HIM 220 Module 1 Discussion Example

Reviewed by Delia Ravenscroft, MSN, RN

This HIM 220 Module 1 Discussion sample begins the data management course with a puzzle many hospitals know well. It is written for SNHU HIM 220 (HIM-220), where BS Health Information Management students start by considering why trustworthy data matter and what makes data trustworthy. The writer, a composite data integrity analyst at a system with two hospitals and eleven clinics, reports that the quality dashboard shows a 30-day readmission rate of 14.2% while the finance dashboard shows 11.6%, and both teams insist their number is right. Tracing the gap reveals different definitions rather than bad arithmetic. Data quality frameworks and research on how observation stays affect readmission counts explain the problem, and the post asks classmates whether they have seen conflicting reports.

CourseHIM 220 Healthcare Data Management
ModuleModule 1
Paper typeBS Health Information Management discussion post on data quality and definitions
LengthAbout 360 words, 3 pages
FormatAPA 7 student paper
SchoolSouthern New Hampshire University
ProgramBS Health Information Management
UpdatedSeptember 2026

Free sample paper for HIM 220 Module 1

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Module One Discussion

Same Hospital, Same Month, Two Different Readmission Rates

At last month's leadership meeting at Kettle River Health, the quality director reported a 30-day readmission rate of 14.2%. Twenty minutes later, the finance director showed 11.6% for the same hospitals and months. Both used data from the same electronic record. I was asked to find out which one was wrong. The answer turned out to be that neither was wrong; they were measuring different things.

What this page is doingThe writer introduces the conflicting dashboards.
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Quality counted any return to either of our hospitals within 30 days, including observation stays and planned readmissions for chemotherapy. Finance counted only inpatient readmissions to the same hospital and excluded planned returns. Zuckerman et al. (2016) showed why such choices matter: after Medicare's readmissions penalty began, readmissions fell while observation stays rose, so whether observation stays are counted can change the story a dashboard tells. Our two teams were not disagreeing about patients; they were disagreeing about definitions nobody had written down.

Kahn et al. (2016) offer a vocabulary for this problem. Their harmonized framework asks whether data conform to agreed definitions and formats, whether they are complete and whether values are plausible, and it stresses that quality depends on fitness for a particular use. Weiskopf and Weng (2013) list five dimensions in their review, among them concordance, meaning agreement between sources, and currency, meaning how up to date the data are. Our readmission data were complete and correct; they failed on conformance, because each team applied its own rules, and on concordance, because the two sources could not be reconciled without knowing those rules.

What this page is doingResearch explains why definitions drive the discrepancy.
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The fix is a shared data dictionary with one definition for each purpose, clearly labeled. Medicare's penalty program uses its own rules, our quality team needs a broader count to find patients who bounce back to any of our sites and finance needs something closer to what payers measure. All three can coexist if each dashboard names the definition it uses, and if leaders stop comparing numbers built on different rules as though they measured the same thing. For classmates: have you seen two reports of the same measure disagree at work? What turned out to be the cause?

What this page is doingThe writer proposes a fix and asks peers about their experience.
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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 18. https://doi.org/10.13063/2327-9214.1244

Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. https://doi.org/10.1136/amiajnl-2011-000681

Zuckerman, R. B., Sheingold, S. H., Orav, E. J., Ruhter, J., & Epstein, A. M. (2016). Readmissions, observation, and the Hospital Readmissions Reduction Program. New England Journal of Medicine, 374(16), 1543-1551. https://doi.org/10.1056/NEJMsa1513024

What the HIM 220 Module 1 instructions ask for

The first HIM 220 discussion typically asks why data quality matters in healthcare and how poor quality shows up in practice. An opening entry near 350 words citing two or three peer-reviewed studies in APA 7 fits most versions, with peer replies later. Use a concrete example, such as a conflicting report, a duplicate record or missing data, and explain it with a recognized framework rather than general statements. Show that you can tell the difference between wrong data and data defined differently, and invite classmates to share similar experiences. HIM 220 graders notice clean headings in HIM 220 papers. HIM 220 names and dates need checking before HIM 220 submission. HIM 220 prompts vary by term, so recheck HIM 220 directions.

How this HIM 220 Module 1 discussion example is built

A data integrity analyst describes quality and finance dashboards reporting 14.2% and 11.6% readmission rates. Tracing the gap shows quality counted observation stays, planned returns and either hospital, while finance counted only unplanned inpatient returns to the same hospital. Zuckerman and colleagues' finding that observation stays rose as readmissions fell shows why the choice matters. Kahn and colleagues' conformance, completeness and plausibility categories and Weiskopf and Weng's dimensions name the failure, and the post proposes a shared data dictionary before asking classmates about conflicting reports. HIM 220 students can reuse this structure for HIM 220 work. HIM 220 claims here trace to cited HIM 220 sources. HIM 220 readers can adapt each section to HIM 220 data.

Where the HIM 220 Module 1 rubric puts the points

Opening data discussions in HIM 220 are generally judged on the relevance of the example, correct use of data quality concepts, accurate citation of research, clear reasoning and engagement with peers. Posts score well when they name specific quality dimensions, explain how definitions shape results and propose a practical remedy such as a data dictionary. A question that asks classmates to diagnose a cause, not just share a complaint, encourages useful replies. HIM 220 marks favor careful formatting across HIM 220 sections. HIM 220 citations keep every HIM 220 argument credible. HIM 220 instructors weigh evidence heavily in HIM 220 grading.

HIM 220 Module 1 help: the mistakes that cost points

Data quality posts lose points when they describe bad data vaguely, blame staff without examining definitions or processes, cite frameworks without applying them or offer no remedy. Another frequent gap is assuming one number must be wrong when both may be correctly calculated under different rules. Use a concrete case, apply named dimensions, explain definitions and propose a fix. If your prompt asks about a different data problem, such as duplicate records or missing demographics, send it with your HIM 220 notes. HIM 220 drafts start well from a HIM 220 outline. HIM 220 feedback already received guides HIM 220 revisions. HIM 220 rubrics posted in Brightspace clarify HIM 220 expectations.

Get HIM 220 Module 1 written to your instructions

Share the HIM 220 Module 1 prompt and a data problem you have seen. The post will explain it with recognized data quality dimensions, show whether definitions or errors are to blame, cite research and propose a practical fix, within 24 to 48 hours, free the first time. 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.

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HIM 220 Module 1 questions, answered

Where can I find a free HIM 220 Module 1 Discussion sample?

The full HIM 220 Module 1 post is here: two dashboards show different readmission rates, and data quality frameworks explain why.

Why do readmission rates differ between reports?

Reports may differ in whether they count observation stays, planned returns, other hospitals or transfers.

What are the dimensions of data quality?

Weiskopf and Weng describe completeness, correctness, concordance, plausibility and currency; Kahn and colleagues group checks as conformance, completeness and plausibility.

What does fitness for use mean?

Data can be good enough for one purpose and not another, so quality must be judged against the intended use.

How can organizations prevent conflicting reports?

By maintaining a shared data dictionary that defines each measure, its inclusions and exclusions and its source.