NU802 Unit 6 DNP Data Collection Plan Example

Course	NU802 DNP Project I
Unit	Unit 6
Paper type	Data collection plan
Length	About 1,060 words, 6 pages double-spaced
Format	APA 7 student paper
School	Purdue University Global
Program	Doctor of Nursing Practice (DNP)
Updated	October 2026

Free sample paper for NU802 Unit 6

From Discharge Field to Monthly Chart: A Unit 6 Data Collection Plan for an Insulin Discharge Readiness Project

[Student Name]

Purdue University Global

NU802: DNP Project I

Unit 6 Data Collection Plan

[Instructor Name]

[Date]

The hospital, units, staff and data systems are composite and illustrative; the sources cited are real.

What this part is doingPromising source, collector, timing and accuracy for every measure in the lead sets the structure the plan follows.

Principles

Three principles shape the plan. First, use data the hospital already records wherever possible, because data collected by hand for a project tend to stop when the project's energy fades. Second, check that recorded data match what actually happened, since a documentation field can be ticked without the care being given. Third, collect only what the measures need and keep it in a form that does not identify patients outside the hospital's systems.

Data Sources

Electronic health record. Admissions, discharges, insulin orders, glucose results, diagnosis codes for return visits and the discharge navigator's required fields for each bundle component.

Pharmacy dispensing system. The date and time the pen and needles were dispensed, and whether prior authorization or an assistance application was started.

Text follow-up platform. Message dates, replies, alerts and nurse response times, exported weekly without message content.

Chart audits. A structured review of a monthly sample of charts on each unit, to confirm what the fields record.

Unit training records. Completion of the online module and the pen station by staff on each unit.

Measure by Measure

30-day return for a glucose problem. Source: encounter data and diagnosis codes. The analyst runs a query on the fifth of each month for patients discharged in the month that ended 30 days earlier, so each month's figure is complete. Collector: the quality department analyst. Accuracy check: each quarter, the resource nurses review every return flagged on their units to confirm the visit was for a glucose problem rather than a coding coincidence.

All five components delivered. Source: the discharge navigator fields, combined with the pharmacy and text platform records. A patient counts only if every field is complete. Collector: the analyst, monthly. Accuracy check: the chart audit below.

Pen by the first insulin day. Source: pharmacy dispensing time compared with the time of the first insulin dose recorded on the medication administration record. Collector: the analyst, monthly.

Contact by day three. Source: the text platform's first completed reply or the nurse's documented call. Collector: the analyst, monthly.

Transition questions answered. Source: the day-seven contact record, where the nurse records the patient's answers to three short items about confidence and the meaning of insulin. Collector: the diabetes educator's team, entered at the time of contact. Reported as exploratory.

Time from discharge order to departure. Source: order and departure timestamps. Collector: the analyst, monthly, as a median by unit.

Nurse minutes on follow-up. Source: the time logged in the follow-up work queue. Collector: the diabetes educator, weekly.

A field that says the injection was supervised is a claim; the audit is how the project learns whether the claim is true.

What this part is doingGiving every measure its own source, collector and accuracy check means the plan could be handed to the analyst as written.

The Chart Audit

Each month, the resource nurse on each unit reviews five randomly selected charts of patients discharged on new insulin, using a one-page audit tool with a decision rule for each component. For the supervised injection, for example, the rule asks whether a nurse's note describes the patient or family member giving an actual dose with the discharge pen type, not only watching a demonstration. Vassar and Holzmann (2013) recommend defining variables in advance, writing rules for unclear cases, training the reviewers and testing agreement between them, and the audit follows that advice. Before go-live, the resource nurses will audit the same ten charts independently, and the tool will be revised until they agree on at least nine of ten components across those charts. Every third month, two resource nurses will audit the same five charts to check that agreement holds.

What this part is doingBuilding agreement testing into the audit shows the plan treats data accuracy as something to measure, not to assume.

Data Dictionary

A data dictionary will define every variable used: its name, its definition, the system it comes from, the allowed values and how missing values are handled. For example, "pen_first_day" is yes when the pharmacy dispensing time falls on or before the calendar day of the first insulin dose, no when it falls later and missing when no pen was dispensed because the patient brought one from home. The dictionary is written before baseline data are pulled, so that the year before each start and the months after it are counted by the same rules.

Storage and Privacy

Patient-level data stay inside the hospital. The analyst builds each month's data set in the quality department's secure database, which uses REDCap, a web-based system designed for capturing and managing study data with user access controls and audit trails (Harris et al., 2009). Each patient receives a project number, and the key linking numbers to record numbers stays with the analyst. The DNP student receives only data sets without names, record numbers, dates of birth or addresses, with dates converted to days from discharge. Those steps follow the safe harbor approach in federal guidance on removing identifiers from health information (U.S. Department of Health and Human Services, 2012). No project data are kept on personal devices, sent by email or entered into public software tools.

Timeline

Baseline data for all eight units are pulled in NU802 week six, covering 12 months before each unit's planned start. The data dictionary and audit tool are finalized in week seven, and the agreement test is completed in week eight. Monthly collection begins with the first go-live in NU804 week one and continues through NU806.

Risks to the Data

The main risks are drift in documentation, changes to the electronic record during the project and the loss of the analyst's time to other priorities. The quarterly audit agreement check addresses drift. Any change to a field used by the project will be logged with its date. The sponsor has written the analyst's four hours a month into the quality department's work plan.

Conclusion

Every measure in the project has a named source, a collector, a schedule and an accuracy check. Most data come from systems the hospital already uses, the chart audit tests whether those systems tell the truth and the storage plan keeps patient data inside the hospital. With the dictionary and audit tool settled before baseline, the project's results will mean the same thing in month 14 as in month one.

References

Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic data capture (REDCap): A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377-381. https://doi.org/10.1016/j.jbi.2008.08.010

U.S. Department of Health and Human Services. (2012). Guidance regarding methods for de-identification of protected health information in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification/index.html

Vassar, M., & Holzmann, M. (2013). The retrospective chart review: Important methodological considerations. Journal of Educational Evaluation for Health Professions, 10, 12. https://doi.org/10.3352/jeehp.2013.10.12

NU802 Unit 6 instructions, in plain terms

Unit 6 typically asks for the plan that will gather the DNP project's data. Expect to list each measure with its definition, data source, instrument or field, collector and frequency. Instructors also want to know how accuracy will be checked, especially when the data come from documentation, and how the data will be stored and protected. A data dictionary or similar set of definitions is often requested, and a timeline for baseline and follow-up data is usually required. Plans that rely on routine data where possible tend to be judged more feasible than those that add collection by hand. Many prompts also ask about privacy rules and who will have access to identifiable information. A short section on risks to data quality, such as documentation drift or system changes, shows the plan was written for real conditions.

Inside the NU802 Unit 6 example

The plan opens with three principles: use existing data, test that recorded data match care and keep only what the measures need. Data sources are listed, from the health record to the text follow-up platform. Each of the seven measures then receives its own paragraph with source, collector, timing and check, and a highlighted line notes that a documentation field is only a claim until audited. The audit section sets five charts a month per unit, decision rules for unclear cases and an agreement test before go-live, following Vassar and Holzmann. A data dictionary entry for the pen measure shows how missing values are handled. Storage and privacy describe REDCap, project numbers and the federal de-identification approach. A timeline and three risks, documentation drift, record changes and analyst time, close the plan.

Where the points sit in the NU802 Unit 6 rubric

A data collection plan is usually graded on completeness and feasibility. Every measure should have a definition, a source, a collector and a frequency, and graders check that none are missing. Accuracy checks earn significant credit, since documentation-based measures can be misleading without them. Privacy and storage are frequently their own rubric item: who sees identifiable data, where it lives and how it is protected. Reliance on routine data, with honest notes on what those data miss, shows feasibility. A data dictionary or set of definitions written before baseline earns credit for consistency. A timeline that ties baseline and follow-up to the implementation schedule shows planning. Clear organization, with each measure easy to find, and correct APA 7 citations of the methods and guidance used complete the expected work.

Common NU802 Unit 6 mistakes, and how to avoid them

Data plans lose points most often by naming measures without saying where the numbers will come from or who will pull them. Another common gap is trusting documentation fields without any audit, so the plan would report care that may not have happened. Some students plan to collect everything by hand, which is rarely sustainable over a year. Privacy sections are often thin or say only that data will be kept confidential, without describing storage or access. Plans that change definitions between baseline and follow-up make results meaningless. Missing values and edge cases, such as a patient who brought a pen from home, are often ignored. Begin by meeting the analyst who will run the queries and walking through each measure together. Send your measure list and the systems your site uses, and the plan can be drafted to fit them.

Get an NU802 Unit 6 example written to your instructions

Send the measures, the setting and the plan instructions with the rubric. A custom data plan shows how each measure will be gathered and protected. It is free the first time and arrives within 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.

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NU802 Unit 6 questions, answered

What belongs in a data collection plan?

Each measure's definition, data source, instrument, frequency, collector and storage, plus how quality is checked.

Can I use existing hospital data?

Often, yes, if the site agrees and access follows its privacy rules.

Should the plan include a data dictionary?

It helps, especially when several people collect data.

Where can I find a free NU802 Unit 6 sample paper?

The complete NU802 Unit 6 data collection plan is on this page, setting out the source, collector, schedule and accuracy check for each of seven measures in an insulin discharge project.

How does the NU802 Unit 6 sample check documentation accuracy?

Resource nurses audit five random charts per unit each month with written decision rules, reach agreement on ten test charts before go-live and recheck agreement every third month.