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  1. Home
  3. Decoding Mohs Micrographic Surgery Through An Artificial Intelligence-powered Search Analytics Approach.
  1. Home
  3. Decoding Mohs Micrographic Surgery Through An Artificial Intelligence-powered Search Analytics Approach.

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Decoding Mohs Micrographic Surgery Through an Artificial Intelligence-Powered Search Analytics Approach.

Landon E Ebbert1, Joshua L Ebbert2, Justin L Knapp1

  • 1Otolaryngology, Mayo Clinic Alix School of Medicine, Phoenix, USA.

Cureus
|September 15, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Patients researching Mohs micrographic surgery frequently ask about technical details and follow-up care. Machine learning analysis of online queries can identify key patient concerns for better healthcare communication.

Keywords:
artificial intelligencecutaneous skin cancerinternetmohs micrographic surgerysearch analytics

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Area of Science:

  • Dermatology
  • Medical Informatics
  • Health Communication

Background:

  • Patients increasingly seek online health information, particularly for procedures like Mohs micrographic surgery.
  • Google's "People Also Ask" feature offers insights into common patient queries.
  • Understanding these patient concerns is crucial for effective communication and treatment adherence.

Purpose of the Study:

  • To analyze patient questions about Mohs surgery using search analytics and machine learning.
  • To identify the primary concerns and information needs of patients undergoing Mohs surgery.
  • To evaluate the quality of online information available for Mohs surgery.

Main Methods:

  • Utilized Google's "People Also Ask" data for Mohs surgery queries.
  • Applied Rothwell's classification system and JAMA benchmark criteria for evaluation.
  • Employed machine learning for qualitative data analysis and classification performance assessment.

    • Main Results:

    • The most common patient questions concerned technical details (19.8%), follow-up care (12.5%), and indications (11.6%).
    • Government websites scored highest on JAMA benchmark criteria.
    • Machine learning algorithms achieved high agreement (kappa values up to 0.86) with human reviewers.

    Conclusions:

    • Mohs surgery patients prioritize understanding procedural specifics and post-operative care.
    • Addressing patient concerns regarding technical aspects can improve treatment compliance and reduce complications.
    • Large language models can effectively assist human reviewers in qualitative research for healthcare topics.