| Course | HIM 550 Data Management and Data Quality |
|---|---|
| Module | Module 1 |
| Paper type | graduate discussion post on data quality as fitness for use |
| Length | About 340 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 1
Module One Discussion
Accurate for Billing, Wrong for the Dashboard
Cypress Hollow Medical Center's leaders want 40% of patients discharged before noon, and every morning they look at a dashboard that says we are at 29%. Last month I traced the number back to its source. The discharge time the dashboard uses is the moment a unit clerk closes the encounter in the registration system, which triggers billing. On our medical units, clerks often close encounters after lunch, so a patient who left at 10:30 is recorded as discharged at 1:45. The field is accurate for billing, since the encounter really did close then. It is wrong for measuring when patients leave.
This is what data quality as fitness for use means. Wang and Strong (1996) surveyed data consumers and found that they judged quality along several dimensions, not only accuracy but also relevance, timeliness, interpretability and accessibility, and that data were high quality only when they suited the consumer's task. Kahn et al. (2016) later proposed shared terms for judging clinical data, conformance, completeness and plausibility, and emphasized that quality assessment starts from the intended use. Our discharge time passes conformance and completeness checks; it is a valid time and it is always present. It fails the test of fitness for the dashboard's purpose.
The measure matters because hospitals act on it. Wertheimer et al. (2014) described a medical service that lifted morning discharges from 11% to 38% using multidisciplinary rounds, daily feedback and staff engagement, with observed-to-expected length of stay falling at the same time. A hospital pursuing that kind of result needs a discharge time that captures when patients actually leave. The nursing documentation already records a departure time; the dashboard simply was never built to use it.
I have asked our analytics team to rebuild the measure from the nursing departure time and to report both numbers for a month. For classmates: can you name a field in your work that is accurate for the purpose it was designed for but misleading when reused for something else?
References
Kahn, M. G., Callahan, T. J., Barnard, J., Bauck, A. E., Brown, J., Davidson, B. N., Estiri, H., Goerg, C., Holve, E., Johnson, S. G., Liaw, S.-T., Hamilton-Lopez, M., Meeker, D., Ong, T. C., Ryan, P., Shang, N., Weiskopf, N. G., Weng, C., Zozus, M. N., & Schilling, L. (2016). A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. eGEMs, 4(1), Article 1244. https://doi.org/10.13063/2327-9214.1244
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099
Wertheimer, B., Jacobs, R. E. A., Bailey, M., Holstein, S., Chatfield, S., Ohta, B., Horrocks, A., & Hochman, K. (2014). Discharge before noon: An achievable hospital goal. Journal of Hospital Medicine, 9(4), 210-214. https://doi.org/10.1002/jhm.2154
What the HIM 550 Module 1 instructions ask for
The first HIM 550 discussion asks what makes data good, which usually means explaining data quality as fitness for use. Aim for about a page with two or three scholarly sources in APA 7, then respond to classmates with examples of your own. The strongest approach follows one real or realistic field from its source to a report and shows that it can be correct for one purpose and misleading for another. Name the quality dimensions involved, such as accuracy, timeliness and relevance, and connect them to a published framework. Explain why the purpose matters, propose a practical fix and close by asking classmates to share a field that behaves the same way in their own work.
How this HIM 550 Module 1 discussion example is built
Cypress Hollow Medical Center's discharge-before-noon dashboard reads 29% against a 40% target, but its discharge time is when a clerk closes the encounter for billing. The post uses Wang and Strong's finding that data consumers judge quality by fitness for their task and Kahn and colleagues' shared quality terms to show that the field passes conformance and completeness yet fails the dashboard's purpose. Wertheimer and colleagues' discharge-before-noon results show why the measure matters. The writer asks analytics to rebuild the measure from the nursing departure time and invites HIM 550 classmates to name similar fields. Both versions of the number will run side by side for a month.
Where the HIM 550 Module 1 rubric puts the points
Opening posts in HIM 550 tend to be graded on a clear explanation of data quality as fitness for use, a concrete example traced to its source, correct use of quality dimensions, relevant research, a practical fix and engagement with peers, plus APA 7 citations. Posts that stand out show that accuracy alone is not quality and name the specific dimension that fails. Graders reward writers who trace a number back to the field and workflow that produce it, since that habit prevents many analytic errors later in the course. Replies that offer a different field and purpose extend the discussion well and show independent thinking.
HIM 550 Module 1 help: the mistakes that cost points
First posts in HIM 550 lose ground when they define data quality as accuracy alone, give examples without tracing them to a source field, list dimensions without applying them or cite frameworks without explaining them. Some drafts also propose fixes that clean the report rather than the data. Instructors sometimes name a framework, perhaps a maturity model or their own list of dimensions; pass that along and the post will be built on it. Mention your work setting if it helps, along with the reports you use most. HIM 550 posts we write follow one field from source to report, name the failing dimension, bring in research, propose a fix and ask a question.
Get HIM 550 Module 1 written to your instructions
Send the HIM 550 Module 1 discussion prompt and, if you have one, a data field from your workplace. The post will trace it from source to report, explain fitness for use with research, name the failing quality dimension and propose a fix with a question for classmates, ready in 24 to 48 hours, free on a first request. 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 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 550 Module 6 Journal: The Ethics of Secondary Use
- HIM 550 Module 7 Final Project Milestone Three: Data Management Recommendations
- HIM 550 Module 8 Visualization Short Paper: Dashboards That Inform Rather Than Mislead
- HIM 550 Module 9 Final Project: The Data Management Plan
- HIM 550 Module 10 Reflection: What Managing Data Taught the Writer
- HIM 540 Module 9 Final Project: The Information Governance Program
- HIM 520 Module 1 Discussion: Leadership Styles and What They Change in an HIM Team
- HIM 510 Module 1 Discussion: Professional Identity and the Roles of HIM Leaders
- HIM 500 Module 9 Final Project: The Health IT Recommendations Report
HIM 550 Module 1 questions, answered
Where can I find a free HIM 550 Module 1 Discussion sample?
The complete HIM 550 Module 1 post is on this page: a discharge time that is right for billing and wrong for operations, and what fitness for use means.
What does fitness for use mean in data quality?
Data are high quality only when they suit the purpose they are used for, which can differ from the purpose they were collected for.
Can accurate data still be poor quality?
Yes. A value can be accurate for one purpose, such as billing, yet untimely or irrelevant for another, such as tracking discharge times.
What are common data quality dimensions?
Accuracy, completeness, timeliness, relevance, consistency and interpretability, or conformance, completeness and plausibility in one clinical framework.
Why trace a dashboard number back to its source field?
It reveals how the data are created and whether the field actually measures what the dashboard claims.