Title: Already Heard - The AI-Powered Patient and What It Means for Healthcare Communications
Date: Wednesday 10th June, 12:30pm (BST)
Virtual Event
Presenter: Sonya Cullington - Cyberpsychologist | Helping the public sector govern AI with behavioural integrity
What is this session about?
Patients are arriving at clinical encounters in a different psychological state than they were five years ago. They are not coming from a search. They are coming from a conversation.
That conversation is increasingly with an AI system that has not interrupted them, not displayed impatience, and not visibly reacted to what they disclosed. Recent evidence shows patients are telling AI things they will not tell their doctor, particularly on mental health, sexual health, and substance use. They are doing so because they feel less judged.
What they are receiving in return is not information. It is psychological mirroring. Beliefs are forming, and conviction is being produced, before the patient ever reaches the clinical encounter.
This session will examine what the AI-Powered Patient means for healthcare communications, and where communications expertise needs to position itself to meet patients where they actually are.
If you are shaping how health and care services communicate or designing patient-facing digital services, this session will ask a question the sector has not yet named: what does it mean for the clinical encounter when patients arrive having already been heard?
Biography - Sonya Cullington
Sonya Cullington (MA, MSc) is a cyberpsychologist and policy advisor specialising in the "behavioural integrity" of digital systems, the gap between what technology can do and what it actually does to people when deployed at scale.
As Chair of the Patient and Public Advocacy Steering Committee at UK Digital Health and Care (UKDHC), she is a leading voice in ensuring digital health innovation remains ethical, inclusive, and grounded in public trust.
Sonya is the creator of the AI Judgment Framework, a cyberpsychological methodology designed for high-stakes environments like the NHS and public policy. Unlike traditional AI training, which focuses on prompt engineering, her framework focuses on cognitive oversight, teaching professionals to separate fluency (how well AI speaks) from veracity (how accurate it is), resist automation bias, and maintain epistemic vigilance in AI-augmented decision-making.
She advises public sector leaders on mitigating behavioural risks such as automation bias and digital exclusion, ensuring that emerging technology enhances human judgment rather than eroding it.