Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: JMIR AI

Date Submitted: Apr 1, 2024
Date Accepted: Oct 24, 2024

The final, peer-reviewed published version of this preprint can be found here:

A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis

Leitner K, Cutri-French C, Mandel A, Christ L, Koelper N, McCabe M, Seltzer E, Scalise L, Colbert JA, Dokras A, Rosin R, Levine L

A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis

JMIR AI 2025;4:e58454

DOI: 10.2196/58454

PMID: 40605798

PMCID: 12223682

Healing at Home: Developing a Conversational Agent That Leverages Natural Language Processing to Engage Postpartum Patients

  • Kirstin Leitner; 
  • Clare Cutri-French; 
  • Abigail Mandel; 
  • Lori Christ; 
  • Nathaneal Koelper; 
  • Meaghan McCabe; 
  • Emily Seltzer; 
  • Laura Scalise; 
  • James A Colbert; 
  • Anuja Dokras; 
  • Roy Rosin; 
  • Lisa Levine

ABSTRACT

Background The “fourth trimester”, or postpartum time period, remains a critical phase of pregnancy that significantly impacts parents and newborns. Care poses challenges due to complex individual needs as well as low attendance rates at routine appointments. A comprehensive technological solution could provide holistic and equitable solution to meet care goals. Methods We report on development of a postpartum conversational agent from concept to useable product as well as the patient engagement with this technology. Content for the program was developed using patient and provider based input and clinical algorithms. Our program offered two-way communication to patients and details on physical recovery, lactation support, infant care and warning signs for problems. This was iterated upon by our core clinical team and an external expert clinical panel before being tested in patients. Patients eligible for discharge around 24 hours after delivery who had delivered a singleton full term infant vaginally were offered use of the program. Patient demographics, accuracy and patient engagement were collected over the first six months of use. Results 290 patients used our conversational agent over the first six months, of which 38.6% were first time parents and 56% were Black. 98.6% of patients interacted with the platform at least once, 93.4% completed at least one survey and 52% patients asked a question. First time parents and those breastfeeding their infants had higher rates of engagement overall. Black patients were more likely to promote the program than White patients (p=0.047). The overall accuracy of the conversational agent during the first six months was 77%. Conclusions It is possible to develop a comprehensive, automated postpartum conversational agent. The use of such a technology to support patients post discharge appears to be acceptable with very high engagement and patient satisfaction.


 Citation

Please cite as:

Leitner K, Cutri-French C, Mandel A, Christ L, Koelper N, McCabe M, Seltzer E, Scalise L, Colbert JA, Dokras A, Rosin R, Levine L

A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis

JMIR AI 2025;4:e58454

DOI: 10.2196/58454

PMID: 40605798

PMCID: 12223682

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.