From 27.8% to 33.4%: Three PDSA Cycles, a Run Chart and a Carefully Defined Adenoma Detection Rate at an Ambulatory Endoscopy Center
[Student Name]
University of Phoenix
HCS/451: Health Care Quality Management and Outcomes Analysis
Week 3 Assignment
[Instructor Name]
[Date]
The endoscopy center, physicians and figures are a composite written for a model paper.
In Week 1, the composite endoscopy center identified its most important quality problem: adenoma detection rates that ranged from 17% to 38% across nine physicians. This paper describes how the center measured the problem precisely, applied a structured improvement method and determined whether performance improved. An improvement effort can fail quietly in two ways: by measuring the wrong thing or by mistaking a lucky month for real change.
Defining the Indicator
Before improving a measure, the team defined it so that everyone would calculate it the same way. The numerator counts screening examinations that found one or more adenomas; the denominator counts all screening examinations performed. Colonoscopies done for symptoms, for surveillance after earlier polyps or in patients with a family history of cancer were excluded from the denominator, because those patients are more likely to have adenomas and would distort comparisons. Adenomas were counted only after the pathology report confirmed them, not from the physician's impression during the procedure. The data came from the endoscopy reporting system linked to pathology results, reviewed monthly by a quality nurse.
With this definition, the center's baseline over the prior 12 months was 27.8% for the group, with a monthly median of 27.5%.
Choosing a Method
The team used the Model for Improvement, which frames any project around three questions: the aim, the evidence that will show a change helped and the changes worth trying (Langley et al., 2009). Changes are then tested in plan-do-study-act cycles, starting small so that the team learns quickly with little risk. The center chose this method over a larger redesign because the likely changes were in physicians' technique and feedback, which are best tested one at a time.
The aim was stated precisely: raise the group's adenoma detection rate from 27.8% to at least 33% within six months, with every physician at or above 25%.
Cycle 1: Withdrawal Time
Plan: careful inspection during withdrawal of the colonoscope is associated with finding more adenomas, and the reporting system showed that the three physicians with the lowest rates had the shortest average withdrawal times. The team predicted that a visible timer and a minimum withdrawal time of 8 minutes would raise their detection rates.
Do: the three physicians agreed to use a timer displayed on the monitor for four weeks.
Study: their average withdrawal time rose from about 6 minutes to 9 minutes, and their combined detection rate rose from 21% to 26%, with the largest gain in one physician.
Act: the timer was adopted for all physicians.
Cycle 2: Physician Report Cards
Plan: physicians had never seen their own rates compared with colleagues'. The team predicted that quarterly unblinded report cards, showing each physician's rate against the group and the target, would prompt improvement.
Do: the medical director met with each physician to review the first report card, framing it as information rather than judgment.
Study: over the next two months, the group rate rose further. Two physicians asked to observe colleagues with higher rates, which the team had not planned.
Act: report cards continued quarterly, and peer observation was offered to all physicians.
Cycle 3: Computer-Aided Detection
Plan: Repici et al. (2020), in a randomized trial, found that real-time computer-aided detection, software that highlights possible polyps on the screen during colonoscopy, increased adenoma detection. The team predicted a gain and planned a one-room trial before any purchase.
Do: a vendor provided the system in one room for six weeks.
Study: detection rose in that room, but by less than in the trial, and physicians reported frequent false alerts. The team judged the result promising but not yet decisive.
Act: the team adapted the test, extending it for another two months with all physicians rotating through the room before a purchase decision.
Reading the Run Chart
Each month's group detection rate went onto a run chart, with a center line drawn at the 27.5% median from the baseline year. Before the project, monthly rates moved above and below the median with no pattern. After the first cycle, the rate stayed above the baseline median for eight consecutive months, meeting the run chart rule of six or more consecutive points on one side of the median, which signals a real shift rather than chance. The average rate over the six months after the project began was 33.4%, and the lowest-performing physician reached 26%.
Month-to-month variation remained. One month fell to 29%, still above the median. Without the run chart rules, the team might have concluded that the effort had failed that month.
Balancing Measures
Improvement in one measure can create problems elsewhere. The team tracked two balancing measures. Average procedure time rose by about three minutes, which reduced the number of procedures per room per day slightly; the center adjusted scheduling templates. The perforation and bleeding rates did not change. Tracking these measures reassured physicians and administrators that longer, more careful examinations were not causing harm.
What the Data Could Not Show
The run chart shows that the group's rate changed, but it cannot say which cycle caused how much of the change, since the cycles overlapped. Six months is also too short to show any effect on cancers, which appear years later. The team was careful to report the result as an improvement in a process measure strongly linked to outcomes, not as proof of fewer cancers.
Why This Matters for Organizational Performance
Corley et al. (2014) linked each 1% gain in a physician's detection rate to about a 3% lower risk of interval colorectal cancer. A group-wide gain of more than five percentage points therefore represents a meaningful reduction in cancer risk for the center's patients, achieved mostly through changes in technique and feedback rather than large capital spending.
Conclusion
The center improved its adenoma detection rate from 27.8% to 33.4% by first defining the indicator precisely, then testing changes in plan-do-study-act cycles and reading the results on a run chart. Two cycles were adopted and one was adapted. Balancing measures confirmed that the gains did not come at the cost of safety. Week 5 will address how the center sustains the gain.
References
Corley, D. A., Jensen, C. D., Marks, A. R., Zhao, W. K., Lee, J. K., Doubeni, C. A., Zauber, A. G., de Boer, J., Fireman, B. H., Schottinger, J. E., Quinn, V. P., Ghai, N. R., Levin, T. R., & Quesenberry, C. P. (2014). Adenoma detection rate and risk of colorectal cancer and death. New England Journal of Medicine, 370(14), 1298-1306. https://doi.org/10.1056/NEJMoa1309086
Langley, G. J., Moen, R. D., Nolan, K. M., Nolan, T. W., Norman, C. L., & Provost, L. P. (2009). The improvement guide: A practical approach to enhancing organizational performance (2nd ed.). Jossey-Bass.
Repici, A., Badalamenti, M., Maselli, R., Correale, L., Radaelli, F., Rondonotti, E., Ferrara, E., Spadaccini, M., Alkandari, A., Fugazza, A., Anderloni, A., Galtieri, P. A., Pellegatta, G., Carrara, S., Di Leo, M., Craviotto, V., Lamonaca, L., Lorenzetti, R., Andrealli, A., ... Hassan, C. (2020). Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology, 159(2), 512-520. https://doi.org/10.1053/j.gastro.2020.04.062
How this HCS 451 Week 3 example is structured
The University of Phoenix library guide for HCS/451 lists Week 3 as Organizational Performance, and many sections introduce improvement methods and the data behind a chosen indicator. The paper starts with the indicator's definition, because improvement is only as trustworthy as the measure. It then applies one improvement method in cycles, reads the data with run chart rules rather than impressions and checks that the gain did not cause harm elsewhere. Students search this week as HCS 451 Week 3, HCS451 Wk 3 or HCS/451 Wk 3; all three are the same assignment.
HCS/451 Week 3 questions, answered
What does HCS/451 Week 3 usually ask for?
The University of Phoenix library guide for HCS/451 lists Week 3 as organizational performance. Many sections ask students to describe a quality improvement method, such as PDSA, Lean or Six Sigma, and apply it with data to a performance indicator in their chosen organization.
What is a PDSA cycle?
Plan-do-study-act is a method for testing a change on a small scale. The team plans a change and predicts its effect, carries it out, studies the results against the prediction and acts by adopting, adapting or abandoning the change before the next cycle.
What is a run chart?
A graph of a measure over time with its median marked. Simple rules, such as six or more consecutive points above or below the median, help distinguish a real change from random variation.
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