Genetic TestingEngaging Patients in Prenatal Genetic Testing Decisions as a Pathway to Improve Obstetric Outcomes

The goal of this study is to ensure that pregnant patients have the resources and support needed to access Prenatal Screening \& Diagnostic Testing (PS\&D) in an informed and evidence-based fashion by developing an innovative digital tool to support patients' decision-making and contributing fundamental knowledge to advance science in a way that promotes patients' access to new prenatal applications of genomic science and technology. Our central hypothesis is that, by focusing on patient engagement as a key driver to improve patient outcomes, the use of an evidence-based artificial-intelligence (AI) powered patient engagement tool will increase patients' ability to seek information and structure a decision-making process that, in turn, increases informed decisions about PS\&D and decreases decisional conflict associated with those decisions.

Using data from NEST (Ensuring Patients Informed Access to NIPT \[non-invasive prenatal testing\]), the investigators designed the next iteration of NEST, a point-of care shared decision-making tool powered by artificial intelligence (AI) to provide a personalized and dynamic decision support tool: Obstetric Prenatal Genetic Testing Engagement Solution (OPUS). OPUS is an AI-enabled healthcare chatbot (a computer program capable of processing and simulating human conversation) that provides patients with personalized information and decision-making support at different stages of the PS\&D pathway. It functions using a series of questions contained in the NEST with a branching logic sequence of questions and answers based on the responses to and from the patient, using a conversational and adaptable interaction. It also contains nested tiers of information, ranging from introductory to detailed information about patient engagement, health literacy, the different PS\&D options, and resources to learn about insurance coverage for PS\&D. OPUS was designed to be accessed by patients with different technological resources and preferences, using a cell phone, a mobile device, or a computer.