Development and planning for future scenarios in ophthalmology: content generation using a modified Delphi process

Abstract

Purpose: To identify the most influential drivers shaping the field of ophthalmology and develop expert-derived, consensus-driven future scenarios. Design: A mixed-methods study. Methods: A modified Delphi process was performed to develop and build expert consensus on future scenarios within ophthalmology. Initial faculty surveys were used to identify critical drivers in the field. Focus groups with key opinion leaders (KOLs) in the United States were conducted to discuss implications of these drivers within the field in the context of "aspirational", "conventional", and "bleak" state scenarios. Focus groups and surveys were qualitatively analyzed using grounded theory principles of coding to develop the future scenarios. Drafted scenarios were then sent back to KOLs for feedback to achieve consensus. Results: Twenty-seven faculty responded to an initial survey, identifying five key drivers: artificial intelligence in eye care, health policy and financial reform, physician shortages, the aging population, and research funding. Thirty-one experts participated in five focus groups, yielding 276 coded quotations. Discussion centered most heavily on artificial intelligence (AI) and least on aging. Across all drivers, 51% of coded data reflected conventional projections, 30% aspirational, and 19% bleak. Aspirational futures emphasized whole-person preventive care, AI integration with proper safeguards, equitable workforce distribution, enhanced advocacy for research, and improved care access utilizing assistive technologies. Conversely, bleak futures involved drastic funding cuts, regulatory misalignment in AI, worsening physician shortages, and system overload from demographic pressures. Conclusion: Scenario planning reveals that ophthalmology's trajectory depends on proactive strategies to strengthen research advocacy, adopt population-based care models, optimize the workforce, and ensure responsible AI implementation.