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Related Experiment Video

Updated: Jun 30, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Disparities in engagement with an artificial intelligence-based chat intervention post-appendectomy.

Anna M Lin1, Brenna Rachwal1, Lindsey Asti1

  • 1Nemours Surgical Outcomes Center, Department of Surgery, Nemours Children's Health, Wilmington, DE, United States.

Journal of Pediatric Surgery
|June 11, 2026
PubMed
Summary

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This summary is machine-generated.

Engagement with AI chat for post-appendectomy care was lower in non-White patients and those with lower socioeconomic status. This highlights a need to improve digital health accessibility for vulnerable populations.

Area of Science:

  • Digital Health
  • Surgical Care
  • Health Equity

Background:

  • Artificial intelligence (AI)-based text messaging can reduce emergency department visits in post-appendectomy care.
  • Assessing the impact of digital health interventions across diverse patient populations is crucial.
  • Social determinants of health (SDoH) significantly influence patient outcomes and engagement with technology.

Purpose of the Study:

  • To evaluate engagement with an AI-based texting platform for post-appendectomy patients.
  • To identify patient demographics and SDoH-related risk factors associated with engagement with AI chat.
  • To understand how socioeconomic factors impact the use of digital health tools in surgical recovery.

Main Methods:

  • An AI chat tool was developed with input from pediatric surgeons and nurses for appendectomy recovery.
Keywords:
AppendicitisArtificial intelligenceChat botEngagementPostoperative care

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Last Updated: Jun 30, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

  • Data were collected from March 2022 to December 2023, with caregivers/patients responding to chats.
  • Statistical analyses, including Mann-Whitney U tests, Fisher's exact tests, and ordinal logistic regression, were used to assess engagement factors.

    • Main Results:

    • Of 1023 eligible patients, 497 did not engage, 229 engaged once, and 297 engaged multiple times.
    • Increasing patient age, higher socioeconomic vulnerability index (SVI) scores, and Medicaid insurance were associated with decreased chat engagement.
    • Higher SVI housing and transportation scores were linked to increased engagement with the AI chat platform.

    Conclusions:

    • AI chat engagement was lower in non-White patients and those with lower socioeconomic status.
    • These findings suggest potential disparities in digital health tool accessibility for at-risk populations.
    • Future research should prioritize optimizing the feasibility and accessibility of technological interventions for patients facing SDoH-related challenges.