Development of a practical training method for a healthcare artificial intelligence (AI) chatbot

### Summary box #### What are the new findings? #### How might it impact on healthcare in the future? The rise of healthcare chatbots using artificial intelligence (AI) to understand unconstrained natural language input and reply with appropriate answers presents an emerging field of research, but few published studies on this topic include structured evaluation of efficacy or safety.1 In the past few months, healthcare has seen COVID-19 accelerate the adoption of digital health solutions to enable more timely care.2–4 Before AI chatbots can be deployed in healthcare applications, they need to be appropriately ‘trained’ on clinically relevant data.5 We will discuss the context that led to the development of a practical training method for a healthcare AI chatbot that efficiently improves chatbot accuracy and patient safety. The Healing at Home programme at the Hospital of the University of Pennsylvania (HUP) coordinates prioritised discharge and digital access to care for mothers and newborns.6 The American College of Obstetricians and Gynecologists recommends more immediate contact between obstetricians and patients to support postpartum care as an ongoing process, especially during the ‘fourth trimester’ after discharge.7–9 Literature shows many examples of texting interventions improving access to perinatal care.10–15 Healing at Home developed a postpartum support chatbot named ‘Penny’ in a partnership between a multidisciplinary clinical team from HUP, …