EDN 818 Module 8 Ethics of Innovation Example

Course	EDN 818 Innovation and Technology in Health Care
Module	Module 8
Paper type	Innovation ethics review
Length	About 1,066 words, 6 pages
Format	APA 7 student paper
School	Aspen University
Program	Doctor of Education
Updated	September 2026

Free sample paper for EDN 818 Module 8

Who Counts the Work? An Ethical Review of an AI Acuity Tool Before It Sets Nurse Staffing

Student Name

Doctor of Education Program, Aspen University

EDN 818: Innovation and Technology in Health Care

Instructor Name

Month Day, Year

What this page is doingThe title asks the question at the center of the review, whose needs the tool will see. APA 7 student title page.

Who Counts the Work? An Ethical Review of an AI Acuity Tool Before It Sets Nurse Staffing

A vendor has offered Kingsmere Regional Hospital a machine learning tool that scores each inpatient's nursing acuity from electronic health record data every four hours and recommends nurse staffing for the next shift. The vendor reports that client hospitals matched staffing to need more closely and reduced overtime. The chief nursing officer supports a trial; the nurses' practice council is wary. Before any decision, the executive team asked for an ethical review. This paper examines the tool using the Code of Ethics of the American College of Healthcare Executives, which the course names, together with research on the ethical problems that machine learning raises when it enters clinical care.

The Professional Code

The ACHE code describes health care executives' responsibilities to the profession, to patients and others served, to the organization, to employees and to the community and society (American College of Healthcare Executives, n.d.). Among other duties it asks executives to protect patients' rights and safety, to ensure that resources are used fairly, to create a working environment that supports staff and their concerns, to avoid conflicts of interest and to be honest in communication. These responsibilities provide the frame for the review, because an acuity tool touches each of them.

What this page is doingGrounding the review in the course's named code first makes the later judgments traceable to professional duties.

Ethical Challenges of Machine Learning

Char et al. (2018) identified ethical challenges that arise when machine learning enters clinical care: algorithms can reproduce biases in the data used to train them; the goals of the people who design and sell them may differ from those of clinicians and patients; clinicians may come to rely on outputs they cannot inspect; and the introduction of a third party into care decisions changes responsibility and trust. Each challenge applies to a staffing tool, whose output determines how much nursing care each patient can receive.

The Proxy Problem

The most serious risk lies in what the tool actually measures. Obermeyer et al. (2019) examined a widely used commercial algorithm that identified patients for extra care management. Because it predicted future health care costs rather than illness, and less money is spent caring for Black patients than for equally sick White patients, Black patients at a given risk score were considerably sicker than White patients; fixing the bias would have lifted Black patients' share of the group chosen for extra help to 46.5%, up from 17.7%. An acuity tool faces a parallel risk. If it infers nursing need from documented tasks and orders, patients whose needs are poorly documented, such as those who need interpreters, those with dementia who cannot ask for help, or those whose care is time-consuming but routine, may be undercounted, and the units that care for them understaffed.

The Review in Summary

The table sets out the ethical questions, what the review found and the conditions the team attached.

ACHE responsibility	Ethical question	Finding	Condition before use
To patients	Does the tool see all patients' needs equally?	Vendor could not show performance by language, age or cognitive status	Local validation by patient group before use
To patients	Who is accountable if staffing proves unsafe?	Contract assigns no responsibility to the vendor	Nurse leaders keep final authority; tool advises only
To employees	Will nurses understand and be able to challenge scores?	Scores are shown without reasons	Explanation of main factors for each score; override without penalty
To employees	Could the tool be used to cut staffing?	Vendor marketing emphasizes labor savings	Written commitment that the tool cannot lower staffing below existing minimums
To the organization	Are there conflicts of interest?	Vendor offered a discount for a case study	Decline the discount; independent evaluation
To the community	Is the tool's use disclosed?	No plan for disclosure	Tell patients and staff how staffing decisions are made

Local Validation

Vendor performance data from other hospitals do not show how a tool will perform with Kingsmere's patients, documentation habits and units. The review requires a silent trial: for three months the tool will score patients without influencing staffing, while charge nurses independently rate acuity with the hospital's existing tool. The team will compare the two by unit, patient age, language, cognitive status and race and ethnicity, and will examine any group for which the tool consistently scores lower than nurses do. Only if the tool performs fairly across groups will it move to an advisory role.

Transparency and the Nurse's Judgment

A score that cannot be explained cannot be challenged. The vendor must display the main factors behind each patient's score, and charge nurses must be able to override the recommendation with a brief reason, without needing supervisor approval. Overrides will be reviewed monthly, not to discipline nurses but to learn where the tool misses. Keeping nurse judgment in the loop addresses the risk that clinicians will defer to outputs they do not understand.

Workforce and Trust

Nurses' wariness is reasonable. Staffing tools have been used elsewhere to justify reductions, and the vendor's own marketing emphasizes savings. The ACHE code's responsibilities to employees require the executive team to address this directly. The hospital will put in writing that the tool will not be used to lower staffing below current minimums, will involve the nurses' practice council in the evaluation and will share the validation results with all nursing staff.

Recommendation

The review recommends against adopting the tool on the vendor's current terms, and in favor of a conditional trial if the vendor agrees to the conditions in the table: a three-month silent trial with validation across patient groups, explanations for each score, nurse override without penalty, no use to reduce staffing below current minimums, clear accountability in the contract and independent evaluation without a discounted case study. If the vendor declines, Kingsmere should continue with its existing acuity process and revisit the question when tools with published, independent validation are available.

Conclusion

An AI acuity tool could help Kingsmere match nursing care to patients' needs, but it could also hide some patients' needs behind a score that no one can question. The ACHE code's responsibilities to patients, employees, the organization and the community, and research showing how algorithms can encode bias through the proxies they use, together require local validation, transparency, preserved nurse judgment and protections for staff before any use. Innovation that meets those conditions deserves a trial; innovation that cannot meet them does not belong in decisions about who receives care.

What this page is doingThe conclusion links the recommendation to both sources of the review, the professional code and the research on bias.

References

American College of Healthcare Executives. (n.d.). ACHE code of ethics. https://www.ache.org/about-ache/our-story/our-commitments/ethics/ache-code-of-ethics

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care: Addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1714229

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

What the EDN 818 Module 8 instructions ask for

The ACHE Code of Ethics is named in Aspen's EDN 818 description, and since the last module's directions are available only to registered students, this example applies the code to an innovation the hospital is considering. The ethics assignment here generally calls for judging an innovation or technology against professional standards and recommending how leaders should proceed. Name the code's specific responsibilities rather than general principles. Use research on the risks of the technology, not only hypothetical concerns. Identify who could be harmed and how, including groups whose needs may be invisible to the technology. Separate what must be true before use from what can be monitored afterward. End with a recommendation, including conditions, that a leadership team could act on.

How this EDN 818 Module 8 example is built

The review opens with a vendor's offer, the chief nursing officer's support and the nurses' practice council's wariness. It sets out the ACHE code's responsibilities, then summarizes ethical challenges of machine learning, including biased training data, differing motives of designers and over-reliance on outputs. The proxy problem receives its own section: a commercial algorithm that predicted cost rather than illness and so underserved Black patients, and the parallel risk that an acuity tool would undercount patients whose needs are poorly documented. A four-column table links each ACHE responsibility to questions, findings and conditions before use. Sections on a three-month silent trial, explainable scores with nurse override, a written staffing commitment and a conditional recommendation complete the paper.

Reading the EDN 818 Module 8 grading rubric

Ethics reviews are graded on correct use of the professional code, grounded analysis of risks, attention to affected groups and a practical recommendation. This example maps each finding to a named responsibility in the ACHE code, which shows the code in use rather than quoted. It cites three APA sources: the ACHE Code of Ethics, a New England Journal of Medicine article on ethical challenges of machine learning and a Science study that exposed racial bias in a care-management algorithm. Translating that study's proxy problem to a staffing tool is the paper's key analytical move. Conditions such as a silent trial, validation across patient groups and preserved nurse judgment show that the review leads to action, which instructors value over a list of concerns.

Common EDN 818 Module 8 mistakes, and how to avoid them

Ethics papers often list principles such as autonomy and justice without applying them to the specific technology. Tie each concern to a responsibility in the named code and to a feature of the technology. Another weakness is relying on hypothetical harms when real studies exist; find research on similar tools. Ask whose needs the technology can see and whose it might miss. Distinguish conditions required before use from issues to monitor. Consider staff as well as patients, since workforce effects are ethical questions too. Avoid ending with a vague call for caution; say what should happen and under what conditions. If connecting a professional code to a technology is difficult, a tutor can help you build a table linking the two.

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 Aspen 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.

More EDN 818 and Doctor of Education sample papers

EDN 818 Module 8 questions, answered

What does EDN 818 Module 8 usually ask for?

Aspen's EDN 818 says leaders are accountable to the ACHE Code of Ethics, so an ethical analysis of an innovation or technology using that code is a typical final assignment. Follow your classroom prompt.

How can an algorithm be biased if it does not use race?

It can predict a proxy, such as cost or documented tasks, that differs systematically between groups, so patients with equal needs receive different scores.

What is a silent trial of a clinical algorithm?

A period in which the tool runs and records its outputs without affecting care, so its performance can be compared with clinicians' judgments before it influences decisions.

Where can I find a free EDN 818 Module 8 sample paper?

The full review is above, examining an AI acuity tool against the ACHE Code of Ethics with a table of questions, findings and conditions before use.

What is the ACHE Code of Ethics?

The American College of Healthcare Executives' code describing executives' responsibilities to the profession, to patients, to their organizations, to employees and to the community and society.

EDN 818 Module 8 Ethics of a Health Care Innovation Example