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Related Experiment Video

Updated: Jun 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

Stephanie Younan1, Pearl Doan1, Kevin Xin2,3

  • 1Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA.

The Laryngoscope
|June 12, 2026
PubMed
Summary

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Implementing deep learning for patient triage in Head and Neck Surgical Oncology significantly boosts financial performance. This AI tool reduces administrative workload and optimizes clinic capacity, generating substantial operating gains and return on investment.

Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Surgical Oncology Workflow Optimization

Background:

  • High-volume Head and Neck Surgical Oncology centers face administrative burdens in patient triage.
  • Fee-for-service models necessitate efficient resource allocation and revenue generation.
  • Current manual triage processes can be labor-intensive and may miss opportunities for capacity optimization.

Purpose of the Study:

  • To quantify the financial impact of integrating a deep learning algorithm for patient triage.
  • To assess the algorithm's effect on labor displacement and clinic slot reallocation.
  • To evaluate the return on investment (ROI) for AI-assisted triage in surgical oncology.

Main Methods:

  • Prospective cost-benefit analysis comparing deep learning triage to standard manual triage.
Keywords:
artificial intelligencecost–benefit analysisdeep learninghead and neck surgeryotolaryngologyreferral triagereturn on investmentworkflow optimization

Related Experiment Videos

Last Updated: Jun 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

  • Inclusion of all new patient referrals (n=214) over one fiscal quarter (FY24 Q3).
  • Financial impact calculation based on annualized labor savings and revenue from reallocated clinic slots.

    • Main Results:

    • AI-assisted triage yielded a net annual operating gain of $535,989, a 14.9× ROI.
    • Direct labor savings amounted to $236,788.80 (5.6× ROI), displacing 1951.20 hours of manual work annually per division.
    • Identification of 80 recoverable clinic slots per year, generating an estimated $6.2 million in downstream revenue.

    Conclusions:

    • Deep learning algorithm integration offers substantial financial benefits via labor reduction and capacity optimization.
    • The system effectively identifies non-indicated referrals, enabling resource reallocation to high-acuity surgical oncology patients.
    • This AI-driven approach presents a scalable model for enhancing financial sustainability in surgical oncology centers.