| Course | EDN 820 Evidence-Based Practice and Quality Improvement |
|---|---|
| Module | Module 6 |
| Paper type | Quality improvement plan |
| Length | About 1,064 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 6
Twenty-Two Percent to Ten: A Quality Improvement Plan With Measures for Preventing Perioperative Hypothermia
Student Name
Doctor of Education Program, Aspen University
EDN 820: Evidence-Based Practice and Quality Improvement
Instructor Name
Month Day, Year
Twenty-Two Percent to Ten: A Quality Improvement Plan With Measures for Preventing Perioperative Hypothermia
Modules 2 through 5 established that prewarming is supported by evidence, that more than a fifth of Allerton Community Hospital's eligible patients reach recovery colder than 36 degrees Celsius, that the problem is concentrated among orthopedic, long and older patients, and that the Model for Improvement suits the work. This paper sets out the quality improvement plan: the aim, the team, a driver diagram of the changes, a family of measures, data collection, a timeline and the ethical and communication considerations.
The Aim
The aim meets the usual SMART tests: within six months of launch, cut the share of eligible adults whose first recovery temperature is under 36.0 degrees Celsius from 22% to 10% or less, and hold it there for eight straight weeks. The target reflects the stratified data, which suggest that bringing the high-risk groups toward the rates of the low-risk groups would roughly halve the overall rate, and the prewarming trial, which found much lower rates of hypothermia with 20 minutes of warming (Horn et al., 2012).
The Team
The team includes the perioperative nurse manager as leader, two preoperative holding nurses, an operating room circulating nurse, a recovery room nurse, a nurse anesthetist, an orthopedic surgeon, a patient transport aide and a quality analyst, with the chief of anesthesia as executive sponsor. The patient experience office will recruit a former surgical patient to review materials and attend monthly meetings.
Driver Diagram
The team's driver diagram links the aim to four primary drivers and their secondary drivers. The first primary driver is prewarming delivered to every eligible patient, which depends on identifying eligible patients at scheduling, having warming gowns connected before patients arrive, and starting warming at the preoperative interview rather than after it. The second is warming continued without interruption into the operating room, which depends on transport with the gown attached, a standard handoff and a room temperature set before arrival for high-risk cases. The third is intraoperative warming started promptly and maintained, especially for long cases. The fourth is accurate and consistent temperature measurement on arrival in recovery. Each secondary driver generates change ideas to test.
Family of Measures
The table lists the outcome, process and balancing measures.
| Type | Measure | Definition | Target |
|---|---|---|---|
| Outcome | Hypothermia on arrival in recovery | Eligible patients with first recovery temperature below 36.0 degrees, divided by all eligible patients | 10% or less |
| Process | Prewarming delivered | Eligible patients with at least 20 minutes of documented forced-air prewarming | 90% or more |
| Process | Warming continued in transfer | Eligible patients arriving in the operating room with warming in place | 90% or more |
| Process | Intraoperative warming within 15 minutes of induction | Eligible patients with warming documented within 15 minutes | 95% or more |
| Process | Standard temperature measurement | Recovery temperatures taken within 5 minutes of arrival by the standard method | 95% or more |
| Balancing | First-case on-time starts | First cases starting within 5 minutes of schedule | No decline from 89% baseline |
| Balancing | Overheating or discomfort | Patients reporting feeling too hot or sweating in holding | Under 5% |
Why Balancing Measures Matter
Every change can have unintended effects. Prewarming might delay first cases if patients arrive late, or make some patients uncomfortably warm. Tracking first-case starts and patient comfort ensures that improvement in temperature is not bought with problems elsewhere. If a balancing measure worsens, the team will adapt the change rather than abandon the aim.
Data Collection
The quality analyst will extract outcome and process data weekly from the electronic record, using documentation fields added for prewarming start and stop times and for warming in transfer. The analyst will plot each measure on a run chart and apply standard rules for detecting non-random signals (Perla et al., 2011). Temperature measurement will be standardized before launch, so that improvement is not confused with a change in how temperatures are taken. Data will be shared at a weekly 15-minute team huddle and posted monthly in the perioperative area.
Timeline
Weeks 1 to 4 will standardize measurement, build documentation fields and train the holding area nurses. Weeks 5 to 12 will run test cycles on first cases of the day, then on orthopedic cases. Weeks 13 to 20 will spread the tested changes to all eligible cases. Weeks 21 to 26 will focus on sustaining, with the process owner named and data reviewed monthly by the perioperative quality committee. The team expects to revise this timeline as cycles reveal what works.
Ethics and Oversight
The project implements a practice supported by evidence and aligned with patient comfort, adds minimal risk and does not randomize patients, so it falls within the hospital's quality improvement oversight rather than research review. Patients will be told that warming before surgery is part of standard care. The team will monitor for adverse effects such as skin irritation from warming devices and will report the project using SQUIRE 2.0, which asks for a clear description of context, interventions, measures and ethical considerations (Ogrinc et al., 2016).
Communication
The plan will be presented to the perioperative leadership group, the surgical services committee and each unit's staff meeting. Surgeons will receive their own service's baseline data, since orthopedics carries most of the burden. A one-page summary will explain the aim, the reasons and what staff will be asked to do, and monthly updates will show progress on the run chart.
Risks to the Plan
Several risks could derail the plan. The holding area has only four forced-air units, enough for first cases but perhaps not for the whole day, so the team has asked the equipment committee to borrow units from the operating rooms during early testing. Turnover among holding area nurses could erase training, which is why prewarming will become part of the holding area's written routine. Some surgeons may resist any change that appears to threaten start times, which the balancing measure on first-case starts is designed to address with data rather than argument.
Conclusion
The plan turns evidence and data into a concrete improvement effort: an aim to reduce hypothermia from 22% to 10% in six months, a team that includes every role in the patient's path, a driver diagram that generates testable changes, and a family of measures that tracks outcome, process and unintended effects. With standardized measurement, weekly data and a timeline that expects revision, the plan is ready for the test cycles of Module 7.
References
Horn, E.-P., Bein, B., Böhm, R., Steinfath, M., Sahili, N., & Höcker, J. (2012). The effect of short time periods of pre-operative warming in the prevention of peri-operative hypothermia. Anaesthesia, 67(6), 612-617. https://doi.org/10.1111/j.1365-2044.2012.07073.x
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 6 instructions ask for
Aspen's EDN 820 description ends with quality improvement plans built from patient outcomes, and because the Module 6 instructions stay in the classroom, this example writes one plan in full. A plan like this needs a dated, specific aim, a team with the right roles, a theory of change such as a driver diagram, and measures that follow outcomes, processes and side effects. Ground the aim's target in your own data and the evidence. Define every measure with a numerator and denominator and set a target for each. Explain how data will be collected, how often and how they will be displayed. Include a timeline that expects revision, the risks to the plan and how the project fits ethical oversight.
Inside the EDN 820 Module 6 example
The plan summarizes what Modules 2 through 5 established and states the aim: 22% to 10% within six months, sustained for eight weeks. It names a team from the preoperative nurses to the transport aide, with the anesthesia chief as sponsor and a former patient as adviser. The driver diagram's four primary drivers, prewarming delivered, warming continued in transfer, prompt intraoperative warming and accurate measurement, are described with their secondary drivers. A four-column table defines one outcome, four process and two balancing measures. Sections follow on why balancing measures matter, weekly data extraction and run charts, a 26-week timeline, risks such as having only four warming units, ethical oversight under improvement rules, and communication to surgeons and staff.
Reading the EDN 820 Module 6 grading rubric
Improvement plans are assessed on the quality of the aim, a coherent theory of change, well-defined measures, practical data collection and attention to risk and ethics. This plan's aim meets each SMART criterion and ties its target to both stratified local data and published trial results. It cites three APA sources: Horn and colleagues' randomized trial of prewarming times, Perla's guide to reading run charts and the SQUIRE 2.0 guidelines. The measures table gives each measure a definition and a target, which lets instructors judge feasibility. Including balancing measures for first-case starts and patient comfort, and planning for equipment shortages and surgeon concerns, shows the anticipatory thinking that separates a doctoral plan from a list of intentions.
Common EDN 820 Module 6 mistakes, and how to avoid them
Improvement plans often state aims like improve patient warmth with no number or date. Write an aim someone could check. Another weakness is measuring only the outcome; without process measures, you cannot tell whether a failure came from the idea or its delivery. Add at least one balancing measure. Define each measure exactly, including who is counted. Plan when and how data will be collected, not only which data. Build a timeline that allows for learning. Name two or three risks and a response to each. If a driver diagram is new to you, sketch one on paper before writing, and a tutor can check that each change idea connects clearly to a driver and the aim.
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 6 questions, answered
What does EDN 820 Module 6 usually ask for?
Aspen's EDN 820 asks students to synthesize patient outcomes into quality improvement plans, so a full improvement plan with an aim and measures is a typical sixth assignment. Follow your classroom prompt.
What is a balancing measure in quality improvement?
A measure that watches for unintended effects of a change elsewhere in the system, such as delays or discomfort, so improvement in one area does not cause harm in another.
What is a driver diagram?
A diagram linking an improvement aim to the primary drivers that influence it, their secondary drivers and specific change ideas to test.
Where can I find a free EDN 820 Module 6 sample paper?
The full plan is above, with a SMART aim, a driver diagram described in prose and seven defined measures, each with its target.
What makes an improvement aim SMART?
It names exactly what will change, by how much, by when and why it matters, in a way that can be checked, such as reducing a named rate from 22% to 10% within six months.