Early drop-off, clustered among patients with no contact in the first four weeks, is the pattern an MN667 Unit 8 analysis finds across a composite practice's antidepressant starts. Searches like "mn 667 unit 8 assignment example", "mn667 unit 8 sample" and "mn667 unit 8 example" land here.
Where Patients Leave: A Quality Analysis of Antidepressant Continuation in a Community Practice
[Student Name]
Purdue University Global
MN667: PMHNP Diagnosis and Management Across the Lifespan III
Unit 8 Assignment
[Instructor Name]
[Date]
Composite practice written as a model document. All figures are composite and bracketed; no patient is identifiable.
Part 1: The Measure, Defined First
The analysis borrows the logic of the HEDIS Antidepressant Medication Management measure, which counts adults with a new diagnosis of major depression who start an antidepressant and remain on it for two phases: an acute phase of at least 84 days, or 12 weeks, and a continuation phase of at least 180 days, or 6 months (National Committee for Quality Assurance, n.d.). The practice applied the same two thresholds to its own patients.
Source. The practice's figures come from pharmacy fill data linked to the practice's prescriptions, for adults who started an antidepressant between [January] and [December] of last year: [112] patients. At 84 days, [61] had continuous fills, [54 percent]. At 180 days, [38] had continuous fills, [34 percent].
Blind spot. A fill proves that a prescription was collected, not that the medicine was taken. The measure will overstate adherence in patients who fill and do not take, and understate it in patients who use a pharmacy not linked to the data. The analysis treats the figures as a floor on dropout, not a complete count.
The problem is not unique to this practice. In a national sample of adults starting antidepressants for depression, about 42 percent stopped within the first 30 days, and only about 28 percent continued for more than 90 days (Olfson et al., 2006).
Part 2: The Run Chart
A run chart plots the monthly 84-day continuation rate for patients starting in each of the last [twelve] months. The rate varies between about [45] and [62 percent] and shows no run of six points above or below the median and no trend of five points rising or falling. It is stable and low.
That matters. A stable process is producing the results it was designed to produce. A falling rate would prompt a search for what changed; a stable, low rate calls for redesigning the process itself. There is no single bad month to blame.
Part 3: The Four-Week Split
The central finding comes from stratifying patients by whether anyone in the practice saw or called them within four weeks of starting.
Contacted within four weeks: [49] patients. Continuing at 84 days: [36], [73 percent].
Not contacted within four weeks: [63] patients. Continuing at 84 days: [25], [40 percent].
The gap between those groups is large. The analysis states outright that an association between early contact and continuation does not prove that the contact caused it. Patients who are contacted may differ in other ways: they may have been more severely ill and so scheduled sooner, or more engaged from the start. But the direction of the gap is consistent with what patients report elsewhere, that early side effects and doubts about whether the medicine is working lead many to stop in the first weeks.
Part 4: Cause Analysis
A fishbone diagram groups possible causes under four headings, drawn from a staff meeting and from a review of [twenty] charts of patients who stopped.
Scheduling: follow-up visits booked six to eight weeks after starting, because appointments are scarce; no routine call in between.
Patient information: no written sheet explaining that benefit takes weeks and that early side effects often fade.
Side effects: nausea, sleep changes and sexual side effects in the first weeks, with no easy way to ask about them between visits.
Cost: copays for patients on high-deductible plans, and some patients not refilling after the first month. Several charts recorded a patient saying the first fill cost more than expected, which suggests that price was sometimes discovered at the pharmacy counter rather than discussed at the visit. The practice cannot lower those prices, but it can ask about coverage before prescribing.
The four-week split points most directly to the first and third causes: patients who hear nothing for weeks may stop at the first side effect without anyone knowing.
Part 5: The Aim and the Change
Aim: increase the 84-day continuation rate among adults newly started on an antidepressant from [54 percent] to [65 percent] by [June 30 of next year].
Change to test: a scripted nurse call two weeks after the first prescription, asking about side effects, answering questions and offering an earlier visit if needed.
The change is framed in the Model for Improvement, built on three questions about the goal, the evidence of improvement and the change to try, followed by small cycles of testing (Langley et al., 2009). The first plan-do-study-act cycle runs at small scale: one prescriber's patients for [six weeks].
Measures:
Outcome: 84-day continuation for patients started during the test.
Process: percentage of patients who received the call within 14 days.
Balancing: nurse time per call, tracked in minutes, to see whether the change pulls time from other work.
Why This Change First
Of the four causes on the fishbone, scheduling and side effects are the two the practice can change within weeks without new money. Patient information sheets are worth adding, but on their own they do not open a conversation when a side effect appears. Cost matters to some patients, but the practice cannot change deductibles. A call at two weeks addresses both of the first two causes at once: it arrives before the usual follow-up visit, and it gives the patient a chance to report nausea or poor sleep before deciding alone to stop. The two-week timing was chosen because staff reported that most patients who called with side effects did so in the second or third week.
What the Change Might Cost
Nurse calls take time. If each takes ten minutes and the practice starts about [nine] patients a month, the change costs about [ninety] minutes a month at full scale. The balancing measure will show whether that estimate holds, and whether the calls crowd out other nursing tasks. The analysis also notes that a call may reveal problems that need a visit, which adds demand for scarce appointments.
What the Analysis Can and Cannot Claim
The analysis can claim that continuation in this practice is stable and low, and that patients contacted early continue more often. It cannot claim that early contact causes continuation, or that the nurse call will work. The small test is designed to find out. If continuation improves for that prescriber's patients without an unacceptable cost in nurse time, the next cycle extends the call to a second prescriber. If it does not, the team returns to the fishbone and tests a different cause.
References
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.
National Committee for Quality Assurance. (n.d.). Antidepressant medication management (AMM). National Committee for Quality Assurance.
Olfson, M., Marcus, S. C., Tedeschi, M., & Wan, G. J. (2006). Continuity of antidepressant treatment for adults with depression in the United States. American Journal of Psychiatry, 163(1), 101-108. https://doi.org/10.1176/appi.ajp.163.1.101
How this MN667 Unit 8 example is structured
The analysis moves from measurement to explanation to one test, and each step limits what the next may claim. The measure is defined before any number appears, including its source and its blind spot: a fill proves collection, not ingestion. The run chart shows a rate that is stable and low rather than falling, which matters, because a stable process calls for redesign rather than a hunt for one bad month. Stratification then carries the explanation, and the paper states outright that an association between early contact and persistence does not prove the contact caused it. The change is framed in the Model for Improvement: an aim, outcome and process measures, a balancing measure in nurse time, and a plan-do-study-act cycle at small scale before any practice-wide rule. No individual patient is identifiable anywhere in the paper.
Get an MN667 Unit 8 example written to your instructions
What pattern is your MN667 Unit 8 prompt pointing at: continuation, follow-up after discharge, monitoring rates or something else? Name it, attach whatever data the case provides and the rubric, and within 24-48h a free first analysis arrives, its measure defined first, the pattern stratified and one small test of change planned. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
MN667 Unit 8 questions, answered
Can a practice quality analysis use invented data?
It should use the data the case provides, or clearly labeled composite figures, and say so. Keep numbers bracketed and consistent across the chart, tables and text. What instructors assess is the reasoning: whether the measure is defined, the pattern read correctly and the proposed change testable. Presenting composite figures as a real practice's results would misrepresent the work.
Why test a change on a small scale first?
A small test shows quickly whether the idea works in this setting and what it costs, before it becomes a rule for everyone. The Model for Improvement uses short plan-do-study-act cycles for exactly this reason. Say what the first cycle will measure, how long it runs and what result would lead you to adapt, adopt or abandon the change.
What is a balancing measure?
A measure of what the change might make worse elsewhere. If a nurse call improves continuation, the balancing measure might be nurse time, or delays in returning other calls. Including one shows the analysis considered the whole system, not just the target. Name it, say how it will be collected and state what level would make the change unsustainable.