Designing Guiding Principles for NLP for Healthcare: A Case Study of Maternal Health

Objective: An ethical framework for the use of large language models (LLMs) is urgently needed to shape how natural language processing (NLP) tools are used for healthcare applications. Drawing directly from the voices of those most affected, we propose a set of guiding principles for the use of NLP in healthcare, with examples based on applications in maternal health. Materials and Methods: We led an interactive session centered on an LLM-based chatbot demonstration during a full-day workshop with 39 participants, and additionally surveyed 30 healthcare workers and 30 birthing people about their values, needs, and perceptions of AI and LLMs. We conducted quantitative and qualitative analyses of the interactive discussions to consolidate our findings into a set of guiding principles. Results: Using the case study of maternal health, we propose nine principles for ethical use of LLMs, grouped into three categories: (i) contextual significance, (ii) measurements, and (iii) who/what is valued. We describe rationales underlying these principles and provide practical advice. Discussion: Healthcare faces existing challenges including the balance of power in clinician-patient relationships, systemic health disparities, historical injustices, and economic constraints. Our principles serve as a framework for surfacing key considerations when deploying LLMs in medicine, as well as providing a methodological pattern for other researchers to follow. Conclusion: This set of principles can serve as a resource to practitioners working on maternal health and other healthcare fields to emphasize the importance of technical nuance, historical context, and inclusive design when developing LLMs for use in clinical settings.