No-show rates to a sleep clinic: drivers and determinants.

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.

Examining body mass index in an urban core population: from health screening to physician visit.

UNLABELLED: BACKGROUND. Childhood obesity is commonly encountered in the primary care office and disproportionately affects those from low income or minority backgrounds. OBJECTIVE: To determine how accurately primary care clinicians in an urban setting identified patients with body mass indices (BMIs) at or above the 95th percentile for age and to determine which obesity treatment strategies are used. MATERIALS AND METHODS: The study population consisted of school-aged, inner-city children with a BMI at or above the 95th percentile for age whose charts were made available for data collection by retrospective chart review. RESULTS: A total of 158 patient medical charts were reviewed. Of these, 90 (57%) patients failed to be identified by the provider as having an elevated BMI. Obesity treatment was initiated in only 68 (43%) of these patients. CONCLUSIONS: Providers are not effectively recognizing childhood obesity and are not consistently implementing effective obesity treatment strategies.