| Course | NR 587AI Advanced Nursing Leadership in Artificial Intelligence-Integrated Healthcare Environments |
|---|---|
| Week | 4 |
| Paper type | Model risk assessment |
| Length | About 1,110 words, 4 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | MSN, Nurse Executive |
| Updated | October 2026 |
Free sample paper for NR 587AI Week 4
Model Risk Assessment: Over-Calls and Under-Calls of the Deterioration Index at a Threshold of 60, Each Costed Separately and Measured Against How Nurses Find Deterioration Today
Student Name
College of Nursing, Chamberlain University
NR 587AI: Advanced Nursing Leadership in Artificial Intelligence-Integrated Healthcare Environments
Instructor Name
Month Day, Year
Model Risk Assessment: Over-Calls and Under-Calls of the Deterioration Index at a Threshold of 60, Each Costed Separately and Measured Against How Nurses Find Deterioration Today
The Tool, the Decision and the Baseline
The tool is the deterioration index at its current threshold of 60 on our four medical-surgical units. The decision it touches is whether a nurse assesses a patient and considers calling the rapid response team. The baseline is what happens now without the score: nurses call the rapid response team using vital sign criteria and their own concern.
From our analyst's retrospective run on 2025 admissions, completed 14 February 2027:
At a threshold of 60, the tool flagged an average of 20 patients a day across 118 beds.
Of all flags, 9% were followed by the outcome (rapid response call, ICU transfer or death) within 12 hours.
Of the 412 outcomes in 2025, 64% had been flagged two or more hours ahead.
In 2025, 61 patients were transferred to intensive care without a rapid response call beforehand; nurses had not escalated them. Of those 61, the tool had flagged 23 at least 2 hours before transfer.
Over-Calls, Costed
About 18 of the 20 daily flags are not followed by the outcome.
Attention. A two-week time study on Medical B found a median of 11 minutes for a nurse to assess a flagged patient, review the chart and decide. Eighteen over-calls a day at 11 minutes is about 200 minutes of nursing time a day across the four units, roughly one nurse's 12-hour shift every three to four days.
Tests. In the time study, 1 in 6 assessments of flagged patients led to an extra laboratory test or a provider call that was not needed in hindsight.
Interruption. Flags cluster at 05:30 after morning laboratory results, the busiest hour before shift change.
Credibility. Each time a nurse walks to a flagged patient and finds nothing wrong, the next flag is taken a little less seriously. Ancker et al. (2017) found that the more often clinicians saw repeated alerts, the less likely they were to accept them. Our oncology unit's nurses already describe the red column as "always on."
Under-Calls, Costed
About 36% of outcomes in 2025, 148 of 412, had no flag at least 2 hours before.
Severity, traced. For a patient who deteriorates without a flag, what happens next depends on whether a nurse notices. If the nurse notices, the patient follows today's path, so the tool's miss costs nothing compared with the baseline. If the nurse does not notice, the patient may reach intensive care later and sicker. Escobar et al. (2020) found that an automated early warning program, with a dedicated team responding to alerts, was associated with lower mortality, which suggests that delays in recognition carry real risk.
The danger is a nurse relying on the score instead of their own judgment, and not escalating a patient because the score is not red. This is automation bias, the tendency to follow or wait for an automated cue. Goddard et al. (2012) found it common across clinical decision support. If nurses wait for red, the tool's misses become the patient's misses.
Compare Against Today, Not Against Perfect
Against perfection, the tool fails often: most flags are false and a third of events are missed.
Against today, the comparison is different. Of the 61 ICU transfers that nurses did not escalate, the tool flagged 23 in advance. That is the tool's added value: patients who may be found earlier than they are now. Its added cost is about 200 minutes of nursing time a day and some unneeded tests. Its added risk is automation bias.
Severity by Who Absorbs It
Over-calls are absorbed by bedside nurses, mainly on nights and on the oncology unit, and by patients who have extra blood drawn.
Under-calls are absorbed by patients whose deterioration is recognized late, and fall hardest where nurses rely on the score.
The 23 earlier flags benefit patients who would otherwise have reached intensive care without escalation.
Controls Attached to Named Failures
Over-call on the oncology unit: test a unit-specific threshold, chosen from our data to bring the oncology unit's positive predictive value up toward the other units'.
Flag surge at 05:30: ask the laboratory whether two units' morning draws can move to 03:00, spreading the flags earlier in the night.
Automation bias: state in policy and training that a score below 60 never overrides a nurse's concern, and keep the vital-sign criteria for rapid response calls unchanged.
Unaddressed flags: require a documented response to each flag within 60 minutes, so that the tool's alerts are either acted on or consciously declined.
Night lag: tell nurses that night scores may rest on old data until charting catches up.
What Nurses Told Us
In the time study, nurses on Medical B were asked after each flagged assessment whether the flag had told them anything new. In about one assessment in five, they said yes: the flag had drawn attention to a patient they had not been worried about. In the rest, they already knew the patient was unwell or judged the flag a false alarm. That proportion is close to the tool's added value in the retrospective run, and it gives a sense of how nurses experience the trade.
Who Decides on the Trade
The risk assessment does not decide whether the trade is worth it. That decision belongs to the quality committee, with the nurse managers whose staff absorb the cost and the medical director whose patients gain the benefit. The assessment's job is to make sure both sides of the trade are stated in the same terms.
Residual Risk
No control removes the basic trade between nursing time spent on false alarms and patients found earlier. The controls make each side of it smaller and easier to see.
With these controls, the tool would still produce many false alerts and still miss a third of events. The question for the decision paper is whether 23 patients a year flagged earlier, and possibly more with better charting, are worth about 200 minutes of nursing time a day.
Limits
The analysis rests on one year of retrospective data. Some 2025 outcomes may have been influenced by nurses who did look at the score, which could inflate or deflate the apparent added value.
References
Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Medical Informatics and Decision Making, 17, Article 36. https://doi.org/10.1186/s12911-017-0430-8
Escobar, G. J., Liu, V. X., Schuler, A., Lawson, B., Greene, J. D., & Kipnis, P. (2020). Automated identification of adults at risk for in-hospital clinical deterioration. New England Journal of Medicine, 383(20), 1951-1960. https://doi.org/10.1056/NEJMsa2001090
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121-127. https://doi.org/10.1136/amiajnl-2011-000089
What the NR 587AI Week 4 assignment asks for
The Week 4 assessment has two columns of cost and a baseline. Take one tool and one decision, then work each direction of error separately: what an over-call costs in attention, unnecessary tests and credibility, and what an under-call costs the patient it missed. State severity as what happens next rather than as a rating, and name by role who absorbs each cost. Compare the tool with what happens now, not with perfection. Attach each control to a named failure. Most rubrics mark down a tool measured against perfection, one direction written up while the other is ignored, severity asserted rather than traced and controls that float free of any failure.
How the NR 587AI Week 4 example is put together
The sample sets the baseline as nurse-initiated rapid response calls and reports a retrospective run: 20 flags a day, 9% followed by deterioration, 64% of events flagged ahead and 23 of 61 unescalated ICU transfers flagged first. Over-calls are costed at about 200 minutes of nursing time a day, extra tests, a 05:30 surge and lost credibility. Under-calls are traced to the risk of nurses waiting for red. The comparison with today sets the 23 earlier flags against those costs. Controls are attached to five named failures, nurses' own views are reported and the decision is assigned to the quality committee. Margin notes mark the two cost columns and the comparison with today against the rubric.
Where the points sit in the NR 587AI Week 4 rubric
Measuring the tool against perfection is the classic and most expensive error; this assessment compares it with current nurse-initiated escalation. Writing up only missed patients makes the tool look free, so over-calls are costed in minutes, tests and credibility. Asserting severity costs a row, while each direction here is traced to what happens next. Controls not tied to failures lose marks, and every control here names the failure it addresses. Naming who absorbs each cost by role earns the stakeholder criterion. Asking nurses whether flags told them anything new adds a bedside check on the figures. Assigning the final trade to the quality committee shows the assessment knows its role.
NR 587AI Week 4 help: mistakes that cost points
Weak NR 587AI Week 4 assessments list the tool's error rate and conclude it is unsafe. Others focus only on missed patients and ignore the cost of false alerts. Many give risk ratings such as moderate without saying what would happen to a patient. Credibility is rarely treated as a cost. Controls are often generic, such as more training. Set out both directions of error, compare each with what happens without the tool and tie every control to a specific failure. If your tool differs, send its threshold and any local performance figures, and a sample assessment can cost them the same way. Ask nurses whether each flag told them something new; their answers test the figures.
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NR 587AI Week 4 questions, answered
Do I need real performance figures to write this?
Constructed ones work as long as they are labeled as constructed and used consistently. What matters is the reasoning: a stated prevalence, a stated performance, and the arithmetic carried through to how many patients a week each direction of error touches. A marker can follow that and argue with it. Numbers lifted from a supplier's slide and presented as established fact are weaker than honest assumptions.
How do I weigh a rare severe error against a frequent mild one?
By saying openly which you are treating as worse and why, then showing what would change the answer if the ratio moved. Executive judgment is what the week is testing, so the position matters less than whether it is argued. What earns nothing is a paragraph listing both, calling the balance difficult and never settling it, because nobody can act on that.
Does this overlap with the oversight week?
They run in order rather than in parallel. This assessment establishes what could go wrong and how much it would cost. The oversight work decides who watches for it, how often, and at what point the tool stops running. Writing the two together usually produces a thin version of each, so keep the arithmetic here and the arrangements there.
Where can I find a free NR 587AI Week 4 sample paper?
The full model risk assessment costing over-calls and under-calls of a deterioration index against current practice is published on this page.
What is the tool's added value in the NR 587AI Week 4 assessment?
It flagged 23 of the 61 patients in 2025 whom nurses did not escalate before intensive care transfer, at a cost of about 200 nursing minutes a day.