| Course | HA 640 Healthcare Operations Assessment and Decision-Making |
|---|---|
| Unit | Unit 4 |
| Paper type | Capacity and queue analysis |
| Length | About 940 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 4
Two Hours Over Capacity Every Morning: A Capacity and Queue Analysis of the Specimen Processing Bench, With Four Priced Options
Student Name
Health Care Administration, Herzing University
HA 640: Healthcare Operations Assessment and Decision-Making
Instructor Name
February 5, 2026
Two Hours Over Capacity Every Morning: A Capacity and Queue Analysis of the Specimen Processing Bench, With Four Priced Options
Capacity and Arrivals on the Same Clock
Specimen processing capacity depends on who is working. Each processor handles a tube in 0.90 minutes on average, about 67 tubes an hour, reduced to about 65 during the morning by phone calls from units. The early block, 4 to 8 a.m., is staffed with two processors; every other hour has one. Capacity is therefore about 130 tubes an hour from 4 to 8 a.m. and about 65 at other times. The table lays capacity over the arrivals measured in the baseline and the February 2026 forecast, the month the demand forecast projects as busiest.
| Hour | Capacity, tubes per hour | Arrivals, November baseline | Arrivals, February forecast | Arrivals exceed capacity? |
|---|---|---|---|---|
| 4:00 to 5:00 a.m. | 130 | 45 | 47 | No |
| 5:00 to 6:00 a.m. | 130 | 150 | 157 | Yes |
| 6:00 to 7:00 a.m. | 130 | 155 | 163 | Yes |
| 7:00 to 8:00 a.m. | 130 | 94 | 99 | No |
| 8 a.m. to 4 p.m. | 65 | 25 | 26 | No |
| 4 p.m. to 4 a.m. | 65 | 8 to 12 | 9 to 13 | No |
The Overloaded Hours
Between 5 and 7 a.m., arrivals exceed capacity, so no steady queue exists: the queue simply grows. Standard queueing formulas assume that capacity exceeds arrivals and do not apply in these hours (Green, 2006). A deterministic running count is used instead. Starting with no backlog at 5:00 a.m., the queue grows by 20 tubes in the first hour (150 arrive, 130 processed) and 25 in the second, reaching 45 tubes at 7:00 a.m. From 7:00 to 8:00 a.m., capacity exceeds arrivals by 36 an hour, leaving 9 tubes at 8:00 a.m.; with one processor after 8:00 a.m., those clear by about 8:15. A tube arriving at 6:59 a.m. waits behind 45 others, about 21 minutes at 130 an hour, before any randomness is added.
In February, at the forecast rates, the queue reaches 27 tubes at 6:00 a.m. and 60 at 7:00 a.m., and clears at about 8:45. The tube arriving just before 7:00 a.m. then waits about 28 minutes before randomness, and the median wait for peak tubes would be expected to rise from the baseline's 31 minutes to about 40.
The Hours Below Capacity, and Why Measured Waits Exceed the Model
Where capacity exceeds arrivals, the multi-server queueing model can estimate waiting. It assumes random, independent arrivals and variable service times. The tube station does not deliver tubes that way: tubes arrive in bursts of 10 to 20 when a carrier is unloaded. That burstiness raises waits above what the model predicts, since waiting rises with the variability of arrivals as well as with utilization (Hopp & Spearman, 2008). The model was therefore calibrated against the off-peak period: with one processor, 25 arrivals an hour and 65 capacity, it predicts a wait of about 0.6 minutes, while the measured median is 2 minutes. Model estimates below are multiplied by 3.5, the ratio of measured to modeled wait, and presented as calibrated estimates. The assumption is that burstiness will be similar under each option, which holds as long as the tube station and carrier size are unchanged.
Operational Effects of the Overload
The overload has visible effects. Physicians rounding at 7:00 a.m. find morning results missing for about a third of their patients, and delay decisions on potassium replacement, anticoagulant dosing and discharges until results post. Units phone the laboratory, about five calls an hour, which the processors answer, further reducing capacity. Processors skip or shorten breaks between 5:00 and 8:00 a.m. on most days. And tubes waiting longer than 60 minutes before centrifugation begin to approach the time limits for some tests, raising the risk of results affected by the delay. Against the commonly suggested goal of completing 90% of routine tests within 60 minutes (Hawkins, 2007), the peak hours miss by a wide margin.
Four Options, Each Stated as a Change in Capacity or Arrivals
Option A: add a third processor from 4:30 to 8:30 a.m. every day. Capacity between 5 and 7 a.m. rises from 130 to 195 tubes an hour. At 152 arrivals an hour, utilization is 78% and the calibrated median wait before accessioning falls to about 3 minutes; at February's rate of 160, about 4 minutes. Cost: 1,460 hours a year at a loaded rate of $28.60 an hour, $41,756 a year.
Option B: change the arrival pattern. Divide morning draws into rounds-critical draws, collected on the current schedule, and routine daily draws for stable patients whose results do not drive a morning decision, collected at 7:00 to 8:00 a.m. by the day phlebotomy shift. If one-third of the 305 peak tubes move later, arrivals between 5 and 7 a.m. fall to about 102 an hour and rise to about 196 from 7:00 to 8:00 a.m. At 102 an hour, utilization of the two processors is 78% and the calibrated wait about 5 minutes; but the 7:00 to 8:00 a.m. hour then becomes overloaded unless the third processor is added for that hour. Cost: minimal in dollars; requires order set changes and agreement from medical staff on which patients qualify.
Option C: reduce processing time per tube. Bedside barcode labeling on inpatient units would cut labeling exceptions from 1.7% to about 0.8%, and an automated result-status message in the electronic record would remove most phone calls. Processing time falls to about 0.85 minutes, raising capacity to about 141 tubes an hour, still below the morning peak. Cost: barcode printers and scanners for 22 units, about $88,000 once, plus $6,000 a year in supplies and support.
Option D: pre-analytic automation, an automated sorter, decapper and centrifuge line. Capacity for the handling steps rises above 400 tubes an hour, with processors accessioning only, at about 0.45 minutes per tube, 133 an hour each and 266 with two. Cost: about $450,000 in capital and $38,000 a year in service, with eight to ten months to install. The decision matrix in a later unit compares these options on equal criteria.
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
Hawkins, R. C. (2007). Laboratory turnaround time. The Clinical Biochemist Reviews, 28(4), 179-194.
Hopp, W. J., & Spearman, M. L. (2008). Factory physics (3rd ed.). McGraw-Hill/Irwin.
The Herzing HA 640 Unit 4 instructions
Unit 4 asks you to analyze capacity and queues at a constrained step and compare options. Expect to align capacity and arrivals by hour, analyze overloaded hours, model hours below capacity, describe operational effects and state each option as a change in capacity or arrivals with its cost. Students often ask when queueing formulas apply. Standard models assume capacity exceeds arrivals; when it does not, the queue simply grows, so use a running count. Where formulas apply, calibrate them to measured waits and explain the difference, such as bursty arrivals. Describe effects in terms leaders care about, such as delayed decisions. Express each option in tubes per hour so results can be compared.
How this HA 640 Unit 4 example is built
The analysis opens with processing time per tube and staffing by hour. The overloaded hours section shows the backlog building from 5:00 a.m. through a deterministic count. The below-capacity section applies a multi-server model and explains why burst unloading of carriers makes real waits longer than the model predicts. The effects section describes physicians delaying potassium replacement, anticoagulant dosing and discharges, and processors interrupted by calls. The options section states each choice as capacity or arrival change: a third processor from 4:30 to 8:30 a.m., cross-trained staff, staggered draws and reducing calls. Each option shows utilization, expected wait at current and February volumes and annual cost.
HA 640 Unit 4 rubric: what full points look like
Herzing's rubric for a capacity and queue analysis typically covers capacity, arrivals, the overload, queue modeling, effects and options. Using the right method for overloaded hours and calibrating the model to measured waits carry the most weight, because misapplied formulas give confident but wrong answers. Capacity points reward hourly staffing and processing rates. Overload criteria look for a running count rather than a steady-state formula. Model credit goes to stated assumptions and calibration. Effect points reward clinical and operational consequences. Options should be stated in common units with utilization, waits and cost. Green's chapter on queueing in health care, Hopp and Spearman's factory physics text and Hawkins on turnaround support the analysis.
Common HA 640 Unit 4 mistakes, and how to avoid them
Queue analyses in HA 640 Unit 4 commonly lose points by applying steady-state formulas to hours when arrivals exceed capacity. Another frequent problem is ignoring bursty arrivals, which makes waits look shorter than they are. Students often model the whole day as one block. Options are described in words, not as changes in capacity or arrivals. Costs are missing. Utilization near 100% is accepted as efficient, though waits explode there. Clinical effects are not described. Future volume from the forecast is not tested, so an option that works in July may fail in February when the morning peak climbs toward 160 tubes an hour. Finally, put every option in tubes per hour, because graders reward an analysis that lets leaders compare choices on the same scale.
Get a HA 640 Unit 4 example written to your instructions
Send the Unit 4 instructions and the rubric from your HA 640 classroom, plus the service, the staffing and any arrival data your section provides. We write a custom example with capacity computed from its components, arrivals shown by hour, queue length and wait calculated at peak, and options compared, returned in 24 to 48 hours. The first custom sample is free.
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HA 640 Unit 4 questions, answered
Do I need queueing formulas, or is a description enough?
Check the rubric, since some sections require a specific model and others accept a simpler treatment. Where formulas are expected, apply one and state its assumptions rather than reproducing a derivation. Where they are not, you still need arrival and service rates in the same units and an explicit comparison of the two, because a description with no rates in it cannot support any conclusion about waiting.
Why does the peak matter more than the daily total?
Because capacity cannot be stored. Unused capacity at seven in the morning does nothing for the crowd at eleven, so a service whose daily totals balance can still fail for hours at a time. Reporting demand against the clock is what makes that visible, and it usually changes the recommendation from adding resources to moving arrivals or extending the hours the resource is available.
What counts as a queue in a healthcare setting?
Anything waiting for a resource, which is far more than a waiting room. Charts awaiting review, specimens awaiting processing, referrals awaiting authorization, patients holding for an inpatient bed and calls holding for an agent are all queues and all behave the same way. Choosing a queue that is not made of people sometimes produces a cleaner analysis, because the data are easier to obtain.
Where can I find a free HA 640 Unit 4 sample paper?
Read the complete HA 640 Unit 4 capacity and queue analysis here at no charge, a lab processing bench over capacity every morning with four priced options. Ask for a custom analysis of another queue.
Why do waits grow so fast as utilization approaches 100%?
As a step's workload nears its capacity, small bursts of arrivals have no slack to absorb them, so queues lengthen sharply and nonlinearly. This sample shows that effect at a lab processing bench.