NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs (2312.11803v2)

Abstract: Ethical frameworks for the use of NLP are urgently needed to shape how LLMs and similar tools are used for healthcare applications. Healthcare faces existing challenges including the balance of power in clinician-patient relationships, systemic health disparities, historical injustices, and economic constraints. Drawing directly from the voices of those most affected, and focusing on a case study of a specific healthcare setting, we propose a set of guiding principles for the use of NLP in maternal healthcare. 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 NLP tools in the context of maternal health. We conducted quantitative and qualitative analyses of the survey results and interactive discussions to consolidate our findings into a set of guiding principles. We propose nine principles for ethical use of NLP for maternal healthcare, grouped into three themes: (i) recognizing contextual significance (ii) holistic measurements, and (iii) who/what is valued. For each principle, we describe its underlying rationale and provide practical advice. This set of principles can provide a methodological pattern for other researchers and 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 NLP technologies for clinical use.

• The paper introduces nine guiding principles for ethically leveraging large language models in maternal healthcare.
• It employs participatory design with healthcare providers and birthing individuals to ensure empathetic and bias-aware applications.
• Findings offer a replicable blueprint for deploying NLP in sensitive healthcare areas while addressing power imbalances and socio-political impacts.

Ethical Guidelines for NLP in Maternal Healthcare

The intersection of NLP and healthcare promises a transformation in the ways care is delivered, particularly in sensitive areas such as maternal healthcare. The paper "NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs" by Antoniak et al. discusses the ethical utilization of LLMs in this domain. The authors propose a structured set of principles to ensure the benefits of these technologies are maximized while minimizing potential harms.

The research delineates nine guiding principles arranged into three key thematic categories: context, measurements, and values. These principles were derived from a rich body of data gathered through participatory design frameworks encompassing healthcare workers, birthing individuals, and workshop interactions. The paper emphasizes elevating underrepresented voices, which is crucial given healthcare's complex power dynamics and historical biases.

Contextual Insights

1. Power Dynamics: Recognizing existing power structures within the care team is emphasized. Historically, shifts in power dynamics have affected roles such as midwives and doulas. LLM applications should not exacerbate these dynamics but rather support cohesive team functions.
2. Political Implications: The deployment of NLP tools should be aware of the medical data's socio-political implications. Data collection processes and resultant narratives could unintentionally reinforce deficit narratives or serve unforeseen political ends.
3. Leverage Community Wisdom: The integration of existing community knowledge and practices is advocated, avoiding the disruption of well-established communal support systems.

Measurement-Centric Principles

1. Comprehensive Outcomes: In addition to traditional outcome measures, the paper suggests that NLP applications consider wider dimensions of patients' experience, reinforcing the holistic well-being of the birthing person.
2. Fair Treatment: Ensuring that models do not perpetuate or amplify existing disparities in treatment is a critical principle. This involves careful dataset curation and model evaluation across diverse demographic groups.
3. Human Empathy: While automating responses, the human elements of empathy and emotion should not be sidelined. Tools should be designed to augment the relational aspects of care, acknowledging that each patient's situation is unique.

Value Alignment and User-Centric Design

1. Inclusion of Birthing Voices: The principle of participatory design underscores the involvement of actual care-seekers in tool design, aligning systems closer to user needs and expectations.
2. Agency and Autonomy: NLP tools should bolster the decision-making capacity of birthing individuals rather than subjugate their autonomy to automated decisions.
3. Respect for Data Sources: Ethical NLP applications must respect the origin of their data and acknowledge the communities that contribute to its richness.

Broader Implications and Future Directions

The implications of these findings extend beyond maternal healthcare, providing a blueprint for ethical AI implementations in other healthcare areas. The participatory approach adopted in this research could be replicated to frame ethical guidelines in other sectors, reflecting a nuanced understanding of specific contextual needs.

This paper provides a comprehensive resource for practitioners seeking to apply NLP technologies in ethical and context-sensitive ways. It reinforces the importance of multi-stakeholder engagements to balance technological capabilities with the realities of diverse healthcare environments. Looking forward, ongoing refinement of these principles, coupled with technological advances, holds promise for fostering equitable and effective healthcare solutions leveraging NLP.