| Course | MMHA 6475 Program Planning, Implementation, and Evaluation |
|---|---|
| Week | Week 9 |
| Paper type | Comparison group evaluation design |
| Length | About 1,030 words, 6 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | Master of Healthcare Administration (MHA) |
| Updated | October 2026 |
Free sample paper for MMHA 6475 Week 9
Did Hospital at Home Make the Difference? A Comparison Group Evaluation Design for Tamarack Health
Student Name
Walden University
MMHA 6475: Program Planning, Implementation, and Evaluation
Instructor Name
Month Day, Year
Did Hospital at Home Make the Difference? A Comparison Group Evaluation Design for Tamarack Health
Purpose
This design sets out how Tamarack Health will judge whether its hospital-at-home program changed outcomes at the flagship campus. A simple comparison of results before and after launch would not answer that question, because many things change at once: admission volumes rise and fall, discharge practices improve, a bad flu season arrives. The design therefore uses comparison groups and time trends to separate the program's effect from those other changes. Indicators are defined first, then the design for each, then the threats that could still mislead.
Indicators
Table 1 defines the six indicators, their sources and their baseline periods. Baseline values are slots to be filled from Tamarack's records for the year before launch.
Table 1
Outcome Indicators
| Indicator | Definition | Source | Baseline |
|---|---|---|---|
| 30-day readmission | Any unplanned return to a Tamarack inpatient bed in the 30 days after an episode ends, pilot conditions only | Discharge abstract and readmission file | Slot: [rate, 12 months before launch] |
| Escalation | Transfer from home back to the hospital during an episode | Command center log | Not applicable before launch |
| Length of acute episode | Days from admission decision to discharge, home or hospital | Program registry; discharge abstract | Slot: [median for inpatients with pilot conditions] |
| Patient experience | Overall rating on the post-discharge survey | Survey vendor | Slot: [inpatient rating, same conditions] |
| Cost per episode | Direct cost of the acute episode | Cost accounting system | Slot: [inpatient direct cost, same conditions] |
| Boarding hours | Monthly median wait, admission order to leaving the emergency department, medical admissions | Emergency department tracking system | Slot: [24 monthly values before launch] |
Patient-Level Outcomes: Difference-in-Differences
Readmission and cost per episode are compared using a difference-in-differences design. The flagship campus, where the program launches, is compared with Tamarack's sister campus, which serves a similar population, treats the same five conditions and has no home program. For each campus, the analyst computes the change in readmission rate and in cost per episode for patients with the five conditions, from the year before launch to the first year after. The program's estimated effect is the change at the flagship campus minus the change at the sister campus. Anything that affects both campuses equally, such as a new readmission policy across the system or a regional flu season, cancels out (Dimick & Ryan, 2014).
The design rests on an assumption: that without the program, the two campuses' trends would have moved in parallel. The analyst tests this by comparing the campuses' trends over the two years before launch. If they diverged before the program began, the design loses much of its force and the estimate becomes a rough guide only. The comparison is made for all patients with the five conditions at each campus, not only for those treated at home, because comparing home patients with inpatients would mix the program's effect with the selection of healthier patients for home care.
Campus-Level Outcome: Interrupted Time Series
Boarding hours are a campus-level measure, so they are analyzed as an interrupted time series. Monthly median boarding hours for admitted medical patients at the flagship campus are plotted for 24 months before launch and 12 months after. Segmented regression estimates whether the launch was followed by a change in the level of boarding, a step down, or in its slope, a change in the trend (Wagner et al., 2002). Because boarding is strongly seasonal, the model includes month-of-year effects. The sister campus's boarding series is analyzed in parallel as a check: if both campuses show the same change at the same time, something other than the program is likely responsible.
Twelve months after launch is a short series, and the program's census will still be growing during part of it. The design therefore treats the first year's boarding analysis as preliminary and plans a second analysis at 24 months.
Program Outcomes Without a Comparison
Escalation has no comparison group, since inpatients cannot be escalated back to the hospital. It is read against the threshold set in the Week 3 objectives and against rates reported by established programs. Patient experience is compared with ratings from inpatients with the same conditions at the flagship campus during the same period, with the caution that patients who chose home care may differ in their expectations. Length of the acute episode is reported descriptively. These outcomes are monitored, not tested for causal effect, and the report will say so.
Threats to Validity
Selection. Patients treated at home are chosen by clinicians and agree to go, so they differ from inpatients. The design addresses this by comparing whole campus populations rather than home patients with inpatients.
Non-parallel trends. Something could shift at the sister campus during the year, such as a new hospitalist group. Pre-launch trends are checked, and major changes at either campus during the evaluation year are recorded.
Co-interventions. If the flagship campus also opens an observation unit or changes discharge processes during the evaluation, effects will be hard to separate. The steering group will log every capacity initiative at both campuses.
Small numbers. The pilot may produce few episodes, and readmission is a relatively rare event, so estimates will be imprecise. The report will give confidence intervals and avoid conclusions the data cannot support.
Contamination. Hospitalists at the sister campus may learn from the program and change their own practice, narrowing the difference between campuses. This would bias results toward finding no effect.
Fidelity. If the program is not delivered as designed, a null result would reflect implementation, not the model. The Week 8 fidelity data will be reported alongside outcomes so that readers can tell the difference (Proctor et al., 2011).
Reporting
Results will be reported to the steering group and the quality committee at 12 months, with the boarding analysis repeated at 24 months. The report will present each indicator with its comparison, the size of the estimated effect and its uncertainty, followed by the fidelity results and a plain statement of what the evaluation can and cannot conclude. A program that shows safe outcomes and lower cost but no change in boarding would still be valuable, and the report should say so rather than treating boarding as the only test of success.
References
Dimick, J. B., & Ryan, A. M. (2014). Methods for evaluating changes in health care policy: The difference-in-differences approach. JAMA, 312(22), 2401-2402. https://doi.org/10.1001/jama.2014.16153
Proctor, E., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G., Bunger, A., Griffey, R., & Hensley, M. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 38(2), 65-76. https://doi.org/10.1007/s10488-010-0319-7
Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299-309. https://doi.org/10.1046/j.1365-2710.2002.00430.x
What the MMHA 6475 Week 9 instructions ask for
Evaluation design assignments ask you to plan how you will know whether a program caused the changes you observe. Define each outcome indicator with its source and baseline period. Choose a design for each indicator that fits its level: patient outcomes can often be compared across sites and periods, while campus-wide measures may call for a time series. Explain the assumption each design rests on and how you will check it. Say which outcomes you will only monitor, without claiming causal effect. List the threats to validity specific to your program and what the design does about each. Close with when and to whom results will be reported, and how you will describe what the evaluation can and cannot conclude.
How the MMHA 6475 Week 9 example is put together
The purpose section explains why a before-and-after comparison is not enough. Table 1 defines six indicators with sources and slots. The difference-in-differences section compares the flagship and sister campuses for all patients with the five conditions, cites Dimick and Ryan, and explains the parallel trends check. The time series section describes 24 months before and 12 after, segmented regression and a parallel check at the sister campus, citing Wagner and colleagues. A section explains why escalation, experience and episode length are monitored only. The threats section covers selection, non-parallel trends, co-interventions, small numbers, contamination and fidelity, citing Proctor and colleagues. A reporting section sets the 12- and 24-month reviews.
Reading the MMHA 6475 Week 9 rubric
Evaluation designs are graded on causal reasoning. Choosing designs that address the main alternative explanations earns the largest share, and naming the assumption each design rests on is essential. Clear indicator definitions with sources and baselines earn the first share. Matching designs to the level of each indicator shows sophistication. Honest separation of monitored outcomes from tested ones is credited. A threats section specific to the program, with responses, carries significant weight. A realistic reporting plan completes the design. Tables and accurate methods citations in APA style supply the last few points. Designs that admit their own weaknesses openly earn more trust than designs that claim to settle everything.
Common MMHA 6475 Week 9 mistakes, and how to avoid them
Many evaluation plans compare outcomes before and after launch and stop there. Ask what else could explain a change and choose a design that rules it out. If you have a comparison site, check that its trends matched yours before launch. Compare whole populations rather than program patients with non-program patients when selection is likely. Use a time series for measures that apply to a whole unit or campus. Don't claim causal effects for outcomes you can only monitor. List threats that apply to your program, not a generic list. Plan for imprecise results with small numbers. We can design an evaluation for your program with comparison groups suited to your organization's data.
Get a MMHA 6475 Week 9 example written to your instructions
Forward the Week 9 prompt and rubric with your objectives and fidelity plan, and the indicators are drawn from them. The sister campus, the time series and every baseline in the sample belong to a made-up system, so values arrive as slots for data you are allowed to use. It returns in 24-48h, free the first time.
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MMHA 6475 Week 9 questions, answered
Where can I find a free MMHA 6475 Week 9 sample paper?
The comparison group evaluation design is posted here in full at no cost.
How does the MMHA 6475 Week 9 design evaluate readmission and cost?
By difference-in-differences, comparing changes at the launch campus with changes at a sister campus without the program.
How are boarding hours analyzed?
As an interrupted time series of monthly values, using segmented regression with seasonal effects.
Why compare whole campus populations rather than home patients with inpatients?
Patients chosen for home care differ from inpatients, so a direct comparison would confuse selection with effect.
Which threats does the design address?
Selection, non-parallel trends, co-interventions, small numbers, contamination and poor fidelity.