No-Show Rates at a Plastic Surgery Clinic: Insights From Appalachian Healthcare Systems

Missed appointments, or "no-shows," occur when patients fail to notify the healthcare clinic of their inability to attend. No-show appointments place a significant burden on healthcare systems, costing clinics hundreds of thousands of dollars annually. Not only do patients miss out on receiving care, but prospective patients also face longer wait times due to appointment vacancies. This study aims to evaluate the factors associated with patient no-shows at a rural plastic surgery clinic.

Two-sample t-tests were used to compare age, BMI, weather temperature, and distance from the clinic between show and no-show groups. Chi-square tests of independence assessed the relationship between categorical variables- such as gender, clinic time (AM vs. PM), weather conditions, appointment type, insurance type, smoking status, and race- and appointment attendance. The Mann-Whitney U test analyzed the distribution of post-operative visit numbers between the show and no-show groups. To control for potential confounders, a multivariate logistic regression model was used to assess the independent effects of demographic, environmental, and procedural factors on no-show rates.

The mean age of patients in the no-show group was 40.73 years (SD = 14.08), compared to 42.85 years (SD = 12.48) for the show group. Higher no-show rates were significantly associated with male gender (p<0.01), impending weather (p<0.001), appointment type (new patient, pre-operative, post-operative) (p<0.001), and self-pay appointments (p<0.01). In addition, facial fracture follow-ups and facelift/blepharoplasty procedures showed significance (p<0.001). After adjusting for cofounders, male patients had significantly higher odds of missing their appointments compared to female patients (OR = 1.85, 95% CI: 1.12-3.06, p = 0.014). Post-operative patients were also more likely to miss appointments than new patients (OR = 1.92, 95% CI: 1.25-2.95, p = 0.003), while pre-operative patients showed an increased likelihood of no-show, though this did not reach statistical significance (OR = 1.45, p = 0.08). Weather conditions remained a significant factor after adjusting for other variables. Cloudy weather was associated with higher no-show rates compared to fair weather (OR = 1.67, 95% CI: 1.20-2.34, p = 0.002), whereas light rain did not significantly affect attendance (p = 0.38). Additionally, facial procedures were linked to significantly lower no-show rates compared to body contouring procedures (OR = 0.48, 95% CI: 0.29-0.79, p = 0.004).  Discussion and conclusion: No-show rates are influenced by demographic and environmental factors. Male gender, insurance type, and weather conditions were significantly associated with higher no-show rates, while age, BMI, and distance from the clinic had mixed or non-significant associations. Future studies should explore the role of telemedicine in improving patient attendance in plastic surgery clinics.