| Course | NR 587AI Advanced Nursing Leadership in Artificial Intelligence-Integrated Healthcare Environments |
|---|---|
| Week | 1 |
| Paper type | Discussion post with two replies |
| Length | About 590 words |
| Format | Discussion post with APA 7 citations |
| School | Chamberlain University |
| Program | MSN, Nurse Executive |
| Updated | October 2026 |
Free sample paper for NR 587AI Week 1
Week 1 Discussion: Initial Post
Four Automated Outputs on Our Medical-Surgical Units, Ordered by Distance From the Patient, and the One Where Nobody Is Sure Who Acts
Initial Post
I direct nursing for four adult medical-surgical units, 118 beds in all, in a 240-bed community hospital. Rather than argue about artificial intelligence, here are four automated outputs our nurses meet every shift, ordered from closest to the patient to furthest.
1. Heparin dose suggestion. When a nurse enters a new partial thromboplastin time, the record proposes the next infusion rate from the protocol. Reader: the bedside nurse. Expected action within the next minute: accept the rate or change it and document why. A wrong suggestion reaches the patient within minutes.
2. Sepsis alert. A pop-up appears when vital signs and laboratory values meet screening criteria. Reader: the bedside nurse. Expected action: complete a sepsis screen in the record and notify the provider if positive. It interrupts whatever the nurse is doing.
3. Deterioration index. A score from 0 to 100 sits in a column of the patient list, updated every 15 minutes from vital signs, laboratory results and nursing assessments, and turns red at 60. Reader: unclear, which I return to below.
4. Acuity score. Each patient gets acuity points that the charge nurse uses to build the next shift's assignments at 06:30 and 18:30. Reader: the charge nurse. Expected action: balance assignments. It touches patients only through who is assigned to them.
Ordering them this way shows they carry very different risks. A wrong heparin suggestion can harm a patient in an hour. A wrong acuity score makes a nurse's night harder.
The deterioration index is the interesting one. It is the only one of the four that predicts rather than calculates; it estimates the chance a patient will need intensive care or die within the next 12 hours. And nobody owns the red score. When I asked six nurses who should act when a patient's score turns red, I got three answers: the bedside nurse, whoever is in charge that shift, and "the rapid response team sees it." The rapid response team does not see it; the column is only on the unit's patient list. Some nurses check the column every hour. Some never look. Nothing in our policies says what a red score requires.
That matters because predictive tools are not perfect. A widely used sepsis prediction model, tested outside the hospitals where it was built, missed many patients and alerted on many who were not septic (Wong et al., 2021). A deterioration score with no named reader could be wrong in either direction and nobody would notice. Worse, clinicians tend to lean on automated cues once they exist (Goddard et al., 2012), so some nurses may already be waiting for red.
My question for classmates: for any predictive score on your units, can you name the role expected to act on it, and is that written anywhere?
Reply to Tamara
Tamara, you wrote about artificial intelligence "transforming" nursing. Could you name one tool on your unit that produces something a nurse sees? Once there is a specific tool, we could ask who reads its output and what it costs them.
Reply to Luis
Luis, you listed a fall risk score and a billing code suggestion together as "AI tools." A wrong fall score might leave a patient without a bed alarm; a wrong billing code costs money. Would you treat them the same way when deciding what needs watching?
References
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
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
NR 587AI Week 1 instructions, in plain terms
The first discussion asks for three or four named systems, not a position on artificial intelligence. Pick tools a colleague would recognize by sight: an early warning score in the patient list, the acuity figure behind tonight's assignments, a coding suggestion while documenting, a dose proposal. For each, say what it produces, which role receives it and what that person is expected to do within the next minute. Order them by how close their errors land to a patient. Then spend time on the one whose reader or expected action is unclear. Most rubrics mark down a post that argues about artificial intelligence in general, an inventory that never reaches a person and every automated tool treated as one category.
Inside the NR 587AI Week 1 example
The sample begins with the writer's role and the 118 beds involved. It lists a heparin dose suggestion, a sepsis alert, a deterioration index and an acuity score, giving each its output, reader and expected action. The ordering shows that a wrong dose can harm a patient within an hour, while a wrong acuity score makes a shift harder. Most of the post concerns the deterioration index: six nurses gave three different answers about who acts on a red score, and no policy says. Evidence on an externally tested sepsis model and on automation bias shows why that matters. A question for classmates and two replies close it.
NR 587AI Week 1 rubric: what full points look like
The expensive failure is a post arguing about artificial intelligence instead of reporting on a workplace; this one names four systems a nurse sees every shift. An inventory that stops at the tool leaves the week's question untouched, so every system here reaches a named role and an action. Treating a billing rule and a deterioration score as one category costs a row, and the ordering by distance from the patient separates them. Dwelling on the unclear system earns the analysis marks. Replies earn credit by pulling a classmate back to a named tool and by separating tools whose errors cost very different things. Ending on a question classmates can answer from their own units keeps the thread practical.
Common NR 587AI Week 1 mistakes, and how to avoid them
Weak NR 587AI Week 1 posts discuss whether artificial intelligence will help or harm nursing. Others list tools by brand name without saying who reads their output. Many lump every automated feature together. Few posts ask who is expected to act and whether that is written down. Replies often agree without adding anything. Walk your unit, list three or four outputs a nurse actually sees and write down the reader and the expected action for each. Then pick the one nobody owns and say why it worries you. If your systems differ, list their names and outputs for us, and we can prepare a sample post that follows the same order.
Get a NR 587AI Week 1 example written to your instructions
Send us the prompt, the rubric and anything your section published about reply expectations, and a custom NR 587AI Week 1 post is written to those exact requirements. It comes back inside 24-48h and the first one is free, so you can hold a finished inventory beside your own before anything is posted.
More NR 587AI papers and MSN samples
- NR 587AI Week 2: Tool Evaluation Brief
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- NR 587AI Week 4: Model Risk Assessment
- NR 587AI Week 5: Oversight Plan
- NR 587AI Week 6: Override Monitoring Analysis
- NR 587AI Week 7: Disclosure Brief
- NR 587AI Week 8: Deployment Decision Paper
- NR 587 Week 1: Opening Figure Post
- NR 562 Week 1: Fix That Backfired Discussion Post
- NR 534 Week 1: Discussion on a System Advantage
- NR 533 Week 1: Discussion on Owning a Budget Line
NR 587AI Week 1 questions, answered
What if my workplace has nothing that could be called artificial intelligence?
Most do, under other names. Anything producing a score, a rank, a suggested code or a proposed assignment out of data somebody else's software combined counts here, and fixed rule-based tools are worth including as the contrast case. Saying openly that a tool is a rule rather than a learned system is exactly the distinction the week rewards, provided the post then follows the output to whoever acts on it.
Can I write about a system I only see the output of?
Yes, and that is the ordinary position of the reader this course trains. You are not being asked to describe an internal mechanism. What the post needs is the part you can observe: when the output appears, what it looks like, who it interrupts, and what changes because of it. An executive reporting those four things accurately has more to work with than one repeating a supplier's description.
How much technical explanation belongs in the post?
Very little, and less than students expect. A sentence or two fixing what kind of tool it is keeps the thread honest, and everything after that should be about decisions. Classrooms differ, though the ones publishing a rubric rarely allocate points for describing machine learning, while nearly all of them allocate points for locating responsibility. Spend the words where the rubric does.
Where can I find a free NR 587AI Week 1 sample paper?
The full post naming four automated outputs on medical-surgical units, with replies to Tamara and Luis, is on this page.
Why does the deterioration index get most attention in the NR 587AI Week 1 post?
It is the only one of the four that predicts rather than calculates, and nurses could not agree on who should act when its score turns red.