
ai.jmir.org/2025/1/e58454
Preview meta tags from the ai.jmir.org website.
Linked Hostnames
56- 82 links toai.jmir.org
- 18 links todx.doi.org
- 17 links towww.ncbi.nlm.nih.gov
- 12 links toorcid.org
- 6 links tocareers.jmir.org
- 3 links toasset.jmir.pub
- 2 links toblog.jmir.org
- 2 links tobsky.app
Thumbnail

Search Engine Appearance
A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis
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.
Bing
A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis
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.
DuckDuckGo

A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis
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.
General Meta Tags
55- titleJMIR AI - A Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis
- charsetutf-8
- viewportwidth=device-width, initial-scale=1
- msapplication-TileColor#247CB3
- msapplication-TileImagehttps://asset.jmir.pub/assets/static/images/mstile-144x144.png
Open Graph Meta Tags
5- og:titleA Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis
- og:typearticle
- og:urlhttps://ai.jmir.org/2025/1/e58454
- og:imagehttps://asset.jmir.pub/assets/c4f1b3bb2f0e5f85a1fa4179debe8e62.png
- og:site_nameJMIR AI
Twitter Meta Tags
5- twitter:cardsummary_large_image
- twitter:site@jmirpub
- twitter:titleA Conversational Agent Using Natural Language Processing for Postpartum Care for New Mothers: Development and Engagement Analysis
- twitter:descriptionBackground 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.
- twitter:imagehttps://asset.jmir.pub/assets/c4f1b3bb2f0e5f85a1fa4179debe8e62.png
Link Tags
67- apple-touch-iconhttps://asset.jmir.pub/assets/static/images/apple-touch-icon-57x57.png
- apple-touch-iconhttps://asset.jmir.pub/assets/static/images/apple-touch-icon-114x114.png
- apple-touch-iconhttps://asset.jmir.pub/assets/static/images/apple-touch-icon-72x72.png
- apple-touch-iconhttps://asset.jmir.pub/assets/static/images/apple-touch-icon-144x144.png
- apple-touch-iconhttps://asset.jmir.pub/assets/static/images/apple-touch-icon-60x60.png
Links
194- http://www.mendeley.com/import/?doi=10.2196/58454
- https://aging.jmir.org
- https://ai.jmir.org
- https://ai.jmir.org/2025/1
- https://ai.jmir.org/2025/1/e58454