DDHA 9902 Week 1 Analysis Choice Post Example

Practice-Based Problem Analysis, Findings, and Recommendations Walden University Free custom sample in 24 to 48h

This DDHA 9902 Week 1 discussion post example asks which analysis is worth running once a practice problem is settled: here, summaries from a composite two-hospital system reaching outside primary care practices late about half the time. Walden DDHA 9902, the course where DHA students analyze a problem and recommend action, opens with that choice. The post restates the problem in one sentence, citing Kripalani and colleagues for the wider pattern, proposes partitioning late summaries by failure point and then applies a filter most posts skip: it lists four results the partition could return and the different recommendation each would lead to. Because the results diverge, the analysis passes. A practice satisfaction survey fails, since every result leads to the same action. Nosek and colleagues support writing the results down first. Replies apply the filter to a classmate's overtime plan and flag a data definition risk, citing Kahn and colleagues.

CourseDDHA 9902 Practice-Based Problem Analysis, Findings, and Recommendations
WeekWeek 1
Paper typeDoctoral discussion post with two replies
LengthAbout 580 words, 4 pages
FormatDiscussion post, APA 7 citations
SchoolWalden University
ProgramDoctor of Healthcare Administration (DHA)
UpdatedOctober 2026

Free sample paper for DDHA 9902 Week 1

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Week 1 Discussion: Choosing an Analysis That Could Change a Decision

An Analysis Worth Running Is One Whose Answer Would Change the Recommendation: Testing a Failure-Point Partition Before Doing It

The problem was settled last term: at Larchmont Health, the invented two-hospital system from last term, roughly one hospital discharge summary in two reaches an outside primary care practice within the two business days the system has promised its accountable care partners. I do not need to prove that again, and the published pattern behind it, described by Kripalani et al. (2007), is not in dispute. The question now is which analysis would be worth performing.

The candidate I propose is a partition of the late summaries by the point at which each one failed. Every untimely summary would be assigned to one of five categories: the registration record named no clinician or the wrong one; the practice had no secure address and the fax failed or was never confirmed; the hospitalist signed after the two-day window; the secure message went to an address the practice no longer used; or the cause could not be determined. The partition would compare the size of these groups.

The filter is to write down, before doing anything, the results the partition could plausibly return and the recommendation each would lead to. If most failures sit at registration, the recommendation is to verify the primary care field at admission and again before discharge. If most sit on the fax path, it is to register the remaining practices for secure messaging and retire faxing for them. If most come from late signing, it is to change hospitalist workflow, perhaps for Friday discharges. If the causes are spread evenly, the recommendation is a daily worklist of failed transmissions that someone owns. Four results, four different recommendations: the analysis passes.

I considered and rejected a second analysis, a satisfaction survey of practices. Whatever it found, the recommendation would be the same, to deliver summaries on time, so it would add cost without changing a decision. Nosek et al. (2018) argue that committing to an analysis before seeing the results protects against shaping findings after the fact, and writing the possible results and their consequences down first is a modest version of that discipline. Has anyone else had an analysis on their list fail this filter?

What this page is doingThe post restates the settled problem in one sentence, proposes one analysis, maps each plausible result to a different recommendation and rejects a rival that fails the test.
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Reply to Halvard

Halvard, your proposed analysis compares overtime hours by unit, and you listed three possible results. I think two of them lead to the same recommendation, more float nurses, which suggests the comparison by unit may not be the one that matters. If the decision is about float pool size, the analysis that could change it might be overtime by shift and day of week, since a float pool helps more with predictable peaks than with one chronically short unit. Would a partition by time rather than by place give you results that point in different directions?

What this page is doingThe reply applies the same filter to a classmate's plan and suggests an analysis whose results would diverge.
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Reply to Nadia

Nadia, I like your plan to compare missed medication reconciliations between pharmacist-led and nurse-led discharges, because the two possible results clearly lead to different staffing recommendations. One caution from my own work: the records that show who performed reconciliation may not be complete. Kahn et al. (2016) treat agreement between what a field claims to record and what it holds as a basic quality check, and a field recording "reconciled by" may sometimes record who clicked the button rather than who did the work. A small chart review before the full analysis would tell you whether the comparison can be trusted.

What this page is doingSupporting the classmate's choice while flagging a data definition risk adds something she can act on before analysis begins.
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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

Kripalani, S., LeFevre, F., Phillips, C. O., Williams, M. V., Basaviah, P., & Baker, D. W. (2007). Deficits in communication and information transfer between hospital-based and primary care physicians: Implications for patient safety and continuity of care. JAMA, 297(8), 831-841. https://doi.org/10.1001/jama.297.8.831

Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600-2606. https://doi.org/10.1073/pnas.1708274114

DDHA 9902 Week 1 instructions, in plain terms

The opening thread of the analysis course usually asks which analysis you would perform on the problem you established earlier. Restate the settled problem in a sentence without re-arguing it. Propose one candidate analysis and say exactly what it would compare or partition. Then do the essential work: list the two to four results the analysis could plausibly return and write, beside each, the recommendation that would follow. If every possible result leads to the same recommendation, the analysis is not worth performing, and you should say so and choose another. Mention an alternative you rejected and why. Keep it short. In replies, apply the same filter to a classmate's proposal and suggest a comparison whose results would point in different directions.

Inside the DDHA 9902 Week 1 example

The first paragraph restates the problem in a sentence and notes that the pattern is well documented. The second proposes a partition of late summaries into five failure categories. The third lists four plausible results, registration errors, fax failures, late signing and an even spread, and pairs each with a different recommendation, concluding that the analysis passes the filter. The fourth rejects a practice satisfaction survey because its results would not change the recommendation, and cites Nosek and colleagues on committing to an analytic approach before seeing results. One reply suggests a classmate partition overtime by time rather than by unit. The other supports a classmate's comparison but recommends a small chart review because a "reconciled by" field may not record who did the work.

DDHA 9902 Week 1 rubric: what earns full marks

Graders of this thread look for a post that chooses an analysis for its decision value. Restating the problem briefly, without relitigating it, shows that the writer understands the course sequence. Mapping plausible results to distinct recommendations is the central element and earns the most credit; a post that proposes an analysis without this mapping misses the week's purpose. Rejecting an alternative that fails the filter demonstrates judgment. Sources should support the logic of committing in advance or the data concerns raised. A reply earns credit when it tests a classmate's plan against the same filter and either proposes a sharper comparison or names a concrete data risk. Brief posts that make one clear argument usually outperform long ones, and correct APA citation, timeliness and a respectful tone toward classmates are expected throughout the discussion.

DDHA 9902 Week 1 help from the desk

The most common weakness is proposing the analysis you most want to run without asking whether its results would change anything. Write down the possible results first, then the recommendation for each. If the recommendations are identical, choose a different analysis. Be concrete about what you will compare. Do not reopen the question of whether the problem exists. Mention what you considered and dropped. In replies, look for proposed comparisons whose possible results collapse into one recommendation, and suggest a split that would separate them. If you want this post built around the problem you established last term, we can draft one from your notes.

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DDHA 9902 Week 1 questions, answered

Where can I find a free DDHA 9902 Week 1 sample paper?

The analysis choice post with both replies and references is free to read here.

Which analysis does the DDHA 9902 Week 1 example propose?

A partition of late discharge summaries by the point at which each one failed.

What filter does the post apply to the analysis?

Whether each plausible result would lead to a different recommendation; if all lead to the same one, the analysis is not worth doing.

Why is a practice satisfaction survey rejected?

Whatever it found, the recommendation would still be to deliver summaries on time.

What do the replies suggest to classmates?

Partitioning overtime by time instead of unit, and checking a reconciliation field with a chart review before trusting it.