The research builds on a well-known puzzle in clinical psychology. Earlier work by Carol Thompson and colleagues, published in Nursing Research in 2008, showed that when nurses are placed under time pressure, their ability to detect high-risk patient conditions measurably declines. The hit rate for spotting deterioration dropped from 86 percent without time pressure to 68 percent under it, and the key signal detection measure of detectability, known as d-prime, fell regardless of how many years of experience the nurse had. But that landmark study left an important question unanswered: was the decline in accuracy simply the result of a speed-accuracy trade-off, the well-documented tendency of people to sacrifice precision when forced to act quickly, or was time pressure fundamentally altering the perceptual machinery of clinical judgement itself?

To answer that question, the Japanese team designed an ambitious laboratory experiment that treated nursing judgement like a psychophysical signal-detection problem. Twenty-one registered nurses, fourteen of them women with an average of 6.6 years of hospital experience, were recruited through snowball sampling. All had worked on hospital wards or in intensive care units, and all were right-handed. The researchers created eighty twenty-second video stimuli that mimicked the displays of bedside medical monitors, presenting seven vital-sign parameters drawn from the National Early Warning Score 2, or NEWS2, the widely used triage tool that aggregates respiratory rate, blood oxygen, systolic blood pressure, pulse, consciousness level, temperature, and oxygen supplementation into a single risk score.

Half of the eighty datasets represented low-risk patients with aggregate NEWS2 scores between zero and four, while the other half represented high-risk patients scoring seven or eight, the range associated with a substantially elevated probability of cardiac arrest, unplanned intensive care admission, or death within twenty-four hours. Three experienced nurses vetted every stimulus for face validity before testing began. Seated eighty centimeters from a laptop in a quiet, carefully controlled room, with display luminance held at 10.4 candelas per square meter, participants watched each simulated patient and pressed one of two mouse buttons to judge whether urgent intervention was warranted, responding as quickly and accurately as possible and then rating their confidence on a five-point scale.

The headline finding is deceptively simple: nurses assessed the risk of acute deterioration in approximately five seconds, with a mean reaction time of 5,169 milliseconds and an overall accuracy of about 76 percent. That speed is plausible, the authors note, because previous research found clinicians needed roughly six seconds to choose among four diagnostic options after reading a detailed patient case; here the information load was lighter and there were only two response options. Crucially, the measured detectability value of 1.71 landed squarely within the range reported by prior signal detection studies of nursing risk judgement, which have produced d-prime values between roughly 0.98 and 1.75, lending the new paradigm immediate credibility.

But the deeper discovery emerged when the researchers broke performance down by individual NEWS2 score. Repeated-measures analysis of variance revealed that stimuli with a score of zero, meaning every parameter was perfectly normal, produced both faster reactions and higher accuracy than every other score. From this pattern, Hishiya and Ishihara inferred a two-stage architecture for nursing risk perception. The first stage, which they call the normal-abnormal discrimination phase, is a rapid triage of whether anything is wrong at all, completed in under five seconds and apparently mastered by anyone holding a nursing license. The second stage, the risk evaluation phase, kicks in only once an abnormality has been detected, and its duration, estimated as the reaction time difference between score-zero stimuli and everything else, proved to be the real engine of individual performance.

The correlations here are striking. Nurses who took longer in this risk evaluation phase showed significantly lower detectability, with a correlation coefficient of minus 0.52, and a significantly more conservative response bias, with a correlation of plus 0.65. In signal detection terms, d-prime measures how well a perceiver separates true signals from noise, while the criterion C captures the liberal or conservative leaning of their decisions. A liberal nurse over-flags danger, triggering unnecessary interventions that can divert scarce staff from patients who genuinely need them; a conservative nurse under-flags, and a deteriorating patient may slip through unnoticed. The finding that the time spent evaluating abnormal stimuli predicts both accuracy and bias suggests that this evaluative stage, not basic abnormality detection, is where clinical judgement quality is forged, and where two of the twenty-one participants even showed the phases running in parallel rather than in sequence.

Notably, the study found no evidence of a speed-accuracy trade-off across individuals. The correlation between d-prime and overall reaction time was weakly negative and statistically non-significant, which the authors interpret as evidence that the accuracy losses previously observed under time pressure reflect genuine changes in perceptual-behavioral information processing caused by the situation, rather than a deliberate slowing-down trade. This distinction matters enormously for patient safety: if time pressure corrupts the processing pipeline itself rather than merely shifting a strategic dial, then interventions must target the processing environment, not just train nurses to be more careful when rushed.

Equally intriguing were the physiological results. The researchers attached silver-silver chloride electrodes to participants’ palms and forearms and recorded skin potential level, a measure of sympathetic nervous system arousal, sampled continuously at 1,000 hertz throughout the task. While arousal levels did not correlate with accuracy or speed, a striking pattern emerged around experience: nurses who reported frequent subjective involvement with acute deterioration showed significantly lower normalized skin potential than their colleagues with rare exposure. In other words, nurses who regularly face patient crises appear to run their judgement machinery at a calmer physiological register, echoing earlier findings that expert nurses maintain stable heart rates during clinical decisions while novices do not, and hinting at a low-arousal, low-cognitive-load strategy honed by repeated real-world exposure to emergencies.

Perhaps the most socially resonant discovery involves personality. Among the five basic traits measured with the Ten Item Personality Inventory, only agreeableness correlated with response bias, and it did so positively and significantly, with a coefficient of 0.57. Agreeable nurses leaned conservative, more inclined to judge ambiguous patients as low-risk. The authors speculate a gently uncomfortable possibility: highly agreeable nurses may weigh the busyness of their colleagues, consciously or not, and hesitate to escalate cases that would demand scarce human resources. Previous work has already linked organizational culture to risk-detection bias, and together these findings point toward a practical intervention already envisaged by the researchers: giving individual nurses structured feedback on their own decision-making tendencies, quantified through the same signal detection lens used in this study.

The research is not without limits, which its authors candidly acknowledge. The sample of twenty-one nurses is small, the stimuli omitted patient background details such as illness history and medication, and high-risk cases appeared in half the trials, far more often than the five to ten percent prevalence seen in real hospitals, a mismatch known in vision science to shift detection accuracy and decision criteria. Yet the study stands as a proof of concept for something genuinely new in nursing science: a way to probe the unconscious, intuitive machinery of clinical judgement without relying on what nurses can articulate about their own thinking. Since nursing judgement is frequently intuitive, built on rapid pattern recognition that practitioners themselves cannot fully explain, behavioral and psychophysical tools like reaction time analysis and signal detection theory may expose what subjective self-report never can. If follow-up studies manipulate time pressure, multitasking, and realistic case prevalence, the five-second risk radar documented here could become the foundation for a new generation of nursing education, staffing policy, and patient safety science, one calibrated not to what nurses say they do, but to what their minds demonstrably do in the critical first seconds of a crisis.

Subject of Research: The information processing mechanisms underlying nurses' clinical risk judgement, examined with signal detection theory and reaction time analysis

Article Title: Exploring information processing underlying risk judgement: Implications from signal detection theory and reaction time analysis

Article References: Hishiya, R., & Ishihara, M. (2026). Exploring information processing underlying risk judgement: Implications from signal detection theory and reaction time analysis. Heliyon, 12(14), Article e45422. https://doi.org/10.1016/j.heliyon.2026.e45422

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45422

Keywords: nursing, risk judgement, signal detection theory, reaction time, clinical decision-making, patient deterioration, NEWS2, skin potential level, response bias, detectability, time pressure, cognitive processing