Alert Fatigue Is a Continuum, Not a Switch: A Closer Read

"It's so mindless," one Australian junior doctor told the researchers. "All you process is, it's an ECG one, cancel, it's an ACD one, cancel." Another reached for an arcade metaphor: "It's like whack-a-mole. You're just getting rid of everything as fast as you can." These are not confessions of carelessness. They are descriptions of a skill — the learned reflex of a clinician keeping a system moving when that system asks for more attention than any human has to give.

That distinction is the whole argument of a qualitative study published on 19 February 2026 in the Journal of Medical Internet Research. Nicki Newton and eight colleagues at hospital and university groups in Australia interviewed 20 junior doctors — nine interns, six residents, five registrars — practising at ten sites across New South Wales, the Australian Capital Territory and South Australia. The interviews ran 18 to 56 minutes, averaging 33. The design is purely qualitative: semistructured conversations, analysed through two human-factors lenses — the Systems Engineering Initiative for Patient Safety (SEIPS) and Wickens and Carswell's model of how a person processes information. It counts no errors and proves no effect. It does what the noisier debate around alert fatigue rarely manages: it describes the mechanism carefully.

The finding worth keeping

The authors put their thesis plainly: alert fatigue is "not a binary concept but is instead experienced on a continuum." You do not simply have it or not. It surfaces at different points in how a clinician takes in an alert — and here the four-stage information-processing model does real work. An alert can fail at selection, where it is never detected at all. It can fail at perception, registered by shortcut: the colour and shape decoded, the words unread. Or it can fail at comprehension, demanding so much effort to interpret that the doctor disengages under the cognitive load. The same person slides between these states across a shift depending on the hour, the workload, the mood, and how many pagers and messages are competing for the same attention.

Framing it this way dissolves a convenient assumption — that fatigue is a property of a person, the careless intern or the burnt-out registrar. The doctors describe an equilibrium, not a defect. Lower the noise and attention returns; raise it and attention degrades again. Nothing is fixed, which is the hopeful part: a state that responds to conditions is a state you can change.

Two factors that live outside the interface

The study sorts contributing factors across five domains — technical, social, individual, organisational, environmental — but two of them deserve a manager's attention precisely because no software upgrade touches them. The first is social learning: junior doctors say they pick up their habits around alerts by watching senior colleagues, and a consultant who visibly and audibly clicks warnings away transmits a norm more efficiently than any policy ever will. The second is diffusion of responsibility: an alert addressed to a whole team or ward is read as everyone's business, which in practice makes it no one's. Both are organisational facts, not configuration settings — and neither is fixed by buying a better system.

Where its claims stop

The tier matters. This is twenty interviews with junior doctors in urban Australian hospitals running mostly commercial systems, looking only at knowledge-based alerts rather than newer AI-driven ones — a small, purposive, self-reported sample, as the authors themselves note. It tells you how fatigue is experienced and why; it cannot tell you how often, in how many clinicians, or with what measurable consequence for patients. The harms participants recall — a drug ordered against a documented allergy, a missed dose, advance-care planning that never happened — are recollections, not adjudicated events. There is no observational data and no quantification, and the authors are right to call for mixed-methods work with objective measures next. As a hypothesis-generating account of a mechanism, it is strong. As a measure of how dangerous alert fatigue is in aggregate, it cannot carry that weight, and does not pretend to.

Why it matters here

For European hospitals the practical lesson outlasts the small sample, because it concerns governance rather than statistics. If fatigue is a moving equilibrium, then the alert burden is a quantity to be managed continuously — reviewed, pruned, and owned by a named person — not set once at go-live and forgotten. The levers the study suggests are unremarkable, which is what makes them credible: alerts short enough to read at a glance (the authors point to roughly ten words, processable in about three seconds), visual distinction by criticality, a required justification before overriding a high-risk warning, and scheduled review of how many alerts the system actually fires. The harder lever is the one the evidence points to most clearly — the behaviour leaders model in front of trainees. A hospital that treats alert governance as a clinical-safety responsibility, rather than an afterthought to a software rollout, is acting on the most defensible part of this paper.

Source: Newton N, Bamgboje-Ayodele A, Forsyth R, et al. Experiences of Alert Fatigue and Its Contributing Factors in Hospitals: Qualitative Study. J Med Internet Res 2026;28:e78676. A qualitative study of 20 junior doctors at Australian hospitals, framed by human-factors theory; it characterises a mechanism through self-reported experience and does not measure error rates or patient outcomes.