| Course | NR 587AI Advanced Nursing Leadership in Artificial Intelligence-Integrated Healthcare Environments |
|---|---|
| Week | 2 |
| Paper type | Tool evaluation brief |
| Length | About 1,110 words, 5 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | MSN, Nurse Executive |
| Updated | October 2026 |
Free sample paper for NR 587AI Week 2
Tool Evaluation Brief: Written Questions for the Deterioration Index on Four Medical-Surgical Units, Each With an Owner and a Standard for an Acceptable Answer
Student Name
College of Nursing, Chamberlain University
NR 587AI: Advanced Nursing Leadership in Artificial Intelligence-Integrated Healthcare Environments
Instructor Name
Month Day, Year
Tool Evaluation Brief: Written Questions for the Deterioration Index on Four Medical-Surgical Units, Each With an Owner and a Standard for an Acceptable Answer
The Tool and the Decision
The tool is the deterioration index supplied by our record vendor, which produces a score from 0 to 100 every 15 minutes for every adult inpatient and turns red at 60. It has run on our four medical-surgical units since 2024 without a written evaluation. The decision is whether to keep it running as it is, restrict it or stop it, to be made by the nursing and medical quality committee in June 2027. This brief sets out the questions that must be answered first, who owns each and what an acceptable answer looks like.
Family 1: What Population It Learned From
Q1. On what patients, hospitals and years was the model developed and validated? Owner: vendor. Acceptable answer: number of admissions, number and type of hospitals, years, and the share of patients over 75, transferred in and admitted at night. Not acceptable: "a large, diverse population."
Q2. Has the model been validated at any community hospital under 300 beds? Owner: vendor. Acceptable answer: the hospitals, sample sizes and performance figures, published or shared in writing.
Q3. What is our own case mix in the same terms? Owner: our clinical informatics analyst. Acceptable answer: the same figures as Q1, for our four units in 2025.
Family 2: What It Does When Unsure
Q4. What happens to the score when inputs are missing, for example when vital signs have not been entered for six hours? Owner: vendor. Acceptable answer: a written description of how missing values are handled and whether the screen shows that the score is based on old data.
Q5. Does the model report any measure of its own uncertainty? Owner: vendor. Acceptable answer: yes with an example, or a plain no.
Family 3: What Arrives on the Screen and What It Costs the Reader
Q6. At the threshold of 60, how many patients a day would we expect to flag on our four units, and what share of them go on to need intensive care or die within 12 hours? Owner: our analyst, from a retrospective run on 2025 data. Acceptable answer: flags per day per unit and the positive predictive value with its interval.
Q7. Who is expected to read the score and what are they expected to do within what time? Owner: the nursing director over the four units, with nurse managers. Acceptable answer: a role, an action and a time, written into policy.
Q8. How long does it take a nurse to assess a flagged patient? Owner: nurse managers, timed over a fortnight on Medical B. Acceptable answer: median minutes per flag.
In a study of decision support alerts, acceptance dropped as clinicians grew busier and saw the same alert again and again (Ancker et al., 2017). Q6 and Q8 together measure how much attention the tool asks for.
Family 4: How Anyone Would Find Out Performance Has Slipped
Q9. Does the vendor monitor the model's performance at each site after go-live, and will it share the results? Owner: vendor. Acceptable answer: what is measured, how often and an example report.
Q10. Will the vendor tell us in advance when the model is retrained or changed? Owner: vendor. Acceptable answer: a contract term with notice period.
Q11. What will we measure locally each month? Owner: our analyst and the quality committee. Acceptable answer: named figures, such as flags, overrides and events after flags, with an author and a schedule.
One Number Is Not Performance
The vendor's materials report a single area under the curve figure from its development data. That number describes how well the model ranks patients overall. It does not say how many false alerts nurses will see at our threshold, how the model performs on older patients or on nights, or whether performance holds at a smaller hospital. Singh et al. (2021) found that a widely used deterioration index performed reasonably at ranking patients with COVID-19 overall but less well at particular thresholds that hospitals might use. Q6, Q8 and the subgroup parts of Q1 and Q3 exist because one number cannot answer them.
Why These Families
The four families follow the life of a prediction: where the model learned, how it behaves when its inputs are thin, what it asks of the person reading it and how anyone would notice it getting worse. A widely implemented sepsis model, when tested outside the hospitals that built it, performed well below the vendor's figures (Wong et al., 2021), and the hospitals using it had not asked these questions in advance. Each family is meant to catch a different way that could happen here.
Not Every Question Is the Supplier's
Five of the eleven questions are ours to answer: our case mix, our expected flags, who reads the score, how long assessment takes and what we will monitor. A brief that sends everything to the vendor would leave the most important questions unanswered, because the vendor cannot know our staffing or our policies.
Silence Is a Finding
If the vendor does not answer Q1, Q2, Q4 or Q9 in writing by 15 January 2027, that silence will be reported to the committee as a finding. A supplier that cannot say where its model was developed or whether it is monitored has told us something about how much weight its output should carry.
How the Answers Will Be Used
The answers feed four later pieces of work: a comparison of training data with our patients (Q1 to Q3), a risk assessment of each direction of error (Q4 to Q8), an oversight plan (Q9 to Q11) and the June decision. Each answer will be filed with its date and author.
What We Will Not Ask
We will not ask the vendor to explain the model's internal workings in detail. Even a full answer would not tell us how it performs on our patients, and the questions above get at that directly. We will also not ask for a guarantee of accuracy, which no supplier could give honestly.
Timetable
Questions sent to the vendor's account manager on 4 November 2026. Internal answers due 15 December. Vendor answers due 15 January 2027. Summary to the committee in February, with any unanswered questions listed by owner.
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
Singh, K., Valley, T. S., Tang, S., Li, B. Y., Kamran, F., Sjoding, M. W., Wiens, J., Otles, E., Donnelly, J. P., Wei, M. Y., McBride, J. P., Cao, J., Penoza, C., Ayanian, J. Z., & Nallamothu, B. K. (2021). Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19. Annals of the American Thoracic Society, 18(7), 1129-1137. https://doi.org/10.1513/AnnalsATS.202006-698OC
Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., Pestrue, J., Phillips, M., Konye, J., Penoza, C., Ghous, M., & Singh, K. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065-1070. https://doi.org/10.1001/jamainternmed.2021.2626
Reading the NR 587AI Week 2 instructions
The Week 2 brief is a set of questions with an owner beside each, arranged so a supplier could reply in writing. Name one tool and the decision it touches. Work through four families: what population it learned from, what it does when unsure, what arrives on a screen and what that costs the reader, and how anyone would find out later that performance slipped. Beside each question, note what an acceptable answer looks like. Most rubrics mark down a page of challenges no supplier could answer, a question with no standard attached, every question sent to the supplier when some can only be answered inside the organization and a single accuracy figure accepted as performance.
How this NR 587AI Week 2 example is built
The sample names the vendor's deterioration index and a June decision to keep, restrict or stop it. Eleven questions follow in four families, each with an owner and an acceptable answer, such as flags per day per unit with positive predictive value from a local run. Internal questions cover case mix, who reads the score and how long assessment takes. A section explains why the families follow the life of a prediction. Another explains why one summary figure cannot answer threshold and subgroup questions. The brief says what it will not ask, treats unanswered vendor questions as findings and sets a timetable. Margin notes show where each family and the silence rule earn credit.
NR 587AI Week 2 rubric: what full points look like
The dearest mistake is a page of challenges nobody could respond to, and every question here can be answered in writing. A question with no standard leaves a reader unable to tell an adequate reply from a reassuring one, so each question carries its standard. Sending everything to the supplier costs a row, and five questions are assigned to the hospital. Accepting one accuracy figure loses marks, and the brief explains why thresholds and subgroups matter. Treating supplier silence as a finding earns credit. A timetable with dates shows the brief is meant to produce answers. Saying what the brief will not ask shows its questions were chosen for evidence rather than to look demanding.
Common NR 587AI Week 2 mistakes, and how to avoid them
Weak NR 587AI Week 2 briefs list tough-sounding questions no vendor could answer. Others ask good questions but give no idea what a good answer would be. Many forget that questions about staffing, case mix and who reads the output must be answered internally. Some accept the vendor's headline accuracy figure. Few say what happens if the vendor does not reply. Write each question so it can be answered in writing, put an acceptable answer beside it and assign it to the vendor or to your own team. If your tool differs, send its name and the decision it touches, and a sample brief can be laid out the same way.
Get a NR 587AI Week 2 example written to your instructions
Send the prompt, the rubric and any material your section supplied about the tool, and we will write a custom NR 587AI Week 2 brief to those requirements. It arrives inside 24-48h, the first one is free, and setting it beside your own draft tends to show which of your questions had no answer standard attached.
More NR 587AI papers and MSN samples
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- NR 587AI Week 7: Disclosure Brief
- NR 587AI Week 8: Deployment Decision Paper
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NR 587AI Week 2 questions, answered
Do I need a real product to write this about?
A named one helps and most classrooms allow it, provided claims about what the tool does are attributed to published material rather than stated flatly. A constructed tool works equally well when the brief describes it precisely: what it predicts, for whom, how often it runs, and where its output appears. What cannot be invented is the decision it touches, because the whole question set hangs off that.
How many questions belong in the brief?
Fewer than students list, each carrying more weight. Twelve to eighteen entries with owners and answer standards read as executive work, while forty generic ones read as a search result. Where a classroom sets a count, follow it, and use the ordering rather than the length to show which items would stop the tool from going live at all.
Is this the same as choosing between products?
No, and drifting into a comparison is the commonest way this brief loses its shape. Selection work weighs candidates and ends in a preference. This one takes a tool already proposed and sets out the evidence it would have to produce before anybody let it near a decision on your patients. The evidence differs too: local records and answers in writing rather than pricing and references.
Where can I find a free NR 587AI Week 2 sample paper?
The full evaluation brief with eleven owned questions about a deterioration index and an answer standard for each is published on this page.
Why is one accuracy figure not enough in the NR 587AI Week 2 brief?
It describes overall ranking, not false alerts at the hospital's threshold, performance for older patients or nights, or performance at a smaller hospital.