| Course | DDHA 9902 Practice-Based Problem Analysis, Findings, and Recommendations |
|---|---|
| Week | Week 7 |
| Paper type | Recommendation set |
| Length | About 670 words, 5 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | Doctor of Healthcare Administration (DHA) |
| Updated | October 2026 |
Free sample paper for DDHA 9902 Week 7
Four Recommendations, Each Opening With the Finding It Rests On, and Two Findings Declared to Support Nothing
Student Name
Walden University
DDHA 9902: Practice-Based Problem Analysis, Findings, and Recommendations
Instructor Name
Month Day, Year
Four Recommendations, Each Opening With the Finding It Rests On, and Two Findings Declared to Support Nothing
How the Set Is Built
Each recommendation below opens by citing the numbered finding it rests on, so the connection is visible before the proposal is read. Each then states the action, the group it applies to and the change in that finding that would show it worked. The findings come from this term's study of why Larchmont's summaries reach outside practices late; Larchmont is an invented system. A recommendation resting on a finding the peer review judged weak carries that judgment forward.
Recommendations
R1. Resting on F1 and F2: register every remaining fax-only primary care practice that received five or more Larchmont discharges last quarter for secure clinical messaging, beginning with the highest-volume practices, and stop sending summaries to those practices by fax once registration is confirmed. Applies to the independent practices currently receiving faxes. It would be shown to work if the fax path's share of untimely summaries (F1) falls and if the timely share for newly registered practices rises toward the 68% observed for practices already registered (F2). Adler-Milstein et al. (2021) describe how much electronic exchange still depends on participants choosing to join, which is why the action is framed as outreach to individual practices rather than a technical change.
R2. Resting on F1a, the secure messages returned as undeliverable: reconcile the system's directory of practice addresses with the messaging vendor's directory each month, and give health information management a daily list of summaries whose transmission failed, with responsibility for resending each within one business day. Applies to all outside practices. It would be shown to work if failures from inactive addresses fall below 5% of untimely summaries and if the undetermined share in F1a falls, since a daily failure list should reveal causes now hidden.
R3. Resting on F4 and on the late-signing portion of F1a: for patients discharged on Fridays and weekends, ask hospitalists to sign the discharge summary before the patient leaves the unit, with a nocturnist or weekend hospitalist signing any summary still open at the end of the day. Applies to the hospitalist group at both hospitals. It would be shown to work if the timely share for Friday discharges approaches the Monday through Thursday figure in F4 and if late signing's share of untimely summaries falls.
R4. Resting on the wrong-practice portion of F1a, which the peer review judged weak because it was assessed only for discharges with follow-up data and is therefore a minimum: verify the primary care clinician at registration by asking the patient to confirm the practice name and location, and again during discharge planning by the case manager. Applies to patient access staff and case managers. Because the underlying finding is weak, the first step is measurement: expand the follow-up visit data to more practices so that the true share of wrong-practice failures is known, and judge success against that better baseline. Were et al. (2009) found the follow-up provider often missing from discharge summaries elsewhere, which suggests the problem may be larger than F1a shows.
Findings That Support No Recommendation
F3 supports nothing. The difference between the two hospitals fell below the plan's threshold, so every recommendation applies to both hospitals and none is tailored to one. Declaring this prevents a reader from expecting hospital-specific actions.
F5 supports nothing. The comparison of readmission between timely and untimely summaries was exploratory and inconclusive, so readmission is not used as a measure of success for any recommendation. Kripalani et al. (2007) describe the clinical risks of missing discharge information, and those risks justify timely delivery on their own; they do not depend on F5.
What the Set Does Not Recommend
It does not recommend purchasing a new transitions platform. No finding points to a failure that a platform would fix and the existing record system would not, since the system already sends summaries automatically when signed and addressed correctly. It also does not recommend changes to summary content, which lies outside the project's scope.
References
Adler-Milstein, J., Garg, A., Zhao, W., & Patel, V. (2021). A survey of health information exchange organizations in advance of a nationwide connectivity framework. Health Affairs, 40(5), 736-744. https://doi.org/10.1377/hlthaff.2020.01497
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
Were, M. C., Li, X., Kesterson, J., Cadwallader, J., Asirwa, C., Khan, B., & Rosenman, M. B. (2009). Adequacy of hospital discharge summaries in documenting tests with pending results and outpatient follow-up providers. Journal of General Internal Medicine, 24(9), 1002-1006. https://doi.org/10.1007/s11606-009-1057-y
The Walden DDHA 9902 Week 7 instructions
Recommendation sets usually ask you to propose actions based on your findings. Begin every recommendation by citing the numbered finding it rests on, so the connection is visible before the proposal. Then state the action, the group or function it applies to and the change in the finding that would show it worked. Where a recommendation rests on a finding that critique judged weak, carry that judgment forward rather than hiding it. Declare any finding that supports no recommendation and explain why. Say what you are not recommending, especially if an obvious solution has been discussed. Keep the set numbered and concise, and make sure no recommendation rests on a finding the analysis did not produce.
Inside the DDHA 9902 Week 7 example
The set explains how it is built. R1 cites F1 and F2, proposes registering high-volume fax-only practices and ending faxes once registration is confirmed and names two signs of success, citing Adler-Milstein and colleagues. R2 cites F1a and proposes address reconciliation and a daily failure list, with success shown by fewer bounced messages and a smaller undetermined share. R3 cites F4 and part of F1a and proposes earlier Friday and weekend signing. R4 cites the weak wrong-practice finding and proposes measurement before workflow change, citing Were and colleagues. A section declares F3 and F5 as supporting nothing, citing Kripalani and colleagues. A final section explains why a new platform is not recommended.
Where the marks sit in the DDHA 9902 Week 7 rubric
Recommendation sets are graded on traceability and restraint. Markers expect each recommendation to cite its finding first and to state an action, a group and a measurable sign of success. Carrying forward a weak finding's limitation shows honesty. Declaring findings that support nothing earns credit because it prevents readers from assuming actions. Explicitly stating what is not recommended demonstrates judgment, especially when a popular solution lacks support. Graders also check that recommendations do not exceed what the findings can support and that success measures are drawn from the same data used in the analysis, so the organization can tell later whether each one worked.
Common DDHA 9902 Week 7 mistakes, and how to avoid them
The usual problem is a recommendation list that reads like best practices rather than responses to findings. Start each item with its finding. Make the success measure something your data can show. If a finding is weak, say so and propose measurement first. Say plainly when a finding supports nothing. Resist recommending what your findings do not support, even if leaders expect it. If you need help building recommendations that trace back to your findings, we can draft a set with you. Test each recommendation by asking what you would expect to see in three months if it worked; if you cannot answer from your own data, the measure needs changing. Keep the set short enough that each item gets real attention.
Get a DDHA 9902 Week 7 example written to your instructions
Send the prompt, the rubric and the numbered findings the set has to derive from, and a recommendation set carrying its citations, its tests and the unsupported items visibly removed returns inside 24-48 hours, free the first time. Permissions inside an operating health system are arranged by the candidate alone.
More DDHA 9902 papers and DHA samples
- DDHA 9902 Week 1: Analysis Choice Post
- DDHA 9902 Week 2: Analysis Plan
- DDHA 9902 Week 3: Data Description
- DDHA 9902 Week 4: Analysis Draft
- DDHA 9902 Week 5: Findings Statement
- DDHA 9902 Week 6: Findings Critique
- DDHA 9902 Week 8: Implementation Memo
- DDHA 9902 Week 9: Limitations Section
- DDHA 9902 Week 10: Findings and Recommendations Paper
- DDHA 9902 Week 11: Dissemination Brief
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DDHA 9902 Week 7 questions, answered
Where can I find a free DDHA 9902 Week 7 sample paper?
The four-item recommendation set for late discharge summaries is free to read here.
How does each recommendation in the DDHA 9902 Week 7 example begin?
By naming the numbered finding it rests on, so the evidence is visible before the proposal.
Which recommendation rests on a weak finding?
R4, verifying the primary care practice, which begins with better measurement because its finding was a minimum count.
Which findings support no recommendation?
The hospital comparison, which showed no meaningful difference, and the inconclusive readmission comparison.
Why is a new platform not recommended?
No finding identified a failure that new software would fix and the current system would not.