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

Area of Science:

• Dermatology
• Healthcare Management
• Health Services Research

Background:

• Long wait times for new patient dermatology visits (NPVs) delay diagnosis and treatment, impacting conditions like skin cancer.
• Extended lead times are linked to increased no-show rates and disproportionately affect underserved populations.
• Scalable, system-level interventions are needed to improve access to dermatologic care.

Purpose of the Study:

• To evaluate the impact of a decision tree-based, acuity-driven scheduling model on appointment lead times, attendance, and clinical outcomes in a large academic dermatology department.
• To assess the model's effectiveness in reducing wait times for new patient visits and improving overall clinic efficiency.

Main Methods:

• Implementation of a decision tree for structured triage based on patient concerns.
• Assignment of patients to acuity-based appointment blocks with defined target time frames.
• Redesign of scheduling blocks to preserve urgent capacity and optimize slot utilization without increasing resources.

Main Results:

• Median new patient visit lead time decreased by 76.8%.
• Lead times for biopsies and biopsy-proven malignancies reduced by 63.9% and 70.8%, respectively.
• No-show rates declined significantly from 18.6% to 8.1%.

Conclusions:

• A structured, acuity-based scheduling model can sustainably decrease dermatology appointment wait times.
• This approach improves patient attendance and maintains diagnostic yield.
• System-level scheduling interventions are effective in enhancing access to dermatologic care.