Reducing New Patient Wait Times in Dermatology Using an Algorithmic Scheduling Strategy

Timely access to dermatologic care remains a persistent challenge, particularly for new patient visits (NPVs), for which long lead times can delay diagnosis and treatment of serious conditions such as skin cancer. Long wait times have also been associated with higher no-show rates and disproportionately affect historically underserved populations, underscoring the need for scalable, system-level interventions to improve access. This study evaluates the impact of implementing a decision tree-based, acuity-driven scheduling model across a large academic dermatology department on appointment lead times, attendance, and downstream clinical outcomes. The decision tree guided schedulers through structured triage based on presenting concerns and assigned patients to acuity-based appointment blocks with defined target time frames. Redesigned scheduling blocks preserved urgent capacity, restricted routine bookings far in advance, and improved overall slot utilization without increasing staffing or clinical capacity. Following implementation, the median NPV lead time decreased by 76.8%. Among NPVs resulting in a biopsy, the median lead time decreased by 63.9%, and among biopsy-proven malignancies, the lead time decreased by 70.8%. No-show rates declined from 18.6% to 8.1%. These findings demonstrate that structured, acuity-based scheduling can sustainably reduce wait times, improve attendance, and preserve diagnostic yield in dermatology.