Asian Journal of Medical Research and Case Reports

Background: Electronic patient-reported outcome measures (ePROMs) and artificial intelligence (AI) create opportunities to support longitudinal mental-health care. Their value depends, however, on the validity of what is measured, who can participate, how outputs are interpreted, and whether services can respond. These dependencies warrant particular attention in rural settings.

Objective: To propose an integrated methodological framework for guiding ePROM selection, design, disease modelling, interpretation, AI integration, and implementation in rural mentalhealth care.

Approach: A targeted narrative integration of measurement guidance, disease-course modelling methods, clinical prediction and AI standards, implementation frameworks, and selected rural mental-health studies informs a provisional conceptual framework. This is not a systematic review, consensus guideline, or empirical validation study.

Proposed Framework: Ten connected domains link rural context, instrument selection, measurement design, data quality and longitudinal change, disease modelling, assessment of AI suitability, interpretation, clinical decision-making, implementation, and evaluation. Disease modelling addresses trajectories and illness-state transitions; its use does not require AI. Each domain requires a documented decision, supporting evidence, an accountable owner, and criteria for progression or revision. An optional multi-objective optimisation layer uses evolutionary algorithms to explore ePROM subsets, adaptive configurations, and monitoring choices under measurement, safety, and resource constraints.

AI is Optional: Implementation should not proceed merely because a model is available. Equity, patient involvement, safety, and data governance apply across the pathway. Hypothetical service and compartmental examples illustrate non-AI decision-making and basic calculations without claiming empirical validation. A staged evaluation agenda separates the usability and decision quality of the framework from disease-model validity, predictive performance, and the effects of a resulting care intervention.

Conclusion: The proposed contribution is an auditable connection between measurement, analysis, and clinical action rather than a new instrument or algorithm. Participatory refinement and comparative evaluation are needed before claims of improved care, equity, or outcomes can be made.