, appointment type (new vs established) and timing, and insurance status. METHODS: We performed a 10-month, retrospective chart review of patients scheduled at Saint Louis University's SLUCare Sleep Disorders Center. Multivariable logistic regression was used to determine which factors were independently associated with no-show. RESULTS: A total of 2,532 clinical visits were reviewed, and the overall no-show rate was 21.2%. Factors associated with a higher incidence of no-show rates included younger age (17-40 years: 21.5%; 41-64 years: 23.5%; ≥65 years: 14.0%; P < .0001), appointment type (new: 30.5% vs established: 18.3%; P < .0001), and insurance status (no insurance: 24.6% vs public: 22.6% vs private: 15.9%; P < .0001). Multivariable logistic regression confirmed the independent association between no-show and age ≤ 40 years (adjusted odds ratio = 1.72; 95% confidence interval: 1.44, 2.20), new patient status (adjusted odds ratio = 1.78; 95% confidence interval: 1.44, 2.20), and absence of health insurance (adjusted odds ratio = 1.62; 95% confidence interval: 1.24, 2.11). Sex, appointment time, day of the week, and season did not significantly influence no-show rates. CONCLUSIONS: Independent predictors of no-show appointments included younger age, new patient status, and lack of health insurance. Our findings will aid future efforts to identify patients with high predictors of nonadherence. Further studies are needed to develop methods to decrease no-show rates once high-risk appointments have been identified.
STUDY OBJECTIVES: Attendance to sleep clinic appointments is imperative to diagnose sleep-related disorders and to offer appropriate treatment. As part of our quality assurance program, we assessed predictors of no-show rates at our sleep clinic. We hypothesize that no-show rates can be predicted by demographics, appointment type (new vs established) and timing, and insurance status. METHODS: We performed a 10-month, retrospective chart review of patients scheduled at Saint Louis University's SLUCare Sleep Disorders Center. Multivariable logistic regression was used to determine which factors were independently associated with no-show. RESULTS: A total of 2,532 clinical visits were reviewed, and the overall no-show rate was 21.2%. Factors associated with a higher incidence of no-show rates included younger age (17-40 years: 21.5%; 41-64 years: 23.5%; ≥65 years: 14.0%; P < .0001), appointment type (new: 30.5% vs established: 18.3%; P < .0001), and insurance status (no insurance: 24.6% vs public: 22.6% vs private: 15.9%; P < .0001). Multivariable logistic regression confirmed the independent association between no-show and age ≤ 40 years (adjusted odds ratio = 1.72; 95% confidence interval: 1.44, 2.20), new patient status (adjusted odds ratio = 1.78; 95% confidence interval: 1.44, 2.20), and absence of health insurance (adjusted odds ratio = 1.62; 95% confidence interval: 1.24, 2.11). Sex, appointment time, day of the week, and season did not significantly influence no-show rates. CONCLUSIONS: Independent predictors of no-show appointments included younger age, new patient status, and lack of health insurance. Our findings will aid future efforts to identify patients with high predictors of nonadherence. Further studies are needed to develop methods to decrease no-show rates once high-risk appointments have been identified.