Ai escalation trace needed
AS AI ENTERS HEALTH, MENTAL HEALTH SERVICES NEED AN ESCALATION TRACE
New Zealand’s mental health and addiction telehealth system received an important safety lesson this week. The Government’s September 8 review found that core systems such as triage, risk assessment, escalation processes, and clinical governance are in place. Yet it also identified a consequential gap: services do not always have enough visibility into what happens after someone is referred or escalated to another service.
That problem matters well beyond telehealth. As health organizations explore artificial intelligence for administrative work, information handling, decision support, and other workflows, they should build continuity into the system from the start. A useful tool for doing so is an escalation trace.
Those questions become especially important when AI sits anywhere in the workflow. AI can help organize information, draft summaries, flag patterns, or reduce routine administrative work. But a fast first step can create false confidence if nobody can see whether a concern reached the right person or what happened next.
The telehealth review illustrates why. Safety depends on more than whether the first provider follows the correct process. It also depends on continuity after referral. The Government is responding with measures including a follow-up call pilot for people experiencing acute distress after an initial telehealth interaction and a dedicated national suicide-prevention crisis-line function.
The same systems logic should guide AI adoption. When an AI-supported process identifies something that requires human judgment, the handoff should not disappear into an inbox, queue, or vague instruction to “follow up.” The system should record that the threshold was crossed and name the person or team that now owns the next step.
This does not require a complicated new technology layer. A service could begin with a few required fields in an existing case-management or workflow system. The important point is organizational: responsibility must remain visible when work passes from software to people or from one service to another.
It can also improve learning. If a service records why escalations occurred, where handoffs slowed down, and whether follow-through happened, leaders gain evidence about the workflow rather than relying on impressions. They can see where staff need clearer rules, where technology creates extra work, and where human capacity needs reinforcement.
Mental health services should be particularly careful about treating automation as a substitute for responsibility. The value of AI should come from helping people perform appropriate work more reliably, while preserving clear human ownership where consequences are serious.
New Zealand’s telehealth review has already highlighted the danger of losing visibility after a handoff. As AI-supported health workflows develop, services have a chance to design that visibility in from the beginning. Every consequential escalation should leave a trace that a human can follow.
