Utility and Safety of Artificial Intelligence for Patient-Initiated Contact After Functional Rhinoplasty.

Publication/Presentation Date

1-1-2025

Abstract

OBJECTIVE: To understand the content of patient calls after functional rhinoplasty and to evaluate artificial intelligence chatbots' ability to provide accurate, intelligible, and safe responses.

STUDY DESIGN: Retrospective review.

SETTING: Tertiary-care institution.

METHODS: A single-institution, retrospective review was conducted for patients who underwent functional rhinoplasty between 2017 to 2023. Postoperative calls and messages prior to first follow-up were analyzed, and 48 representative prompts were generated, including 6 "Red Flag Questions" indicating potential complications. Prompts were input into 4 chatbots (ChatGPT, Claude, Perplexity, and Gemini). Two independent, blinded experts graded responses using a Likert-style Global Quality Scale (GQS) and a binary Expert Opinion Question (EOQ; "Would you feel comfortable if your patient received this response rather than speaking with your staff?"). Flesch-Kincaid (FK) Grade Levels measured readability. Chi-square and Mann-Whitney

RESULTS: Of 378 patients, 137 (36%) initiated contact, with 181 total calls. Common concerns included pain (19%) and medication questions (14%). Seventy-three percent (n = 132) received routine counseling, with no complications at first follow-up. ChatGPT produced "Good" or "Excellent" responses 98% of the time, significantly outperforming the next-best chatbot (Perplexity, 79%;

CONCLUSION: Patient calls are common after functional rhinoplasty. Most can be managed with reassurance. Chatbots, especially ChatGPT, provide reliable responses, which may improve satisfaction and reduce workload without compromising safety. Future development should focus on readability.

Volume

9

Issue

4

First Page

70170

Last Page

70170

ISSN

2473-974X

Disciplines

Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods

PubMedID

41111611

Department(s)

Administration and Leadership

Document Type

Article

Link to Full Text

Share

COinS