Anatomy of a Woebot® (WB001): agent guided CBT for women with postpartum depression

Postpartum Depression (PPD) is common, persistent, and stigmatized. There are insufficient trained professionals to deliver appropriate screening, diagnosis, and treatment. WB001 is a Software as a Medical Device (SaMD) based Agent-Guided Cognitive Behavioral Therapy (AGCBT) program for the treatmen...

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Bibliographic Details
Published inExpert review of medical devices Vol. 19; no. 4; pp. 287 - 301
Main Authors Darcy, Alison, Beaudette, Aaron, Chiauzzi, Emil, Daniels, Jade, Goodwin, Kim, Mariano, Timothy Y., Wicks, Paul, Robinson, Athena
Format Journal Article
LanguageEnglish
Published Taylor & Francis 03.04.2022
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Summary:Postpartum Depression (PPD) is common, persistent, and stigmatized. There are insufficient trained professionals to deliver appropriate screening, diagnosis, and treatment. WB001 is a Software as a Medical Device (SaMD) based Agent-Guided Cognitive Behavioral Therapy (AGCBT) program for the treatment of PPD, for which Breakthrough Device Designation was recently granted by the US Food and Drug Administration. WB001 combines therapeutic alliance, human-centered design, machine learning techniques, and established principles from CBT and interpersonal therapy (IPT). We introduce AGCBT as a new model of service delivery, whilst describing Woebot, the agent technology that enables guidance through the replication of some elements of human relationships. The profile describes the device's design principles, enabling technology, risk handling, and efficacy data in PPD. WB001 is a dynamic and personalized tool with which patients may establish a therapeutic bond. Woebot is designed to augment (rather than replace) human healthcare providers, unlocking the therapeutic potency associated with guidance, whilst retaining the scalability and agency that characterizes self-help approaches. WB001 has the potential to improve both the quality and the scalability of care through providing support to patients on waiting lists, in between clinical encounters, and enabling automation of measurement-based care.
ISSN:1743-4440
1745-2422
DOI:10.1080/17434440.2022.2075726