FAIR-Birth: Development and Feasibility Testing of an AI-Supported Advance Birth Planning Application for Midwifery-Led Antenatal Care—A Mixed-Methods Study Protocol

Background/Objectives: Clinical childbirth in high-income countries is increasingly shaped by standardised routines that do not always accommodate individual preferences. In Germany, approximately one in eight pregnant persons experiences clinically significant childbirth-related post-traumatic stress disorder symptoms, with pregnant persons facing language or health-literacy barriers being at particular risk of inadequate preference integration. Methods: This paper presents the conceptual foundation and proposed study design for FAIR-Birth, an interdisciplinary initiative to develop and feasibility-test a mobile application supporting Advance Birth Planning (ABP) embedded within midwifery-led antenatal care. The intervention combines four elements: transfer of the Advance Care Planning concept to antenatal care, a domain-restricted Large Language Model (LLM) supporting multilingual preference articulation, integration of the resulting ABP document into midwifery-led continuity of care, and iterative adaptation. Following the updated MRC framework, this study will employ a sequential mixed-methods design encompassing systematic review, participatory instrument development, Delphi consensus on the knowledge base, iterative technical development with usability testing, and a feasibility study across two perinatal centres in Bavaria. Results/Conclusions: FAIR-Birth is expected to generate a content-validated ABP instrument, a domain-restricted multilingual LLM dialogue system, and an evaluated application prototype. The work corresponds to the development and feasibility phases of the MRC framework; effectiveness questions are reserved for a planned subsequent randomised controlled trial.