What does a typical heart failure stay look like at three hospitals? HA535's Unit 3 descriptive statistics sample answers with medians, quartiles, a geometric mean and discharge destinations. Searches like "ha 535 unit 3 assignment example", "ha535 unit 3 sample" and "ha535 unit 3 example" land here.
What a finished HA535 Unit 3 healthcare descriptive statistics looks like
Five pages holding two tables and three figures. The summary table covers the whole population, 486 discharges from MS-DRGs 291 through 293 in one fiscal year: mean 4.68 days, median 4, standard deviation 2.96, interquartile range 3 to 6, 90th percentile 8, maximum 23 and a skewness of 1.81. A histogram shows the long right tail, with 22 stays beyond the outlier fence of 10.5 days. Side-by-side box plots compare the hospitals: the flagship's mean of 4.99 sits well above its median of 4, while the rural hospital's 3.68 sits just under its own median of 4. A geometric mean of 3.91 days is computed because CMS publishes expected stays that way, and sits beside a bracketed [GMLOS] placeholder. A frequency table closes the paper: 48.4 percent home, 26.1 with home health, 19.5 to skilled nursing.
How a HA535 Unit 3 example is structured
The paper describes before it compares, and compares before it concludes. A data paragraph states the population, DRG range, fiscal year and counting rule, midnights rather than hours. Center and spread follow as a pair, with a sentence on why the median suits a skewed measure and why the mean still matters to a finance office paying for every day. Shape gets its own short section: the tail is described in bed-day terms, since the longest 49 stays hold 546 of 2,276 days, 24.0 percent. Hospital comparison uses box plots rather than three means, so differences in spread stay visible. Destinations are summarized as counts and percentages, never as averaged codes. In closing, the paper states what the description suggests for the director, which stays deserve case review, and stops short of explaining causes the data cannot reach.
Population and counting rule
MS-DRGs 291 to 293, one fiscal year, 486 discharges, length of stay counted in midnights. Transfers out and deaths stay in the file and are flagged, since dropping them would flatter every average.
Median 4, mean 4.68
Center and spread reported together, the interquartile range beside the standard deviation. One sentence explains which figure a director should quote to a board and which one drives cost.
The expensive tenth
The longest 49 stays account for 24.0 percent of all heart failure bed days. The tail is where a length-of-stay effort would look first, and the paper points there without guessing at causes.
Three hospitals, three boxes
Box plots show the flagship's wider spread and heavier tail, the suburban hospital's lower quartile at two days, and the rural hospital's compact distribution with a maximum of ten.
Where patients went
Discharge destinations as counts and percentages: 235 home, 127 with home health, 95 to skilled nursing, 29 elsewhere. Waiting for post-acute beds is flagged as a possible contributor to the tail for later units.
Where marks go in HA535 Unit 3
Reporting a mean length of stay and stopping there is the weakness this assignment exposes most often. Measures like stay length are nearly always skewed, and rubrics for HA535 descriptive work commonly expect a median, a spread measure and a figure that shows the shape. Labeling carries weight too: units stated, population defined, counting rule named. Averaging category codes, or giving percentages without counts, reads as unfamiliarity with the data. Instructors frequently reward the manager's translation, a sentence saying what a number means for beds or staffing, above extra decimal places. Site comparisons drawn from means alone mislead when spreads differ, and graders say so. Where a benchmark is cited its method has to match: CMS expected stays are geometric means, and setting an arithmetic mean against them overstates the gap.
Get a HA535 Unit 3 example written to your instructions
Pass along whatever dataset the Unit 3 assignment provides, or the variables it names, with the directions and rubric. Within 24-48h a free first model arrives, each statistic computed from that file with the spreadsheet included and a plain sentence under every table saying what the figure means for the service.
HA535 Unit 3 questions, answered
Why use a geometric mean for length of stay?
Because stays are skewed, the geometric mean sits closer to the typical case than the arithmetic mean, and it is the form CMS uses when it publishes expected length of stay for each MS-DRG. If your assignment compares a facility against those figures, computing a geometric mean first keeps the comparison fair. Otherwise the median and quartiles usually describe stays well.
Should outliers be removed before computing statistics?
Rarely at this stage. Descriptive work reports the whole population and then shows the tail, so a reader can judge how much it matters. Removing long stays quietly makes a service look efficient and hides the very cases a director most needs to review. If a later analysis trims them, state the rule used and the number of cases removed.
How should categorical variables like discharge destination be summarized?
With counts and percentages in a frequency table, and a bar chart if the rubric wants a figure. Means and standard deviations of category codes are meaningless even when software produces them. Order categories by frequency or by a logical sequence, and check that percentages sum to 100 after rounding, or note why they do not.