| Course | EDN 820 Evidence-Based Practice and Quality Improvement |
|---|---|
| Module | Module 4 |
| Paper type | Outcome data analysis |
| Length | About 1,011 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 4
Where the Cold Patients Are: Reading Six Months of Recovery-Room Temperature Data Before Changing Practice
Student Name
Doctor of Education Program, Aspen University
EDN 820: Evidence-Based Practice and Quality Improvement
Instructor Name
Month Day, Year
Where the Cold Patients Are: Reading Six Months of Recovery-Room Temperature Data Before Changing Practice
Evidence appraised in Module 3 supports prewarming surgical patients, but evidence does not say where Allerton Community Hospital's problem is concentrated or how large the gain could be. The hospital's own outcome data can. This paper analyzes six months of first temperatures taken in the recovery room for adults whose general anesthesia lasted half an hour or more, stratifies the results, links them to related outcomes, notes the limits of the measurement and draws conclusions for the improvement plan.
Defining the Measure
The measure used throughout is the share of adults with at least 30 minutes of general anesthesia whose first recovery room temperature reads below 36.0 degrees Celsius. The numerator counts those patients; the denominator counts all eligible cases, excluding patients who were intentionally cooled or who went directly to intensive care. Defining the measure precisely, including exclusions, prevents later arguments about whether an apparent change is real or an artifact of counting differently.
The Overall Picture
From January through June, 273 of 1,240 eligible patients, 22.0%, arrived below 36.0 degrees. Monthly percentages ranged from 20% to 24% with no upward or downward pattern. Plotted as a run chart with a median of 22%, the points moved above and below the median in a way consistent with random variation around a stable level. Perla et al. (2011) present the run chart as an easy way to separate ordinary variation from genuine signals of change, and its use here establishes that the hospital's performance is stable: the process reliably produces about one hypothermic patient in five.
Stratifying the Data
An overall rate can hide where a problem lives. The table breaks the six months down by surgical service, length of anesthesia and patient age.
| Group | Cases | Below 36.0 degrees | Rate |
|---|---|---|---|
| Orthopedics | 410 | 127 | 31.0% |
| General surgery | 380 | 91 | 23.9% |
| Gynecology | 250 | 35 | 14.0% |
| Urology | 200 | 20 | 10.0% |
| Anesthesia under 60 minutes | 420 | 38 | 9.0% |
| Anesthesia 60 to 120 minutes | 520 | 114 | 21.9% |
| Anesthesia over 120 minutes | 300 | 121 | 40.3% |
| Patients aged 70 or older | 310 | 102 | 32.9% |
| Patients under 70 | 930 | 171 | 18.4% |
| All eligible cases | 1,240 | 273 | 22.0% |
What the Strata Show
Three patterns stand out. Orthopedic patients had hypothermia at three times the rate of urology patients, likely reflecting long joint replacements, cool rooms and large exposed areas. Rates rose steeply with the length of anesthesia, to 40.3% for cases over two hours. And patients aged 70 or older had a rate of 32.9%, almost double that of younger patients. Orthopedic cases, long cases and older patients overlap, so these are not three separate problems; they describe one group at high risk: older patients having long orthopedic operations.
Related Outcomes
Temperature matters because of what follows it. Recovery nurses documented shivering in 14% of hypothermic patients and 3% of others, and hypothermic patients' median time in the recovery unit was 18 minutes longer. Across trials, active warming has reduced shivering substantially (Madrid et al., 2016). Allerton's surgical site infections are too few over six months to analyze by temperature, but a landmark trial tied keeping patients warm to fewer wound infections (Kurz et al., 1996), which makes infection a reason to act even without local proof.
Cautions About the Data
Several cautions apply. Allerton measures arrival temperature with temporal artery thermometers, which can differ from core measurements, so some patients may be misclassified near the threshold. Temperatures were recorded by different nurses, and the time from arrival to measurement was not standardized. And the data do not show whether patients were warmed during surgery, only whether a warming device was documented. None of these cautions changes the overall conclusion, but they argue for standardizing measurement before judging improvement.
Common Cause, Not Special Cause
Because the process is stable, the 22% rate is not the result of bad days or individual errors that could be corrected one at a time. It is what the current system produces. Improvement therefore requires changing the system, in this case adding prewarming and strengthening intraoperative warming, rather than reminding staff to try harder. This distinction, between variation built into a process and variation from special causes, guides the choice of improvement method in Module 5.
Synthesis for the Improvement Plan
The data point to where the plan should focus and what it can achieve. If prewarming lowered rates in the highest-risk groups toward those seen in the lowest, the overall rate could plausibly fall from 22% to around 10%, which will be the aim in Module 6. The plan should prioritize orthopedic and long cases, give particular attention to older patients, standardize the timing and method of temperature measurement, and add process measures showing whether prewarming and intraoperative warming actually happen.
Questions the Data Raise
The analysis also raises questions for the team: Why are gynecology cases, often long, relatively warm? Does the difference reflect room temperature, surgical exposure or practices worth copying? How often is intraoperative warming started late? And do patients arriving from the holding area already have low temperatures? Answering these will make the plan sharper.
A Measurement Plan Going Forward
Before any change is introduced, the team will fix the weaknesses the analysis exposed. Recovery nurses will take the first temperature within five minutes of arrival using the same device type, the record will capture the device used, and holding area nurses will document the start and stop times of any warming. The quality analyst will produce the outcome measure every two weeks, stratified by service and case length, so that the team can see whether improvement reaches the high-risk groups and not only the easier ones.
Conclusion
Half a year of records shows a steady process that leaves 22% of eligible patients cold on arrival in recovery, most of them older patients having long orthopedic operations. Hypothermia also brings more shivering and longer recovery stays. The data justify a system change, point to where it should start and suggest an achievable aim, while their limits call for better measurement before and during the project.
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
Madrid, E., Urrútia, G., Roqué i Figuls, M., Pardo-Hernandez, H., Campos, J. M., Paniagua, P., Maestre, L., & Alonso-Coello, P. (2016). Active body surface warming systems for preventing complications caused by inadvertent perioperative hypothermia in adults. Cochrane Database of Systematic Reviews, (4), Article CD009016. https://doi.org/10.1002/14651858.CD009016.pub2
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
Reading the EDN 820 Module 4 assignment instructions
The last clause of Aspen's EDN 820 description asks students to synthesize patient outcomes into quality improvement plans, and that work begins with the organization's own data. With the Module 4 directions shared inside the course, this example analyzes one hospital's outcome data before an improvement project. Define your measure precisely, with numerator, denominator and exclusions. Show the data over time, not only as one average, so you can tell stable performance from change. Break results down by groups that might differ, since an overall rate can hide where the problem lives. Connect the outcome to related outcomes that matter to patients. State the limits of the data honestly, and finish by explaining what the analysis means for the plan that follows.
How the EDN 820 Module 4 example is put together
The paper opens by explaining that evidence says what to do but local data say where. It defines the outcome measure and its exclusions, then reports 273 of 1,240 eligible patients arriving cold, with monthly rates between 20% and 24% on a stable run chart. A four-column stratification table follows by service, length of anesthesia and age. The discussion identifies one high-risk group, older patients having long orthopedic operations, rather than three separate problems. Sections on related outcomes report more shivering and 18 extra minutes in recovery among cold patients, and cautions about temporal artery thermometers and unstandardized timing. The final sections explain common-cause variation, propose an aim of about 10%, list questions the data raise and set a measurement plan.
EDN 820 Module 4 rubric: what earns full marks
Outcome analyses are graded on a precise measure, sound interpretation of variation, useful stratification and conclusions that follow from the data. This example defines numerator and denominator before reporting any percentage, and the stratification table adds up to the totals, which lets instructors check it. The sources, in APA form, are Perla and colleagues' BMJ Quality and Safety guide to run charts, the Cochrane review of active warming and a landmark trial on warming and wound infection. Interpreting a stable run chart as a system property, rather than blaming individuals, shows understanding of variation that doctoral graders expect. Naming measurement weaknesses and fixing them before launch shows that the analysis is meant to guide a real project.
EDN 820 Module 4 help from the desk
Outcome papers often present one percentage for a whole year and stop there. Plot the data over time and look at the pattern before you interpret it. Stratify by at least two factors that could plausibly matter, and check that your groups add up to the total. Distinguish common-cause variation from special causes before proposing a response. Link your outcome to at least one consequence patients experience. Note how the data were collected and what could make them misleading. If you cannot get real data, build a composite from published rates and say so. When run charts are new to you, a tutor can plot your data with you and explain what a stable pattern looks like.
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 questions, answered
What does EDN 820 Module 4 usually ask for?
Aspen's EDN 820 asks students to synthesize patient outcomes into quality improvement plans, so analyzing an organization's outcome data to target improvement is a typical fourth assignment. Follow your classroom prompt.
Why stratify outcome data?
Breaking results down by groups such as service, age or case length shows where a problem is concentrated, which an overall rate can hide.
What does a stable run chart mean for improvement?
That performance reflects how the process is designed, so improving it requires changing the system rather than correcting individual lapses.
Where can I find a free EDN 820 Module 4 sample paper?
The complete analysis is above, covering six months of recovery-room temperatures for 1,240 patients with a table stratifying results by service, case length and age.
What does it mean if an outcome is stable on a run chart?
That the process is producing a consistent result, so improving it requires redesigning the process rather than correcting individual cases.