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

IF 2
JMIR AI Pub Date : 2025-04-22 DOI:10.2196/58454
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
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引用次数: 0

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 a holistic and equitable solution to meet care goals.

Objective: This paper describes the development of patient engagement data with a novel postpartum conversational agent that uses natural language processing to support patients post partum.

Methods: We report on the development of a postpartum conversational agent from concept to usable 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 2-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 on 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 6 months of use.

Results: A total of 290 patients used our conversational agent over the first 6 months, of which 112 (38.6%) were first time parents and 162 (56%) were Black. In total, 286 (98.6%) patients interacted with the platform at least once, 271 patients (93.4%) completed at least one survey, and 151 (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=.047). The overall accuracy of the conversational agent during the first 6 months was 77%.

Conclusions: It is possible to develop a comprehensive, automated postpartum conversational agent. The use of such a technology to support patients postdischarge appears to be acceptable with very high engagement and patient satisfaction.

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基于自然语言处理的新妈妈产后护理会话代理:发展与参与分析。
背景:“第四阶段”,或产后时期,仍然是怀孕的关键阶段,对父母和新生儿有重大影响。由于复杂的个人需求以及常规预约的低出勤率,护理带来了挑战。全面的技术解决方案可以提供全面和公平的解决方案,以实现护理目标。目的:本文描述了一种新型的产后会话代理的发展,该代理使用自然语言处理来支持产后患者的参与数据。方法:我们报告了产后对话代理的发展,从概念到可用的产品,以及患者参与这项技术。该计划的内容是使用基于患者和提供者的输入和临床算法开发的。我们的项目提供了与患者的双向沟通,并详细介绍了身体恢复、哺乳支持、婴儿护理和问题警告信号。在对患者进行测试之前,我们的核心临床团队和外部专家临床小组对此进行了反复测试。有资格在分娩后24小时左右出院的患者,阴道分娩了一个单胎足月婴儿,可以使用该计划。在使用的前6个月收集患者人口统计数据、准确性和患者参与度。结果:共有290名患者在前6个月内使用了我们的会话代理,其中112名(38.6%)为首次父母,162名(56%)为黑人。总共有286例(98.6%)患者至少与该平台互动一次,271例(93.4%)患者至少完成了一次调查,151例(52%)患者提出了一个问题。第一次父母和母乳喂养婴儿的人总体上参与的比例更高。黑人患者比白人患者更有可能推广该计划(P=.047)。会话代理在前6个月的总体准确率为77%。结论:开发一种全面、自动化的产后对话代理是可能的。使用这种技术来支持患者出院后似乎是可以接受的,具有很高的参与度和患者满意度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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