| Course | DNP 810 Evidence-based Practice for Quality Improvement |
|---|---|
| Module | Module 5 |
| Paper type | Methodology and data collection plan |
| Length | About 1,013 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | DNP |
| Updated | September 2026 |
Free sample paper for DNP 810 Module 5
Eighteen Months of Data and One Question: Methodology and Data Collection Plan for an Antibiotic Redosing Improvement Project
Student Name
Doctor of Nursing Practice Program, Aspen University
DNP 810: Evidence-based Practice for Quality Improvement
Instructor Name
Month Day, Year
Eighteen Months of Data and One Question: Methodology and Data Collection Plan for an Antibiotic Redosing Improvement Project
A methodology section turns a promising idea into a plan that another person could carry out and a reader could evaluate. This paper sets out the methodology for the project testing a nurse and pharmacist redosing process for admitted sepsis patients who remain in a composite hospital's emergency department when their second antibiotic dose is due. It describes the design, setting and population, eligibility rules, sample, measures and data sources, the data collection plan, data quality procedures and ethical safeguards. The plan is organized to match the reporting standard for improvement studies, so that context, intervention, measures and analysis are described in enough detail for readers to judge whether the results would apply to them (Ogrinc et al., 2016).
Design
The project uses an interrupted time series design, a strengthened form of before-and-after comparison. Monthly measures are collected for 12 months before the intervention, from existing records, and for 6 months during and after implementation. Plotting many points before and after the change allows the team to see whether any improvement coincides with the intervention or reflects a trend that was already under way, which a single before-and-after comparison cannot show. The design does not include a concurrent control group, since the intervention will be implemented across the whole emergency department, so other changes during the period, such as new hospitalist staffing or changes in boarding, will be recorded and considered in interpretation.
The design also fits the practical constraints of a single community hospital. Randomizing patients or shifts would be difficult to administer and could confuse staff, since the same nurses and pharmacists work across shifts. A time series using data the hospital already collects is feasible within the project's resources while still offering stronger evidence than a simple comparison of two periods.
Setting and Population
The setting is the emergency department of a 360-bed community hospital with about 58,000 visits a year and a median boarding time of 5.5 hours for admitted patients. Eligible patients are adults admitted through the emergency department for sepsis, including septic shock, identified through the hospital's sepsis registry, who received a first dose of intravenous antibiotics in the emergency department and were still there when the second dose was due according to the drug's recommended interval.
Patients are excluded if they received only a single-dose antibiotic, died or were transferred out before the second dose was due, declined antibiotics, or were moved to comfort care. Patients whose first dose was given before arrival, for example by paramedics, are excluded because the timing of that dose may not be documented reliably. Based on the baseline audit, about 38 eligible patients per month are expected, or about 228 during the six intervention months.
Eligibility is decided from the record rather than by staff at the bedside, so that no patient is included or excluded because of how busy the department was. The analyst applies the same rules to the baseline and intervention periods.
Measures, Sources and Owners
The primary outcome is, for each month, the share of eligible patients whose second dose was late by the major-delay standard, defined as a first-to-second dose interval at least 125% of the recommended interval. This definition matches the one used in the largest published cohort of second-dose delays, which found such delays in 33% of patients (Leisman et al., 2017), allowing comparison. The data collection plan is summarized below.
| Measure | Type | Source | Frequency | Owner |
|---|---|---|---|---|
| Major second-dose delay, % | Outcome | Medication administration record scan times | Weekly in pilot, then monthly | Informatics analyst |
| First-to-second dose interval, hours | Outcome, secondary | Medication administration record | Monthly | Informatics analyst |
| Second-dose order within 30 minutes of first dose, % | Process | Pharmacy order log | Weekly | Emergency pharmacist |
| Nurse reminder acknowledged, % | Process | Task log in electronic record | Weekly | Nurse champion |
| Duplicate antibiotic doses, count | Balancing | Pharmacy intervention log | Weekly | Emergency pharmacist |
| Pharmacist minutes per patient | Balancing | Time study, two weeks per phase | Twice | Project lead |
| Hospital mortality and length of stay | Monitoring only | Sepsis registry | Monthly | Quality department |
Data Quality
Because the primary outcome depends on accurate administration times, the plan uses barcode scan times rather than manually entered times. Before baseline data are finalized, a second reviewer will independently abstract a random 20% of cases, and agreement on whether a major delay occurred will be calculated; disagreements will be resolved by reviewing the full record. Eligibility will be checked against the sepsis registry monthly, and any patients missing from the report will be added. The analyst's query will be tested against 20 manually reviewed cases before use.
Missing data will be tracked rather than ignored. If a second dose has no scan time, the case is reviewed by hand; if the time still cannot be established, the case is reported as missing and excluded from the proportion, and the number of such cases is reported each month so readers can judge whether missing data could change the conclusions.
Ethical Considerations
The hospital's review office will be asked to classify the project as either quality improvement or research. It implements a process change supported by existing evidence, including a pre-post study in which pharmacist-led redosing cut major delays by more than two thirds (Payne-Cardona et al., 2021), and uses data collected in routine care, which suggests a quality improvement determination. Regardless, data will be extracted into a secure, password-protected file on the hospital's network, identified by study numbers, with the linking key stored separately and destroyed after the project ends. Only aggregate results will be reported.
Staff who take part are not evaluated individually on the measures; results are reported by shift and month, not by named nurse or pharmacist.
Conclusion
The methodology pairs an interrupted time series design with a precisely defined population, a primary outcome that matches published definitions, a data collection plan with owners and sources for every measure, procedures to protect data quality and privacy, and a clear process for ethical review. Together these give the project a credible basis for judging whether the redosing process works.
References
Leisman, D., Huang, V., Zhou, Q., Gribben, J., Bianculli, A., Bernshteyn, M., Ward, M. F., & Schneider, S. M. (2017). Delayed second dose antibiotics for patients admitted from the emergency department with sepsis: Prevalence, risk factors, and outcomes. Critical Care Medicine, 45(6), 956-965. https://doi.org/10.1097/CCM.0000000000002377
Ogrinc, G., Davies, L., Goodman, D., Batalden, P., Davidoff, F., & Stevens, D. (2016). SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence): Revised publication guidelines from a detailed consensus process. BMJ Quality & Safety, 25(12), 986-992. https://doi.org/10.1136/bmjqs-2015-004411
Payne-Cardona, M., San Luis, V. A., Aazami, R., Dermendjieva, M., Erin, M., Kirkwood, J., Tong, C., Marks, G., Smith, E. A., Torbati, S. S., & Gilmore, J. F. (2021). Pharmacist driven antibiotic redosing in the emergency department. The American Journal of Emergency Medicine, 50, 160-166. https://doi.org/10.1016/j.ajem.2021.07.039
DNP 810 Module 5 instructions, in plain terms
Aspen keeps its DNP 810 module prompts in the classroom, and the example therefore tracks the catalog's language on methodology and design for validating and implementing change. A methodology paper at this point normally asks you to name and justify a design, define the setting and sample, describe each measure and its source, and address data quality and ethics. Your prompt may ask for a data collection tool, a timeline or an institutional review determination. Check whether it expects a table of measures and whether the project must be framed as quality improvement or research. Length and source requirements vary, so confirm them.
How the DNP 810 Module 5 example is put together
At about 1,010 words, the example is compact and organized in six sections. The design section names the interrupted time series, explains what it can show that a simple before and after comparison cannot, and admits its main weakness. Setting and population describe the department, the monthly volume and the inclusion and exclusion rules. The measures section places each measure in a table with its source, frequency and owner. Data quality covers how scan times are checked against the medication record and how missing data are handled. Ethical considerations explain why the work is quality improvement, how data are protected and how staff are treated. The conclusion lists the components briefly.
DNP 810 Module 5 rubric: what earns full marks
Rubrics for methods papers usually put the most weight on the fit between design and question, and this example addresses it directly by explaining why a time series suits a project with monthly data. The measures table earns completeness points, because each data stream has a source and an owner, and the margin notes point out why ownership matters. Data quality and ethics are often separate rubric rows, so each gets its own heading here. Organization follows the order a reader expects: design, sample, measures, quality, ethics. Format points depend on a properly labeled APA table and accurate citations for the design literature.
DNP 810 Module 5 help: mistakes that cost marks
The biggest mistake in a methods paper is naming a design without explaining why it fits. Say what the design can show and what it cannot. Students also describe measures without sources, leaving the reader unsure where the numbers will come from. Name the report, the system field or the audit tool. Another common gap is data quality: electronic records contain errors, and a plan that ignores them looks naive. Include at least one check. Some papers also confuse quality improvement with research and promise statistical significance a small project cannot deliver. Describe your analysis honestly, and address ethics even when formal review is not required. A single paragraph on consent, privacy and how staff data will be handled is usually enough to satisfy that rubric row.
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.
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DNP 810 Module 5 questions, answered
What does DNP 810 Module 5 usually ask for?
Aspen's DNP 810 description includes creating methodology and design to validate change, so a methodology and data collection plan is a typical assignment. Check your classroom for the prompt.
What is an interrupted time series design?
A design that collects repeated measurements before and after an intervention, showing whether a change coincides with the intervention rather than with an existing trend.
Why assign an owner to every measure?
Because data that no one is responsible for collecting often go missing. Naming the source, frequency and owner makes the plan executable.
Where can I find a free DNP 810 Module 5 sample paper?
This page carries the whole of one: a methodology and data collection plan for an antibiotic redosing project, with its title page, measures table, references and margin notes. Nothing is charged to read it. A custom plan for your own project can be requested through the form.
What design suits a DNP 810 Module 5 project?
For a single-site improvement project with monthly data, an interrupted time series or a run chart analysis often fits better than a simple before and after comparison. Choose the design your data can support, and justify it in a paragraph.