Optimisation Drift and Substitution Risk in Artificial Intelligence-Supported Personalised Mental Health Nursing: A...

Artificial intelligence-supported personalisation is accelerating in mental health services, yet it can reclassify relationship, biography, and judgement as optional.

To explain how "personalisation" drifts into substitution and to specify minimum conditions for safe, workable implementation in mental health nursing.

A debate essay using purposive critical synthesis of empirical and conceptual literature to organise three recurring tensions and derive auditable safeguards.

Three tensions recur across current deployments: alliance-like chatbot support can be mistaken for therapeutic presence; automation can narrow clinical reasoning and contribute to deskilling; and trace-based personalisation can override care biography and erode trust through extractive data practices.

The common failure mechanism is optimisation drift under weak governance, where simulated alliance replaces therapeutic presence, metric authority displaces professional judgement, and proxy traces override care biography.

Service governance and procurement should treat therapeutic presence, narrative authority, and moral agency protection as non-negotiable clinical outcomes at the point of care.

Implement relationally accountable augmentation with explicit red lines, escalation pathways, protected relational time, biography-first documentation, and evaluation that tests accountability and safety under routine conditions.