| Course | DNP 715 Dynamics and Principles of Information in Healthcare |
|---|---|
| Module | Module 1 |
| Paper type | Discussion board post and replies |
| Length | About 374 words, 4 pages |
| Format | APA 7 student paper |
| School | Arizona State University |
| Program | Doctor of Nursing Practice |
| Updated | October 2026 |
Free sample paper for DNP 715 Module 1
When the Algorithm Cries Wolf: What a Sepsis Prediction Model Teaches Nurses About AI
Student Name
Edson College of Nursing and Health Innovation, Arizona State University
DNP 715: Dynamics and Principles of Information in Healthcare
Instructor Name
Month Day, Year
When the Algorithm Cries Wolf: What a Sepsis Prediction Model Teaches Nurses About AI
Initial Post
My hospital's informatics committee is deciding whether to switch on a vendor sepsis alert built into our electronic health record. Before that vote, I read an external validation of a similar proprietary model at a large academic center. In more than 38,000 hospitalizations, the model separated septic from nonseptic patients only modestly, with an area under the curve of 0.63, far below the vendor's reported performance. Roughly two in three sepsis cases went unflagged, while nearly one patient in five on the wards set off an alert at some point (Wong et al., 2021).
For nurses, both numbers matter. Missed cases mean that an alert cannot replace bedside assessment; if staff come to trust the alert, they may wait for it. The alert volume matters because every alert takes a nurse's attention. Alarm fatigue, in which clinicians become desensitized after exposure to many alarms, is a known safety problem with monitors (Sendelbach & Funk, 2013), and AI alerts can add to that burden.
Artificial intelligence may still help. Large reviews describe real promise in imaging and prediction, while warning that much of the evidence comes from retrospective studies and that tools need prospective testing in real clinical settings (Topol, 2019). The lesson is not to reject AI but to demand local evidence.
For advanced practice nurses, I see three roles: asking for external validation before deployment, measuring local performance after go-live, including how many alerts lead to action, and making sure frontline nurses can report when an alert is wrong. Question for the group: who in your organization decides whether an AI tool is performing well enough to stay on?
Reply to a Classmate (Nurse Practitioner, Primary Care)
You described an AI scribe that drafts visit notes. I agree that it could reduce documentation time. Who checks the drafts for errors, and has your clinic measured how often the AI adds something the patient never said?
Reply to a Classmate (Nurse Educator)
Your point that new graduates may trust alerts more than experienced nurses is important. Should orientation include training on how prediction models work and where they fail, the way we teach the limits of pulse oximetry?
References
Sendelbach, S., & Funk, M. (2013). Alarm fatigue: A patient safety concern. AACN Advanced Critical Care, 24(4), 378-386. https://doi.org/10.1097/NCI.0b013e3182a903f9
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
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
DNP 715 Module 1 instructions, in plain terms
Two written discussion boards share 90 points in the posted syllabus. Their scope is emerging health technology and artificial intelligence, plus the safety and quality problems that technology creates or changes, with attention to the impact on healthcare delivery, nursing practice, advanced nursing practice, patient outcomes or system outcomes. Faculty post the prompts in Canvas, usually a week ahead. Expect to write an initial post grounded in evidence and to reply to classmates. The course also includes an AI module, so connect the discussion to what you learn there. Choose a technology you have seen or will see in practice, since real examples make the discussion stronger. Use peer-reviewed sources rather than vendor websites when describing how a technology performs, since faculty weigh the quality of evidence in each post.
How the DNP 715 Module 1 example is put together
The post starts from a decision the writer's hospital actually faces, which gives the discussion purpose. It reports the key numbers from an external validation precisely and explains what each means for nurses. A second source links alert volume to a known safety problem, and a third places the example within the wider evidence on AI. The post ends with concrete roles for advanced practice nurses and a question for the group. Replies ask specific questions about classmates' technologies, extending the discussion to documentation AI and education rather than repeating the initial post. Each number in the post comes from a named study, and the writer explains in plain words what those numbers would mean for a nurse on shift.
Where the marks sit in the DNP 715 Module 1 rubric
Faculty grade a technology post on sound evidence, a clear account of what the tool does to care and to nurses' work, consideration of safety and quality, application to advanced practice roles, substantive replies, and scholarly writing. Faculty look for posts that weigh benefits and risks rather than promoting or dismissing a technology. Reporting performance measures correctly shows informatics literacy. Linking the discussion to governance, who decides and how, shows leadership thinking. Replies that ask about measurement and oversight move the dialogue forward. Clear references to peer-reviewed sources, not vendor material, earn credit. Posts that name who should govern a technology, and how its performance should be checked after go-live, show the advanced practice perspective faculty want.
DNP 715 Module 1 help from the desk
Posts on AI often stay general: "AI will transform health care." Pick one tool and look for evidence of how it performs outside the setting where it was built. Another weakness is ignoring the nurse's workload; ask what every alert or output costs in attention. Define terms such as sensitivity or area under the curve briefly. To get help with the AI post, share the faculty prompt with the desk. Look for an external validation study, not only the developer's report, and note who funded each study you cite. Read the course AI module before writing, so your post uses the same terms your classmates will.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Arizona State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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DNP 715 Module 1 questions, answered
Where can I find a free DNP 715 Module 1 sample paper?
The technology and AI discussion above is complete: a sepsis prediction model's external validation analyzed for its meaning to nurses and advanced practice governance, with two replies.
How many discussion boards are in DNP 715?
Two, worth a combined 90 points.
What topics do the DNP 715 discussion boards cover?
Emerging health technology and artificial intelligence, and patient safety and quality issues influenced by technology.
What is external validation of an AI model?
Testing a model's performance on data from a different setting or population than the one used to build it.
How can AI alerts affect nurses?
Missed cases can create false reassurance, and frequent alerts can add to alarm fatigue.