Economic Impact of a Deep Learning Algorithm for Automated Head and Neck Surgery Referral Triage.

OBJECTIVE: To quantify the comprehensive financial impact from implementing a deep learning algorithm for assistive patient triage within a high-volume Head and Neck Surgical Oncology center operating under a fee-for-service framework. METHODS: A prospective cost-benefit analysis compared a deep learning algorithm to standard manual triage on all incoming new patient referrals (n = 214) in one fiscal quarter (FY24 Q3). Financial impact was calculated using a model aggregating two economic components: (1) annualized labor displacement savings, and (2) revenue impact of reallocating verified non-indicated clinic slots to appropriate surgical candidates. RESULTS: Automated head and neck referral triage demonstrated a net annual operating gain of $535,989, yielding a 14.9× return on investment. Directly measured labor savings ($236,788.80; 5.6× ROI) represent the conservative estimate; capacity value is modeled, contingent on slot reallocation. The AI-assisted workflow displaces 1951.20 h of manual work (0.94 FTEs) annually per 6-provider division processing 30 referrals/week, and up to 351,936 h (169.20 FTEs) per hospital system. Furthermore, the algorithm's superior accuracy identified 80 verified recoverable clinic slots per year for the division; reallocating these to appropriate candidates generated an additional $6.2 million in downstream revenue. CONCLUSION: The integration of a deep learning algorithm for Head and Neck Surgical Oncology referral triage delivers substantial financial benefits through the dual mechanisms of direct reduction of administrative labor and the strategic optimization of clinical capacity. By identifying non-indicated referrals, the system allows for the reallocation of limited resources to patients requiring high-acuity surgical oncological care, offering a scalable model for financial sustainability. LEVEL OF EVIDENCE: NA.