| Course | HIM 550 Data Management and Data Quality |
|---|---|
| Module | Module 6 |
| Paper type | graduate reflective journal on the ethics of reusing hospital data |
| Length | About 370 words, 3 pages |
| Format | APA 7 student paper |
| School | Southern New Hampshire University |
| Program | MS Health Information Management |
| Updated | September 2026 |
Free sample paper for HIM 550 Module 6
Module Six Journal
Two Requests in One Week
Two data requests reached my desk this week, and both made me stop. A research team at a nearby university asked for our discharge data set for a study of transfers to skilled nursing facilities. They offered to accept it without names or record numbers, but they wanted admission and discharge dates, ages and five-digit ZIP codes. Then our operations group asked whether we could build a model that predicts which patients will leave late so case managers can start earlier.
The research request looked harmless until I thought about who could be identified. A patient in a small parish ZIP code, age 94, discharged on a particular date to a named facility, might be one of a handful of people. El Emam et al. (2011) reviewed re-identification attacks and found that most successful ones used data that had not been properly de-identified under existing standards. Leaving out names is not the same as de-identifying. I have asked our privacy officer to route the request through our review board and to offer either a limited data set under a data use agreement or a version with dates shifted and ZIP codes shortened.
The internal request troubled me more. Operations is allowed to use our data to run the hospital, so no rule stops it. Price and Cohen (2019) argue that privacy concerns in large health data extend beyond what the rules cover. Among the fields proposed for the model are admission source, payer and ZIP code. Obermeyer et al. (2019) found that a common population health algorithm gave Black patients lower risk scores at the same level of illness, since it forecast spending and spending follows access to care. A model that flags patients from certain neighborhoods as slow to leave could lead staff to treat them differently, even though the delay often comes from a shortage of nursing home beds, not from the patient.
I told operations I would help if the model's purpose is to start planning earlier and its results are checked across payer and neighborhood groups before anyone uses it. What still feels unresolved is who should decide such questions when the law allows a use but fairness is uncertain.
References
El Emam, K., Jonker, E., Arbuckle, L., & Malin, B. (2011). A systematic review of re-identification attacks on health data. PLoS ONE, 6(12), Article e28071. https://doi.org/10.1371/journal.pone.0028071
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
Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0272-7
What the HIM 550 Module 6 instructions ask for
The HIM 550 Module 6 journal asks you to reflect on the ethics of secondary data use, meaning any use of patient data beyond the purpose it was collected for. The entry is personal and fairly short, about a page, with a few sources in APA 7 that help you think the question through. Ground it in a real or realistic situation, such as a research request, an internal analytics project or a vendor proposal. Consider privacy and re-identification, whether patients would expect the use, fairness when data reflect unequal access to care and who should decide. Describe what you would do, explain why and be honest about which parts of the question still seem unsettled.
How this HIM 550 Module 6 journal example is built
The Cypress Hollow data manager weighs a university request for a discharge extract with dates, ages and five-digit ZIP codes and an operations idea to predict late departures. El Emam and colleagues' review of re-identification attacks shows that dropping names is not de-identification, so the request goes to review with a limited data set or shifted dates offered. Price and Cohen's point that privacy concerns extend beyond the rules and Obermeyer and colleagues' finding on biased proxies shape conditions for the model: an early-planning purpose and checks across payer and neighborhood groups. The HIM 550 journal closes on who decides when a use is lawful yet uncertain, and it leaves that question open on purpose.
Where the HIM 550 Module 6 rubric puts the points
Ethics journals in HIM 550 tend to be graded on a concrete situation, accurate understanding of privacy and de-identification, recognition of fairness concerns in data and models, sources used to reason rather than to decorate, a clear decision with its reasons and honest reflection on uncertainty. Journals that stand out distinguish between what the law allows and what is right, and they notice that internal uses can raise harder questions than outside requests. Graders reward practical conditions attached to a decision, such as review steps or fairness checks, because they show ethics turned into management action. A named reviewer or committee strengthens the plan. Candor about an open question often earns more credit than false certainty.
HIM 550 Module 6 help: the mistakes that cost points
HIM 550 ethics journals fall short when they restate HIPAA rules without applying them, treat removing names as de-identification, overlook bias in internal models or end without a decision. Some also moralize without a real situation. If your prompt focuses elsewhere, such as selling data, patient portals, research consent or artificial intelligence in coding, send it along with a situation from your work and the journal will reflect on that case in the same personal, careful way. Your role and the kinds of requests you see help shape the entry. HIM 550 journals we write start from real requests, weigh privacy and fairness and end with a decision and a question.
Get HIM 550 Module 6 written to your instructions
Send the HIM 550 Module 6 journal prompt and a data request or project you have seen at work. The entry will weigh privacy, re-identification and fairness with a few well-chosen sources, describe a reasoned decision with conditions and reflect honestly on what remains unsettled, delivered in 24 to 48 hours, first request free. The paper above is an original model document written by our desk, not a submitted student paper and not an official Southern New Hampshire University document.
More HIM 550 papers and related MS Health Information Management samples
- HIM 550 Module 1 Discussion: What Fitness for Use Means for Hospital Data
- HIM 550 Module 2 Journal: Following One Data Element Through Its Life Cycle
- HIM 550 Module 3 Final Project Milestone One: A Data Quality Assessment of the Discharge Data Set
- HIM 550 Module 4 Warehousing Short Paper: Warehouses, Metadata and the Nightly Load
- HIM 550 Module 5 Final Project Milestone Two: Analyzing Discharge Timing and Length of Stay
- HIM 540 Module 10 Reflection: What Governing Information Taught the Writer
- HIM 520 Module 6 Ethics Short Paper: A Physician's Request to Change a Note After an Adverse Event
- HIM 500 Module 7 Final Project Milestone Three: Technology Recommendations for the Hospital
- HIM 510 Module 8 Trends Short Paper: Price Transparency, Prior Authorization and AI Coding
HIM 550 Module 6 questions, answered
Where can I find a free HIM 550 Module 6 Journal sample?
The complete HIM 550 Module 6 journal is on this page: two data requests in one week and the ethics of reusing patient data beyond its purpose.
What is secondary use of health data?
Using data for a purpose other than the one it was collected for, such as research, quality improvement, operations analytics or commercial uses.
Is removing names enough to de-identify data?
No. Dates, ages, ZIP codes and rare details can identify people, so recognized de-identification methods or data use agreements are needed.
How can a predictive model be unfair even if it is accurate?
If it relies on data shaped by unequal access to care, its predictions can lead staff to treat some groups differently.
What is a limited data set?
A HIPAA-defined data set with direct identifiers removed but some dates and geography kept, shared only under a data use agreement.