Last Updated on August 28, 2026 by Nurseslab.in Editorial Team
Introduction
AI Early-Warning Score, there are two moments that define a nurse’s relationship with a deterioration algorithm.
The first: the score fires, you walk in, and the patient is sitting up eating breakfast, looking better than they did yesterday.
The second: the score is quiet, everything charts within range, and something about the patient is wrong in a way you can’t yet put into a number.
Both of these are overrides. They are not the same kind of override, they don’t carry the same risk, and the way you handle them should be completely different. Most of the education nurses receive on early-warning systems covers neither — it covers how to respond to the alert, not how to think about it.
That gap matters more each year, because these tools are now everywhere. Sepsis prediction models, deterioration indices, fall-risk algorithms, and unplanned-transfer predictors are running in the background of most acute care EHRs. A 2025 narrative review of AI in ICU nursing identified patient risk prediction as the single most prominent area of AI research in critical care nursing, with 30 separate studies building and validating models to forecast deterioration, mortality, delirium, ICU transfer, and readmission.
What these tools actually are — and aren’t
An early-warning score is a probability statement about a population, applied to an individual. That sentence is worth reading twice, because almost every misunderstanding of these tools traces back to forgetting it.
When a deterioration index returns a high score, it is not saying “this patient is deteriorating.” It is saying “patients whose data looked like this, in the population this model was trained on, deteriorated more often than patients whose data didn’t.” Your patient may be one of them. Statistically, more often than not, they aren’t.
That’s not a flaw in the tools. It’s what prediction is. But it has direct implications for how you should weight the output against what you see in the room.
It’s also worth knowing that these tools vary enormously in rigor. Some are rule-based scores with decades of validation behind them, like NEWS2. Some are proprietary machine-learning models whose internals the vendor doesn’t disclose. A few now carry regulatory authorization — the Sepsis ImmunoScore became the first AI diagnostic for sepsis authorized by the FDA in 2024, using up to 22 data points including vitals, labs, demographics, and sepsis biomarkers to place patients into risk categories. Most of what’s running on your unit does not have that pedigree.
What the validation evidence actually shows
This is where nurses deserve honesty, because the published performance of widely deployed models is considerably worse than their marketing suggests.
The most cited example is the Epic Sepsis Model. A retrospective external validation at Michigan Medicine, published in JAMA Internal Medicine, examined more than 38,000 hospitalizations and found the model failed to identify 67% of patients with sepsis while generating alerts on 18% of all hospitalized patients — a combination the authors described as creating a large burden of alert fatigue. At the commonly used threshold, positive predictive value was 12%. The model identified only 7% of septic patients who had been missed by a clinician.
A separate external validation across two county emergency departments, published in JAMIA Open, reported a positive predictive value of 7.6% and sensitivity of 14.7% for sepsis occurring within six hours of the alert. In half the encounters where the model alerted on a patient who did have sepsis, the alert came after sepsis had already occurred.
Epic pushed back on the Michigan findings, arguing that the researchers used a low threshold appropriate for a rapid response team casting a wide net rather than one tuned for bedside clinicians, and that each health system must set thresholds balancing false negatives against false positives. That’s a fair point, and it contains an important lesson: the same model behaves very differently depending on how your hospital tuned it. The alert on your unit is a local configuration decision, not a fixed property of the software.
The newer version performs better and still isn’t clean. A multicenter prospective validation of Epic Sepsis Model version 2 across 227,091 inpatient encounters at four major US health systems found AUC between 0.82 and 0.92 — respectable discrimination — alongside high institutional variability, low positive predictive value, and high alert burden. Number needed to evaluate ranged from 21 to 35 at the 12-hour horizon. The authors’ recommendation was that institutions conduct local validation, build workflows to manage false positives, and implement alert-silencing strategies.
Read that last sentence again from a bedside perspective. The researchers who validated the model are recommending that hospitals design systems to manage the false positives. They are describing your Tuesday.
And there’s a documented consequence when they don’t. In one implementation where remote clinicians monitored algorithm output, the volume of interruptions led floor nurses to cover the camera. That’s what happens when a tool’s alert burden exceeds its credibility.
The asymmetry that should govern every override
Here is the single most important principle in this entire topic:
The two override directions carry radically different risk. Treat them differently.
Escalating when the score is low but you’re worried is almost always safe. The cost is a phone call, a set of repeat vitals, maybe an unnecessary rapid response. The benefit is catching what the model missed — and given sensitivity figures in the range the validation literature reports, the model misses a great deal.
Standing down a high score because the patient looks fine is where harm lives. The cost of being wrong is a missed deterioration with documentation showing you were told and didn’t act.
This asymmetry means “override” should almost never mean dismiss. It should mean contextualize, act proportionally, and document your reasoning. There’s a version of “overriding the algorithm” that is expert practice, and a version that is silently clicking through a best-practice advisory at 0300. The distinction is entirely in what you do next.
Override type one: the score is high, the patient looks well
This is the common one, and given the positive predictive values above, most of these alerts genuinely are false positives. But “most” is not “this one.”
Do this, not that
Don’t: dismiss the alert from the workstation. Do: lay eyes on the patient. Every time. This is non-negotiable, and it’s the entire value of the alert even when the alert is wrong — it prompted an assessment that wouldn’t otherwise have happened at that moment.
Don’t: assume you know why it fired. Do: look at what’s driving it. Most systems show contributing variables. If the score is being driven by a lactate from six hours ago and a heart rate that normalized after pain medication, that’s very different from a score driven by a rising respiratory rate over three consecutive sets.
Don’t: treat “looks fine right now” as the end of the assessment. Do: check the trajectory. Early warning tools are often detecting a trend you haven’t consciously registered. A patient can look fine at the moment their trend turns.
Common legitimate sources of false positives
These are the patterns experienced nurses learn to recognize. They are reasons to contextualize, not reasons to skip the assessment:
- Chronic abnormal baselines. A patient with COPD living at a saturation of 88% and a respiratory rate of 24 will light up a score built on general population norms, permanently.
- Rate-controlled atrial fibrillation, chronic tachycardia, beta blockade. Both directions distort vital-sign-driven models.
- Expected post-operative physiology. Post-op tachycardia and low-grade temperature elevation are frequently flagged.
- Timing artifacts. Vitals taken immediately after ambulation, during a dressing change, or mid-panic attack.
- Pain and anxiety. Both drive heart rate, respiratory rate, and blood pressure without indicating sepsis.
- Dialysis patients and end-stage renal disease. Lab-driven models handle them poorly.
- Documentation lag. The score may be computed on stale data. If the score is high and your fresh vitals are reassuring, you may simply have newer information than the algorithm.
That last one is important and underappreciated. Sometimes you’re not overriding the model — you’re just ahead of it.
What to actually do
Assess. Get a current, complete set of vitals. Look at the trend, not the point. Consider whether the drivers make clinical sense for this patient. Then act proportionally: increase monitoring frequency, notify the provider, escalate — or document that you assessed, and why the elevated score is attributable to a known chronic factor.
Then, if it’s a recurring false positive on a specific patient, tell your informatics team. That’s how thresholds get fixed.
Override type two: the score is low, and you’re worried
This is the override that matters most, and it’s the one nurses are least often given explicit permission to make.
Given documented sensitivity as low as 14.7% in one ED validation and 67% of septic patients unrecognized in another, a reassuring score is weak evidence of stability. A low score should never be the reason you don’t escalate.
What scores structurally cannot see
- Mentation change. Subtle confusion, a patient who’s slightly less engaged than four hours ago, the family member who says “he’s not himself.” Almost nothing in a vitals-driven model captures this, and it is one of the earliest signs of deterioration in sepsis, hypoxia, and hemorrhage.
- Appearance. Mottling, a grey cast, a change in how a patient is breathing that hasn’t yet changed the respiratory rate number.
- Pain out of proportion to the expected clinical picture.
- Trajectory within normal. A heart rate moving from 62 to 88 over six hours is entirely normal at every point and may be the most important data on the chart.
- The compensating patient. Young, previously healthy patients maintain normal vitals until they suddenly don’t. Physiologic reserve is the enemy of early warning scores.
- Context the model never received. Anything not entered in a structured field doesn’t exist to the algorithm.
The gut feeling is data
Nurse concern — the unquantified sense that a patient is going bad — has repeatedly been shown to carry predictive value, which is why most rapid response criteria include a “staff member is worried” trigger. That criterion is not a courtesy. It’s there because it works, and because the people who designed those systems knew that structured criteria miss things.
If your assessment and the score disagree and your assessment says worse, act on your assessment. Every time. There is no defensible version of “the score was low so I waited.”
Why the scores miss things — the structural reasons
Understanding the failure modes makes you better at anticipating them.
Lagging and missing inputs. Models score what’s in the record. A patient whose vitals were charted at 0800 is being scored on 0800 physiology at 1100.
Label problems. Sepsis models are trained on retrospective sepsis labels, which are themselves imperfect and often derived from billing codes or treatment patterns. The original Epic model included provider antibiotic orders as an input — a form of data leakage, since ordering antibiotics means infection is already being considered.
Population shift. A model validated on one health system’s patients performs differently on yours. This is precisely why the multicenter ESM v2 study found high institutional variability, and why local validation is the recommendation.
Threshold politics. Where your hospital set the cut-point determines whether you get a flood of false positives or a quiet system that misses people. Somebody made that choice. It’s rarely explained to the nurses living with it.
Everything unstructured. Your narrative note, the family’s comment, the way the patient looked — none of it typically reaches the model.
A workable framework at the bedside
When an alert fires and you’re deciding what to do, four questions:
1. What is the patient actually doing right now? Eyes on. Current vitals. Mental status. Work of breathing. This happens before anything else, regardless of what you suspect about the alert.
2. What is driving the score, and does it make sense for this patient? Open the contributing factors. Is this chronic baseline, a timing artifact, or a genuine change?
3. What direction is the trend going? Compare across the shift, not against the threshold. Trajectory beats snapshot.
4. If I’m wrong about this, what happens? The asymmetry question. If you’re wrong about a false positive, you did an unnecessary assessment. If you’re wrong about a real deterioration, the patient codes at 0400. Let the consequences shape how much certainty you need.
Documenting an override defensibly
Whatever you decide, the documentation standard is the same: what you assessed, what you found, what you concluded, and what you did.
A defensible note reads something like: elevated deterioration score noted, patient assessed at [time], full vitals obtained and documented, patient alert and oriented with no change from baseline, elevated score attributable to chronic baseline respiratory rate, provider notified, monitoring frequency increased to q2h.
An indefensible chart shows an alert, no corresponding assessment, and no note.
This matters legally as well as clinically. You are accountable for your response to information available in the record. An unexplained gap between a high score and no documented assessment is difficult to defend, and it’s a pattern that shows up in retrospective review after adverse events.
Building the judgment — for individuals and units
If you’re a newer nurse: Your risk profile is specific — you’re more likely to over-trust the score, in both directions, because you have less internal baseline to weigh it against. Deliberately practice predicting: before you look at the score, decide what you think it should be. Then check. That feedback loop builds calibration faster than anything else. And escalate freely; nobody has ever been disciplined for a rapid response that turned out to be unnecessary in a functioning culture.
If you’re a preceptor: Make your reasoning audible. When you dismiss an alert, say why out loud. When you escalate against a reassuring score, say what you saw. The tacit knowledge here is exactly the kind that disappears if it isn’t narrated.
If you’re a charge nurse or educator: Track the false positive patterns on your unit and route them to informatics. Alert burden is a fixable problem, and the people who can fix it don’t know what’s happening on your floor unless you tell them.
If you’re a nurse leader: Two things. Publish your local validation data — nurses trust tools whose performance they can see, and distrust tools that arrive with no accuracy figures. And make it explicit, in policy, that a low score never overrides nurse concern. If that isn’t written down somewhere, nurses will eventually assume the opposite.
The bottom line
Deterioration algorithms are useful. They catch patterns across more variables than any human tracks simultaneously, and there’s real evidence they surface some deterioration earlier than unaided observation. Nobody serious is arguing for turning them off.
But they are probability estimates built on populations, running on incomplete and sometimes stale data, tuned by a threshold decision you probably weren’t part of, and validated — where they’ve been validated at all — with positive predictive values that would be unacceptable in a lab test.
The right posture is neither compliance nor dismissal. It’s treating the score as one more assessment finding: real information, weighted appropriately, integrated with everything else you know. A high score earns a look. A low score earns nothing at all if your patient looks wrong.
The algorithm has access to the chart. You have access to the patient. Those are different, and the second one is still the more important of the two.
Has a low score ever made you second-guess an escalation you were right about? That’s the story other nurses need to hear.
REFERENCES
- Park, D., So, K., Prabhakar, S.K. et al. Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review. Biomed. Eng. Lett. 15, 717–734 (2025). https://doi.org/10.1007/s13534-025-00486-4, https://pubmed.ncbi.nlm.nih.gov/40621610/
- Censinet.com, When the Model Is Wrong: Clinical Override Protocols for AI Recommendations, April 9 2026, https://www.censinet.com/perspectives/when-model-is-wrong-clinical-override-protocols-ai-recommendations
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