| Course | DNP 851A Project Implementation |
|---|---|
| Module | Module 5 |
| Paper type | Data monitoring paper |
| Length | About 1,005 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | DNP |
| Updated | September 2026 |
Free sample paper for DNP 851A Module 5
Clean Data Before the Analysis: Monitoring Completeness and Accuracy in a Pediatric Asthma Action Plan Project
Student Name
Doctor of Nursing Practice Program, Aspen University
DNP 851A: Project Implementation
Instructor Name
Month Day, Year
Clean Data Before the Analysis: Monitoring Completeness and Accuracy in a Pediatric Asthma Action Plan Project
Data problems found at the end of a project can rarely be fixed; data problems found in week three usually can. This paper describes how data collection is being monitored in my DNP project, in which clinic nurses give school-age children and teenagers with persistent asthma a written plan and coaching on their inhalers. The project's outcomes depend on age-appropriate asthma control questionnaires at enrollment and at 12 weeks, on counts of emergency and urgent care visits for asthma, and on process data from the nurses' checklists. The paper sets out the framework for judging data quality, the weekly checks, what they found by week seven and the rules that will govern the final data set.
A Framework for Data Quality
Reviewing how studies judge electronic record data, one team found five dimensions that recur across studies: completeness, correctness, concordance between sources, plausibility and currency (Weiskopf & Weng, 2013). A later framework, developed to harmonize terms, grouped data quality checks into conformance, completeness and plausibility, and stressed that checks should be chosen for the specific use of the data (Kahn et al., 2016). The project uses both. For each outcome and process variable, the data dictionary names the dimension most at risk and the check that guards it.
The Weekly Checks
Every Friday, the project lead runs the extraction query and applies five checks, summarized in the table with what they found in the first seven weeks.
| Check | Dimension | Rule | Found by week 7 |
|---|---|---|---|
| Control score present at enrollment | Completeness | Every enrolled child has a baseline score | 6 of 97 missing, 4 during the tablet failure |
| Right questionnaire for age | Conformance | Childhood version for ages 5 to 11; adolescent and adult version for 12 and older | 3 children aged 12 given the childhood version |
| Score within range and summed correctly | Plausibility, correctness | Childhood 0 to 27; adolescent 5 to 25; hand-summed forms rechecked | 2 addition errors on paper forms |
| Acute visits captured | Completeness, concordance | Record search plus parent report at 12 weeks; outside visits confirmed through the regional exchange | 3 visits at an outside emergency department not in the record |
| No duplicate enrollment | Correctness | One record per child | 1 child enrolled twice after a name change |
Why the Questionnaire Checks Matter
The primary outcome is the share of children whose asthma is well controlled at 12 weeks, judged by validated questionnaires. The childhood version, completed jointly by the child and caregiver for ages 4 to 11, scores from 0 to 27, and a score of 19 or less indicates inadequate control (Liu et al., 2007). The version for adolescents and adults scores from 5 to 25 and uses the same cut point of 19 (Nathan et al., 2004). Because the two instruments share a cut point but not a range, a child given the wrong version, or a form summed wrongly, could be misclassified. The three 12-year-olds given the childhood version were identified in week four; their parents completed the correct version by telephone within the same week, and the rule for choosing the form was printed on the rooming checklist.
Missing Baseline Scores
Six children lacked a baseline score, four of them during the week the check-in tablet could not accept Spanish entries. Two were recovered from paper forms that had been completed but not scanned. For the remaining four, a baseline completed after the visit would not be comparable, because the nurse's teaching could already have changed answers. Those children will stay in the program and in the process measures but will be excluded from the paired analysis of control, and the report will say so. The paper form in Spanish, introduced in the second plan-do-study-act cycle, has prevented further gaps.
Acute Visits Outside the Clinic's System
Emergency visits for asthma are a secondary outcome, and they are easy to miss when children are seen at hospitals outside the health system. In week five, a parent mentioned a visit that was not in the record. The project lead then compared parent report at the two-week call with the record for every child reached, and found three outside visits. With the site's approval, the regional health information exchange is now queried for enrolled children at 12 weeks, and parent report is recorded on a standard form. Visits confirmed by either source will be counted, and the source will be noted.
Process Data From Nurse Checklists
Nurse checklists supply the fidelity measures. Their accuracy is checked by auditing one in five visits against the chart. By week seven, 19 audited visits showed agreement on every core element in 18, and the one disagreement, a school consent ticked but not documented, was corrected. The audit rate will continue unchanged, since agreement has been high.
Should agreement drop under 90% across a fortnight, the audit will double to two visits in five until it recovers.
Rules for the Final Data Set
Four rules were fixed now, before any outcome data are examined, so that later decisions cannot be influenced by results. First, a child is included in the paired analysis only if both scores come from the age-appropriate instrument completed within the planned windows. Second, the 12-week score is valid if completed between weeks 10 and 14. Third, acute visits count if confirmed by the record, the exchange or a completed parent report form. Fourth, every correction to the data set is logged with its date, the reason and the person who made it.
Conclusion
Weekly checks organized around recognized dimensions of data quality found problems in completeness, conformance, correctness and concordance while they could still be fixed. Most were corrected at the source; the few that could not be are handled by rules set in advance. The data set that reaches the analysis will be smaller in places, but it will be accurate and its limits will be known.
The checks take about 40 minutes each Friday, a small cost set against the value of results the committee can trust.
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 (Generating Evidence & Methods to Improve Patient Outcomes), 4(1), Article 18. https://doi.org/10.13063/2327-9214.1244
Liu, A. H., Zeiger, R., Sorkness, C., Mahr, T., Ostrom, N., Burgess, S., Rosenzweig, J. C., & Manjunath, R. (2007). Development and cross-sectional validation of the Childhood Asthma Control Test. Journal of Allergy and Clinical Immunology, 119(4), 817-825. https://doi.org/10.1016/j.jaci.2006.12.662
Nathan, R. A., Sorkness, C. A., Kosinski, M., Schatz, M., Li, J. T., Marcus, P., Murray, J. J., & Pendergraft, T. B. (2004). Development of the Asthma Control Test: A survey for assessing asthma control. Journal of Allergy and Clinical Immunology, 113(1), 59-65. https://doi.org/10.1016/j.jaci.2003.09.008
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
Reading the DNP 851A Module 5 assignment instructions
Module 5 turns to the data themselves. The catalog describes DNP 851A as the implementation course, and that description anchors this sample, since the module prompt is posted only for enrolled students. Assignments on data monitoring generally want to know how completeness and accuracy are checked while collection runs, what problems you found and how you corrected them. Your chair may ask for a data dictionary, a list of checks or evidence of an audit. Some programs want the rules for handling missing data set now, before analysis. Check the page range and source rules, and describe your instruments precisely, including score ranges and cut points, because errors often hide there.
How this DNP 851A Module 5 example is built
Roughly 1,020 words fill nine sections. The introduction explains why data problems are cheaper to fix early. A framework section introduces the five dimensions of record data quality and a harmonized set of check categories. The weekly checks are presented in a table with dimension, rule and findings. A section on the two questionnaires explains their ranges and shared cut point and how wrong-form errors were corrected. Missing baseline scores are handled with a stated rule. Acute visits outside the system are captured through the regional exchange and parent report. Nurse checklists are audited against the chart. Four rules for the final data set are fixed in advance, and the conclusion weighs a smaller but accurate data set.
DNP 851A Module 5 rubric: what earns full marks
Data monitoring papers are graded on a systematic approach, accurate handling of instruments and transparency. This example is systematic because each check is tied to a named dimension of data quality, and the margin notes show the table reporting both method and yield. Instrument accuracy is shown by stating each questionnaire's range and cut point, with sources, and by explaining why the wrong form could misclassify a child. Transparency comes from rules set before outcomes are seen and from a correction log. Handling outside visits through two sources shows attention to concordance. Format marks close the rubric: an APA-styled table and exact citations for both frameworks and both questionnaires.
DNP 851A Module 5 help from the desk
Students often wait until the end of the project to look at their data, then discover gaps that cannot be filled. Check weekly. Another common mistake is assuming the record captures everything, when children are often seen at outside hospitals. Use a second source. Papers also leave missing data rules until the analysis, which invites decisions shaped by results. Set them now. Some students report data problems without saying how they were fixed. Describe each correction and log it. Finally, know your instruments. Two tools with the same cut point but different score ranges are easy to mix up, and a single wrong form can change a child's classification.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Aspen University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
More DNP 851A and DNP sample papers
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- DNP 851A Module 6: Communicating Progress to Stakeholders
- DNP 851A Module 7: Midpoint Review and Adjustments
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DNP 851A Module 5 questions, answered
What does DNP 851A Module 5 usually ask for?
Aspen's DNP 851A description covers the implementation phase, so a paper on monitoring data collection for completeness and accuracy is a typical assignment. Check your classroom for the prompt.
What are the dimensions of data quality?
A widely cited review of electronic record data names completeness, correctness, concordance, plausibility and currency. Choose checks for the dimensions most at risk for each variable in your project.
What score shows poorly controlled asthma?
On both the Childhood Asthma Control Test and the Asthma Control Test, a score of 19 or less indicates asthma that is not well controlled, although the two tests have different score ranges.
Where can I find a free DNP 851A Module 5 sample paper?
The whole data monitoring paper, checks table and margin notes included, is on this page with no charge. It is part of the DNP 851A series that follows a pediatric asthma program through its implementation phase.
How often should data be checked in DNP 851A Module 5?
Weekly checks during collection catch most problems while they can still be fixed. Tie each check to a data quality dimension such as completeness, correctness or concordance, and log every correction you make.