Case Study Scenario

Case Study Scenario.

I’m trying to learn for my Health & Medical class and I’m stuck. Can you help?

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Case Study Scenario

The hospital readmission rate is often considered an indicator of an undesirable patient outcome. You quality improvement team is located at a metropolitan hospital that serves a large population. The quality improvement team is conducting a study pertaining to readmissions, smoking and number of drugs administered to admitted patients with congestive heart failure (CHF). The team is interested in reducing the number of readmissions among patients discharged with a principal diagnosis of CHF.
The team believes that the high admission rate is due to the difficulty that these patients have in controlling the number of drugs that they typically take. The team believes that by improving patient/family education regarding drug administration, the readmission rate could be reduced. Thus, they have developed the screen “CHF patients taking three or more drugs” to identify these patients before discharge. To evaluate the effectiveness of the measure, the team conducts a study on all CHF patients discharged the previous year. The results are shown below:

CHF patients

Number of Drugs Administered

Readmitted

Not readmitted

Total

≥3 drugs

200

40

240

<3 drugs

100

900

1000

Total

300

940

1240

  • Your team needs to decide how to measure the effectiveness of this measure. You think the best way would be to compute the values of the sensitivity, specificity and predictive value. Show your work and provide an interpretation of these measures.
  • On the basis of your results, is this an effective measure? Why or why not?
  • Once you assess the effectiveness of this measure, and your team feels confident that this is an appropriate measure to use, the Health Administrator of the Unit asks your team to examine the odds of patients being readmitted for those who were administered at least 3 drugs. Calculate the odds ratio and explain this finding to your administrator.

    Next, your team is examining the number of cigarettes smoked per day in this sample and sex-specific death rate per 1000 per year among CHF patients admitted to the hospital. The following table shows the results that you obtained.

  • Your team would like to calculate the attributable risk or attributable proportion of CHF death rates due to smoking 1-14 and 15-24 cigarettes per day separately for males and females. Do you notice any differences within and between these groups in death rates due to CHF based on the different number of cigarettes smoked per day?
  • What can the team conclude about the death rate due to CHF for males and females separately when zero cigarettes per day are smoked? How do they compare?
  • Obtain a regression equation for the data shown above that predicts readmissions based on the number of drugs administered. How is the number of drugs administered associated with readmissions for these patients?

Cigarettes/Day

Death Rate/1000/Year due to CHF
Males

Death Rate/1000/Year due to CHF
Females

0

0.01

0.008

1-14

0.27

0.11

15-24

1.23

1.15

25+

2.00

1.50

Your quality improvement Team is revisiting the following patient readmissions and would like to predict the number of readmissions based on the number of drugs administered.

CHF patients

Number of Drugs Administered

Readmissions

1

100

2

100

3

200

4

400

5

500

6

550

7

600

8

630

9

660

Case Study Scenario

Case Study Scenario

As a Manager for a company, Julia was generally well regarded by most of her work. Julia was an easygoing individual who tried to help her employees in any way she could. If a worker needed a small loan until payday, she would dig into her pocket with no questions asked. Should an employee need some time off to attend to a personal problem, Julia would not dock the individual’s pay; rather, she would take up the slack herself until the worker returned.

Everything had been going smoothly, at least until the last performance appraisal period. One of Julia’s workers, Bill, had been experiencing a large number of personal problems for the past year. Bill’s wife had been sick much of the time and her medical expenses were high. Bill’s son had a speech impediment and the doctors had recommended a special clinic. Bill, who had already borrowed the limit the bank would loan, he had become upset and depressed over his general circumstances. 

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When it was time for Bill’s annual performance appraisal, Julia decided she was going to do as much as possible to help him. Although Bill could not be considered more than an average worker, Julia rated him “outstanding” in virtually every category. Because the firm’s compensation system was heavily tied to performance appraisal, Bill would be eligible for a merit increase of 10 percent in addition to a regular cost-of-living raise.

Julia explained to Bill why he was giving him such ratings, and Bill acknowledged that his performance had really been no better than average. Bill was very grateful and expressed this to Julia. As Bill left the office, he was excitedly looking forward to telling his friends about what a wonderful boss he had. Seeing Bill smile as he left gave Julia a warm feeling.

 

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