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Updated: Sep 17, 2025

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
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A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and

Kirstin Leitner1, Clare Cutri-French2, Abigail Mandel3

  • 1Department of Obstetrics and Gynecology, University of Pennsylvania, 3701 Market Street, 3rd Floor, Philadelphia, PA, 19104, United States, 1 651-492-3856.

JMIR AI
|July 3, 2025
PubMed
Summary
This summary is machine-generated.

A new postpartum conversational agent offers accessible support for new parents, addressing challenges in postpartum care with high patient engagement and satisfaction. This technology demonstrates potential for improving the postpartum experience for diverse patient populations.

Keywords:
breastfeedingconversational agentdevelopmentinfantinfant carenatural language processingnewbornsparentspatient engagementpatient satisfactionpatient supportphysical recoverypostpartumpostpartum carepregnancysupportsurveytext messaging

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Area of Science:

  • Digital Health
  • Maternal Health
  • Artificial Intelligence in Healthcare

Background:

  • The postpartum period, or
  • fourth trimester,
  • is a critical phase for parents and newborns, often complicated by individual needs and low appointment attendance.
  • Existing postpartum care models face challenges in meeting diverse patient needs and ensuring consistent engagement.
  • Technological solutions are needed for holistic and equitable postpartum care.

Purpose of the Study:

  • To describe the development and patient engagement of a novel postpartum conversational agent.
  • To evaluate the feasibility and acceptability of using natural language processing (NLP) for postpartum support.

Main Methods:

  • Development of a postpartum conversational agent using patient and provider input, and clinical algorithms.
  • The agent provided 2-way communication on recovery, lactation, infant care, and warning signs.
  • Iterative development and testing with a clinical team, expert panel, and postpartum patients (n=290).

Main Results:

  • High patient interaction: 98.6% used the platform, 93.4% completed surveys, 52% asked questions.
  • First-time parents and breastfeeding individuals showed higher engagement.
  • The conversational agent achieved 77% accuracy, with Black patients more likely to promote the program than White patients (P=.047).

Conclusions:

  • A comprehensive, automated postpartum conversational agent can be successfully developed.
  • This technology is acceptable and demonstrates very high patient engagement and satisfaction for postpartum support.
  • Conversational agents represent a promising tool for enhancing postpartum care accessibility and equity.