Twelve Weeks Before, Twelve Weeks After: A Data Collection and Evaluation Plan for Replacing Nothing After Midnight, Built on Structure, Process and Outcome and Read in Time Order
[Student Name]
University of Phoenix
DNP/710: Evidence-Based Practice Measurement and Clinical Inquiry
Week 7 Assignment
[Instructor Name]
[Date]
The project is a composite written for a model paper.
The new fasting instructions, with a carbohydrate drink taken shortly before surgery, will be piloted in our same-day surgery center. This paper plans how I will collect data and evaluate whether the change works, including the design, measures organized by Donabedian's framework, responsibilities, timeline, data quality and analysis.
Design
The project uses a before-and-after design with 12 weeks of baseline data under current instructions and 12 weeks after the new instructions begin, with a two-week transition in between that is excluded from analysis. A randomized design is not feasible because instructions are given to all patients by the same nurses, and mixing instructions would confuse staff and patients.
Donabedian's Framework
Donabedian (1988) offered three lenses for judging care. Structure is the setting and its resources, such as staff, equipment and written policies. Process is what clinicians and patients actually do. Outcome is what happens to the patient as a result. He described these as linked in sequence, each making the next more likely, and argued that an assessment relying on only one of them will miss part of the picture.
Structure Measures
Structure measures confirm that the conditions for the change exist: updated written instructions in English and Spanish, the carbohydrate drink stocked and given to patients at their preadmission visit, nurse training completion rates and an updated order set approved by anesthesia. I will verify these before the change begins and record them once.
Process Measures
Process measures capture whether the change is carried out: the proportion of eligible patients who received the new instructions, the share who finished the drink at the planned time and median hours without liquids before induction. These are collected for every eligible patient by the preoperative nurse.
If thirst does not fall, the process measures will say whether the drink failed or whether it was never drunk.
Outcome Measures
Outcome measures are patient-reported thirst and hunger before surgery on 0-to-10 scales, quality of recovery by the QoR-15 at the next-day phone call and balancing measures: aspiration and any schedule disruption traced to oral intake.
Who Collects What and When
Preoperative nurses record time of last intake, carbohydrate drink timing, thirst and hunger in the final minutes before the patient is wheeled to surgery, using new fields in the electronic record. Anesthesia induction time is extracted from the anesthesia record. Follow-up nurses administer the QoR-15 during the routine next-day call. The quality analyst extracts safety events weekly. I review completeness weekly.
Eligibility Screening
Each patient's eligibility is determined at the preadmission call using a short checklist of exclusions. The checklist result is recorded, so the evaluation can report how many patients were excluded and why, and so exclusions can be audited for consistency.
Handling Schedule Changes
When a case moves earlier, the nurse records the change and whether the patient had already had the drink. These cases will be analyzed separately to see whether schedule changes create delays or safety concerns, a practical question the pilot must answer.
Sample Size
Our center treats about 115 elective adult patients per week; after exclusions, about 70 are eligible. Over 12 weeks, that yields roughly 840 patients per period. If only half have complete data, 420 per period is enough to detect a 1-point difference in mean thirst with high power.
Controlling for Seasonal and Case-Mix Change
A before-and-after design is vulnerable to changes other than the intervention. Case mix may shift between periods, for example if an orthopedic surgeon joins. I will compare the two periods on age, sex, procedure type and scheduled time of day, and if they differ, adjust comparisons for these factors. Running both periods within six months limits seasonal differences.
Data Quality
Missing data are the main risk. New record fields will be required before the patient can be marked ready for transfer, preventing omission. A one-in-ten sample of charts from the opening month will be checked against the anesthesia record to confirm that entered times are accurate.
Training Data Collectors
All preoperative and follow-up nurses will complete a short session on the new fields, the eligibility checklist and the scripts. Consistent collection across nurses and shifts is as important to the evaluation as the choice of measures.
Privacy
Data will be extracted without names into a secure project database, with a study number linking records. Only aggregated results will be reported, and the project database will be deleted two years after the final report.
Analysis Over Time
Perla et al. (2011) describe run charts as simple tools for learning from variation, plotting data in time order with a median and applying rules to detect shifts, trends and unusual runs. I will plot weekly median fasting duration, weekly mean thirst and weekly carbohydrate drink adherence, with the baseline median extended into the intervention period. A sustained run of weekly points on the improved side of the baseline median, long enough to meet the published run rules, would signal nonrandom improvement.
Comparison Between Periods
I will compare thirst and hunger between periods with an independent-samples test appropriate to the distribution, report differences with 95% confidence intervals and compare QoR-15 scores similarly. Balancing measures will be reported as counts and rates.
Qualitative Feedback
Numbers will not capture everything. At the end of the pilot, I will hold two short debriefs with nurses and invite five patients to describe their experience. Their comments will help explain the results and improve the process before wider adoption.
Reporting
Ogrinc et al. (2016) developed SQUIRE 2.0 to guide reporting of quality improvement work, including the context, the intervention, the study of the intervention, measures, analysis and the results including unintended consequences. I will structure the final report accordingly, including any changes made to the intervention during the pilot.
Interim Review
At the midpoint of the intervention period, the team will review process and balancing measures. If adherence to the drink is below 60%, we will investigate why and adjust the instructions or supply. If any aspiration event occurs, the stopping rule applies immediately. Interim review keeps a pilot from running for weeks with a correctable problem.
Feedback to Staff
Weekly run charts will be posted in the preoperative area. Seeing fasting times fall and thirst scores improve can motivate staff, and seeing gaps early can prompt correction.
Timeline
Weeks 1 to 12: baseline data. Weeks 13 and 14: training, new instructions and transition. Weeks 15 to 26: intervention data. Weeks 27 to 30: analysis and report.
Conclusion
The evaluation plan organizes measures by Donabedian's structure, process and outcome, uses a 12-week before-and-after design with weekly run charts, assigns every data element to a person and a time and protects data quality and privacy. SQUIRE 2.0 will structure the report. The plan can show not only whether outcomes change but why.
References
Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 260(12), 1743-1748. https://doi.org/10.1001/jama.1988.03410120089033
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 and Safety, 25(12), 986-992. https://doi.org/10.1136/bmjqs-2015-004411
Perla, R. J., Provost, L. P., & Murray, S. K. (2011). The run chart: A simple analytical tool for learning from variation in healthcare processes. BMJ Quality and Safety, 20(1), 46-51. https://doi.org/10.1136/bmjqs.2009.037895
How this DNP 710 Week 7 example is structured
The DNP/710 Week 7 work usually plans data collection and evaluation. This paper links each measure to a level of Donabedian's model, defines the design and timeline, names who collects each data element and how, and chooses analysis methods that fit a quality improvement project. Students search this week as DNP 710 Week 7, DNP710 Wk 7 or DNP/710 Wk 7; all three are the same assignment.
DNP/710 Week 7 questions, answered
What does DNP/710 Week 7 usually ask for?
Many sections ask students to plan how data will be collected and analyzed to evaluate their evidence-based practice project, including design, timeline, responsibilities and analysis.
What is Donabedian's model?
A framework for assessing quality of care through structure, the settings and resources in which care occurs; process, what is actually done in giving and receiving care; and outcome, the effects of care on patients.
Why use run charts for a DNP project?
Run charts display data in time order with a median line and simple rules for detecting nonrandom change, which suits projects where data accumulate week by week and change needs to be detected as it happens.
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