| Course | EDN 820 Evidence-Based Practice and Quality Improvement |
|---|---|
| Module | Module 8 |
| Paper type | Improvement evaluation |
| Length | About 1,066 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Doctor of Education |
| Updated | September 2026 |
Free sample paper for EDN 820 Module 8
Did It Work, and How Do We Know? Evaluating Six Months of a Perioperative Hypothermia Improvement Project
Student Name
Doctor of Education Program, Aspen University
EDN 820: Evidence-Based Practice and Quality Improvement
Instructor Name
Month Day, Year
Did It Work, and How Do We Know? Evaluating Six Months of a Perioperative Hypothermia Improvement Project
Six months after Allerton Community Hospital began testing prewarming, the perioperative quality committee asked whether the project had met its aim of cutting cold arrivals in recovery from 22% to 10%. This paper evaluates the results. It explains how run charts distinguish real improvement from random variation, presents outcome, process and balancing results, examines results by surgical service, considers whether the changes caused the improvement, states the limitations and sets out plans for sustaining and reporting the work.
Reading Change Over Time
A run chart shows a measure over time with the median of the baseline period as a reference line. Perla et al. (2011) set out four rules for spotting non-random signals. A shift is an unbroken series of six or more values all above, or all below, the median line. A trend is a series of five or more values in which each one rises, or each one falls, compared with the value before it. The count of runs, meaning stretches of consecutive values on one side of the median, can be improbably low or high. And an astronomical point stands far apart from all the others. These rules let a team judge change without formal statistical testing and guard against celebrating ordinary fluctuation.
The Signal
The outcome was plotted every two weeks. During the 12 baseline periods the percentage varied around a median of 22%. After prewarming spread to all first cases in the third month, the chart showed eight consecutive points below the baseline median, meeting the rule for a shift, and the last six points ranged from 9% to 13%. The shift began within two weeks of the change to first-case warming and deepened after orthopedic transport warming was adopted, a timing that supports a link between the changes and the result.
Results at Six Months
The table compares baseline results with the final two months of the project.
| Measure | Baseline | Final two months | Target | Met? |
|---|---|---|---|---|
| Outcome: arrival below 36.0 degrees | 22.0% (273 of 1,240) | 11.4% (47 of 412) | 10% or less | Not yet |
| Process: 20 minutes of prewarming | 0% | 84% | 90% | Not yet |
| Process: warming continued in transfer | Not measured | 88% | 90% | Nearly |
| Process: intraoperative warming within 15 minutes | 88% | 96% | 95% | Yes |
| Balancing: first-case on-time starts | 89% | 90% | No decline | Yes |
| Balancing: reported overheating or sweating | Not measured | 3% | Under 5% | Yes |
Results by Service
The overall result conceals differences. Hypothermia among orthopedic patients fell from 31.0% to 18.6% and among general surgery patients from 23.9% to 10.2%, while gynecology and urology reached 6% or below. Orthopedics remains the largest contributor, mainly in long joint replacements that begin later in the day, when prewarming is least often achieved. The aim of 10% is within reach overall, but orthopedic cases after the first of the day need further cycles.
Did the Changes Cause the Improvement?
Improvement projects rarely prove causation, but several features support it here. The shift began when the main change was introduced and deepened when a second change was added. Process measures moved in step with the outcome. The gains were largest in the groups the changes targeted, and the scale of the drop fits what published prewarming trials have reported. No other change, such as a new thermometer or a change in operating room temperature, occurred during the period. Still, the design cannot rule out other influences completely, and the conclusion is that the changes very likely contributed rather than that they alone caused the improvement.
Why It Matters Clinically
A drop from 22% to about 11% means roughly 45 fewer hypothermic patients each quarter. Recovery nurses documented less shivering, and median time in recovery for eligible patients fell by 7 minutes. A landmark randomized trial that found fewer wound infections among patients kept warm (Kurz et al., 1996) suggests further benefits that Allerton's numbers are too small to show directly in six months.
Limitations
The evaluation has limitations. It uses a before-and-after design without a concurrent comparison group. Temperature measurement was standardized at launch, so some early improvement may reflect more consistent measurement rather than warmer patients, although the shift appeared after standardization was complete. Six months is too short to judge durability, and infection and other clinical outcomes were not analyzed. These limits are common in improvement work and are reported so readers can weigh the findings.
Sustaining the Gains
Sustaining improvement requires ownership and routines. The preoperative holding area manager now owns the prewarming process, the measures remain on the monthly perioperative quality dashboard, the documentation fields and transport equipment are permanent, and new holding area nurses learn prewarming during orientation. If the outcome rises above 15% for two consecutive months, the team will reconvene. Further cycles will address later orthopedic cases.
Reporting the Work
The team will report the project internally and submit it for a regional quality conference following SQUIRE 2.0, whose checklist covers the problem, the context, the interventions and how they evolved, how they were studied, the measures, the analysis, the results and the limitations, so others can judge whether the work applies to their settings (Ogrinc et al., 2016). Reporting the orthopedic gap and the limitations honestly is part of what makes the report useful.
What Other Hospitals Can Take From This
Several lessons from the project are likely to transfer. Tying warming to something that already happened, the preoperative interview, moved it earlier without extra steps. Gains from prewarming were partly lost in transfer until warming stayed on the stretcher. Standardizing measurement before launch made the results easier to trust. And a balancing measure on first-case starts answered the surgeons' main worry with data. Other hospitals will face different constraints, but these principles, attach the change to existing routines and protect it at handoffs, are general.
Conclusion
The project produced a clear signal on the run chart: hypothermia on arrival in recovery fell from 22.0% to 11.4%, with process measures improving alongside and no harm to first-case starts or patient comfort. The aim of 10% is not yet fully met, mainly because orthopedic cases later in the day remain difficult. The evidence supports a real improvement to which the changes very likely contributed, and the plan now is to sustain it, close the orthopedic gap and share the work so others can learn from both its success and its limits.
References
Kurz, A., Sessler, D. I., & Lenhardt, R. (1996). Perioperative normothermia to reduce the incidence of surgical-wound infection and shorten hospitalization. New England Journal of Medicine, 334(19), 1209-1216. https://doi.org/10.1056/NEJM199605093341901
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
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 & Safety, 20(1), 46-51. https://doi.org/10.1136/bmjqs.2009.037895
What the EDN 820 Module 8 instructions ask for
Aspen's EDN 820 description ends with synthesizing patient outcomes into quality improvement, and the final module asks whether that improvement happened. Because the Module 8 prompt appears only in the course, this example evaluates the results of a completed project. Explain the method you used to judge change, such as run chart rules, before presenting results. Compare results with the aim and with each measure's target, including process and balancing measures. Break results down by the groups you targeted. Reason carefully about causation: what supports a link between your changes and the results, and what cannot be ruled out. State the limitations plainly. Close with how the gains will be sustained and how the work will be reported.
How this EDN 820 Module 8 example is built
The evaluation begins with the quality committee's question and explains four run chart rules for detecting real change. It then describes the signal: eight consecutive points below the baseline median, starting soon after first-case warming began and deepening after transport warming. A five-column table compares baseline and final results for one outcome, three process and two balancing measures with their targets. Results by service follow, with orthopedics falling from 31.0% to 18.6% but still the largest contributor. Sections weigh causation, translate the change into about 45 fewer cold patients a quarter and 7 fewer minutes in recovery, list limitations such as the before-and-after design, set out ownership for sustaining the gains, and draw lessons for other hospitals before discussing SQUIRE reporting.
Reading the EDN 820 Module 8 grading rubric
Evaluation papers are assessed on appropriate methods for judging change, accurate results against targets, careful reasoning about causation, honest limitations and plans to sustain and share. This example applies named run chart rules and reports a specific signal rather than simply comparing two averages. Its three APA sources are the BMJ Quality and Safety run chart guide, the SQUIRE 2.0 reporting guidelines and the New England Journal of Medicine normothermia trial. The paper admits that the aim of 10% was not yet reached and explains why, which instructors value more than an inflated claim of success. Weighing causation through timing, process measures and targeted groups shows the reasoning expected when an improvement project cannot randomize.
Common EDN 820 Module 8 mistakes, and how to avoid them
Evaluation papers often set one pre-project average against one post-project average and declare victory. Use run chart rules, or another method for judging change over time, and report what signal you found. Compare results with every measure's target, including ones you missed. Break results down by the groups you meant to help. Discuss causation carefully; say what makes your changes a likely cause and what else might explain the results. List limitations without apology. Plan who will own the process and how you will know if performance slips. Use SQUIRE 2.0 if you write the project up. If the run chart rules are confusing, a tutor can apply them to your data with you point by point.
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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- EDN 820 Module 4: Reading Patient Outcome Data
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- EDN 820 Module 6: Quality Improvement Plan and Measures
- EDN 820 Module 7: Plan-Do-Study-Act Cycles
- EDN 814 Module 7: Accreditation as a Leadership Decision
- EDN 818 Module 5: Evidence-Based Innovation
- EDN 810 Module 3: Evaluating Resources and Capabilities
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EDN 820 Module 8 questions, answered
What does EDN 820 Module 8 usually ask for?
Aspen's EDN 820 closes with synthesizing patient outcomes into quality improvement, so evaluating the results of an improvement project is a typical final assignment. Follow your classroom prompt.
What is a shift on a run chart?
Six or more consecutive points on the same side of the median, a signal that the process has changed rather than varied randomly.
Can a quality improvement project prove it caused an improvement?
Rarely with certainty, but timing, movement in process measures, gains in targeted groups and consistency with published evidence can make a causal contribution very likely.
Where can I find a free EDN 820 Module 8 sample paper?
The complete evaluation appears above, including run chart findings and a table comparing baseline and final results with each measure's target.
What are the run chart rules for detecting improvement?
Four patterns count as signals: a long unbroken stretch of values above or below the median, a steady climb or fall across several consecutive values, a count of runs that chance alone would rarely produce, and one value far outside the rest.