| Course | HA 640 Healthcare Operations Assessment and Decision-Making |
|---|---|
| Unit | Unit 6 |
| Paper type | Decision matrix |
| Length | About 1,020 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | MS in Healthcare Administration |
| Updated | October 2026 |
Free sample paper for HA 640 Unit 6
Five Ways to Fix the Morning Peak, Including Doing Nothing: A Weighted Decision Matrix in Which Every Score Points to Its Evidence
Student Name
Health Care Administration, Herzing University
HA 640: Healthcare Operations Assessment and Decision-Making
Instructor Name
February 19, 2026
Five Ways to Fix the Morning Peak, Including Doing Nothing: A Weighted Decision Matrix in Which Every Score Points to Its Evidence
The Alternatives
The capacity and queue analysis produced four options for relieving the specimen processing bench between 5 and 7 a.m. They are genuinely different: one adds people, one changes when work arrives, one makes each tube faster, and one replaces manual handling with equipment. The matrix adds a fifth, doing nothing, because the comparison is only honest if the current state has to compete.
Do nothing: keep two processors and the current draw schedule. Option A: staff a third processor on a daily 4:30-to-8:30 shift. Option B: split morning draws so that routine daily draws for stable patients are collected at 7 to 8 a.m., and add a processor from 7 to 9 a.m. to absorb the shifted volume. Option C: bedside barcode labeling on inpatient units and an automated result-status message. Option D: pre-analytic automation.
Criteria, Defined
Morning turnaround, weight 30%: the projected median wait before accessioning for peak-window tubes in February, the busiest forecast month. This carries the most weight because it is the problem the decision exists to solve.
Annual cost, weight 25%: operating cost plus capital spread over its useful life, in dollars a year.
Implementation difficulty, weight 15%: time to full effect and the number of departments whose agreement is required.
Staff impact, weight 15%: the effect on processor workload and breaks, and on nursing and phlebotomy workload.
Quality and safety, weight 15%: the expected effect on pre-analytic errors and on patients.
The weights total 100% and were agreed by the laboratory director, the chief nursing officer's designee and a hospitalist representative before any option was scored. Scores run from 1, worst, to 5, best.
The Matrix
Weighted total = 0.30 × turnaround + 0.25 × cost + 0.15 × difficulty + 0.15 × staff + 0.15 × quality.
| Alternative | Turnaround (30%) | Cost (25%) | Difficulty (15%) | Staff (15%) | Quality (15%) | Weighted total |
|---|---|---|---|---|---|---|
| Do nothing | 1 | 5 | 5 | 1 | 2 | 2.75 |
| A. Third processor, 4:30 to 8:30 | 5 | 3 | 4 | 4 | 3 | 3.90 |
| B. Split draws plus processor 7 to 9 | 4 | 4 | 2 | 3 | 4 | 3.55 |
| C. Barcode labeling and status message | 2 | 3 | 3 | 3 | 5 | 3.00 |
| D. Pre-analytic automation | 5 | 1 | 1 | 4 | 4 | 3.10 |
The Evidence Behind Each Score
Do nothing. Turnaround 1: the queue analysis projects a median wait of about 40 minutes before accessioning in February. Cost 5: no added cost. Difficulty 5: nothing to implement. Staff 1: processors already skip breaks on most mornings and answer about five calls an hour. Quality 2: tubes waiting over 60 minutes approach time limits for some tests.
Option A. Turnaround 5: capacity rises to 195 an hour and the calibrated median wait to about 4 minutes in February, estimated with a multi-server queueing model of the kind widely used for health care capacity questions (Green, 2006). Cost 3: $41,756 a year. Difficulty 4: one part-time hire, about six weeks to recruit and train, laboratory only. Staff 4: breaks restored; no change for nursing. Quality 3: no change to labeling errors.
Option B. Turnaround 4: peak-window arrivals drop to roughly 102 an hour, with a calibrated wait of about 5 minutes; routine draws are resulted later by design. Cost 4: $20,878 a year for the 7 to 9 a.m. processor. Difficulty 2: requires medical staff agreement on which patients qualify, order set changes and a revised phlebotomy schedule, three departments and probably three months. Staff 3: phlebotomists' 7 to 8 a.m. hour becomes busier. Quality 4: stable patients are no longer woken at 4 or 5 a.m. for draws that do not drive a morning decision.
Option C. Turnaround 2: capacity rises only to about 141 an hour, still below the peak, so the queue shrinks but persists. Cost 3: a one-time $88,000 with $6,000 in yearly supplies and support, about $23,600 a year over five years. Difficulty 3: 22 units and information services, about four months. Staff 3: fewer exception calls for processors, but new labeling steps for nurses. Quality 5: bedside labeling is expected to roughly halve labeling exceptions, the most common pre-analytic error type (Plebani, 2006).
Option D. Turnaround 5: handling capacity above 400 an hour. Cost 1: $450,000 over seven years plus $38,000 service, about $102,300 a year. Difficulty 1: eight to ten months including space renovation. Staff 4: less repetitive handling. Quality 4: automated handling reduces sorting and aliquoting errors.
Sensitivity
Option A ranks first at 3.90 and Option B second at 3.55. Three tests were run. If cost were weighted 35% and turnaround 20%, as finance suggested, Option A would score 3.70 and Option B 3.55; the ranking holds, and doing nothing would rise to 3.15, still fourth of five. If quality were weighted 25% and turnaround 20%, Option A would score 3.70, Option B 3.55 and Option C 3.30; the ranking of the top two holds. If medical staff agreement on Option B came quickly, raising its difficulty score from 2 to 4, it would score 3.85, close to but still below Option A. The top ranking therefore depends mainly on the turnaround weight and on how quickly Option B could be agreed, and the recommendation should say so. Weighted scoring of this kind is a standard way to make such trade-offs visible (Thokala et al., 2016).
Limits of the Matrix
A matrix is only as good as its scores, and three limits apply here. The turnaround scores rest on the calibrated queueing estimates, which assume that tubes will keep arriving in bursts of the current size. The cost of Option D assumes the vendor's quoted price and a seven-year life, both of which could change in negotiation. And the difficulty score for Option B is a judgment about how long medical staff agreement would take, the one score no data can settle in advance. None of these limits favors a particular option, but the decision makers should know which scores are measured and which are estimated.
What the Matrix Suggests
Option A is the strongest single choice. Options A and C are complementary rather than competing: A addresses capacity at once, and C addresses the error loop that consumes about 11% of processor time and puts patients at risk of mislabeled results. Option B remains attractive if the medical staff will agree, because it lowers cost and spares stable patients early-morning draws. The final recommendation, which weighs sequencing and risk, follows the project plan in the next unit.
References
Green, L. V. (2006). Queueing analysis in healthcare. In R. W. Hall (Ed.), Patient flow: Reducing delay in healthcare delivery (pp. 281-307). Springer. https://doi.org/10.1007/978-0-387-33636-7_10
Plebani, M. (2006). Errors in clinical laboratories or errors in laboratory medicine? Clinical Chemistry and Laboratory Medicine, 44(6), 750-759. https://doi.org/10.1515/CCLM.2006.123
Thokala, P., Devlin, N., Marsh, K., Baltussen, R., Boysen, M., Kalo, Z., Longrenn, T., Mussen, F., Peacock, S., Watkins, J., & Ijzerman, M. (2016). Multiple criteria decision analysis for health care decision making: An introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force. Value in Health, 19(1), 1-13. https://doi.org/10.1016/j.jval.2015.12.003
HA 640 Unit 6 instructions, in plain terms
Unit 6 asks you to build a weighted decision matrix to choose among operational options. Expect to define the alternatives, define criteria with weights, present the matrix, explain the evidence behind each score, test sensitivity, state limits and say what the matrix suggests. Students often ask how to keep scores from being arbitrary. Define each criterion so it can be measured, such as projected median wait in the busiest month. Point every score to a source from earlier analysis. Include doing nothing, since it is always an option. Explain why the top criterion carries the most weight. Test the weights stakeholders might prefer. Note where options combine rather than compete.
How the HA 640 Unit 6 example is put together
The matrix opens with five alternatives described in a sentence each. The criteria section defines turnaround, cost, difficulty, staff effect and quality, with weights and the reason for each. A table shows scores and weighted totals. The evidence section takes each option in turn and gives the source of every score, from queue estimates to vendor quotes and staff observations. The sensitivity section reruns totals under finance's weights, a heavier quality weight and equal weights, and reports which ranks hold. The limits section notes that turnaround scores assume current arrival bursts and that automation costs rest on a quote. The final section recommends the third processor with bedside labeling as a complement.
Where the points sit in the HA 640 Unit 6 rubric
Herzing's rubric for a decision matrix typically covers alternatives, criteria and weights, the matrix, evidence for scores, sensitivity, limits and conclusions. Tying every score to evidence earns the most credit, since a matrix with unsupported numbers simply dresses up a preference. Alternative points reward genuinely different options, including doing nothing. Criteria should be measurable, with weights explained. Matrix credit goes to correct arithmetic. Sensitivity points reward weights tested from stakeholders' views. Limits should name assumptions that could change scores. Conclusions should note options that work together, such as added staff paired with fewer labeling errors, rather than forcing a single winner. Thokala's guide to multicriteria decision analysis, Green on queueing and Plebani on lab errors support the matrix.
HA 640 Unit 6 help: mistakes that cost points
Decision matrices in HA 640 Unit 6 run into trouble when scores appear with no evidence. Another frequent problem is leaving out doing nothing. Students often define criteria vaguely, such as effectiveness. Weights are not explained. Arithmetic errors change the ranking. Sensitivity is skipped, or tests only trivial changes. Options that could be combined are treated as rivals. Limits are not stated. Staff effects are ignored, though processors skipping breaks and fielding calls every morning is a cost the budget never shows. Finally, show where each number came from, because graders reward a matrix that a skeptical committee member could audit cell by cell and still reach the same choice.
Get a HA 640 Unit 6 example written to your instructions
Send the Unit 6 instructions and the rubric from your HA 640 classroom, plus the decision, the alternatives and any criteria your section has specified. We write a custom example with criteria defined first, weights justified, every score evidenced, the totals computed and the ranking tested for sensitivity, returned in 24 to 48 hours. The first custom sample is free.
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HA 640 Unit 6 questions, answered
How many criteria and alternatives should the matrix hold?
Enough to make the decision real and few enough to defend each one. Three or four alternatives and five or six criteria is a workable shape, since every cell needs an evidence note and a matrix of forty cells becomes unsupportable quickly. If a criterion would score the same for every option, drop it, because it cannot influence the ranking and only adds weight to be explained.
Where do the weights come from?
From something you can point at. Strategic priorities the organization has published, a regulatory requirement that cannot be traded away, a budget constraint, or a stakeholder process you describe are all defensible sources. What is not defensible is a set of percentages with no origin, because the weights determine the answer and a reader has no way to evaluate a ranking whose inputs appeared from nowhere.
What if my preferred option does not win?
Then you have learned something and the document gets stronger, not weaker. Report the ranking the model produced, say why you expected otherwise, and use the sensitivity section to show what would have to change for your preference to prevail. Adjusting weights until the favorite wins is visible to any experienced reader and costs far more credit than an honest surprise.
Where can I find a free HA 640 Unit 6 sample paper?
Read the complete HA 640 Unit 6 decision matrix here at no charge, five laboratory options scored on weighted criteria with evidence for each score. Ask for a custom matrix for another decision.
How do you build a weighted decision matrix?
List real alternatives including doing nothing, define measurable criteria with weights, score each option from evidence, total the weighted scores and test different weights. This sample does that for a lab bottleneck.