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Convert Gender into a categorical variable. Code male as 0 and female as 1.

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

Complete this assignment using SPSS. Please do not use Excel. Submit your assignment as a Word Document. 

Deliverables: Copy and paste the screenshots along with your answers into a word document. You may upload as many versions of the assignment as you like, but only the last one will be graded. 

Healthcare insurance companies try to minimize the hospital costs for their members. There are two reasons why this is important: First, any hospital costs for members are a direct expense for the healthcare insurance company. Second, the higher the healthcare costs for the insurance company, the higher the insurance premiums for their members.

Cardinal Healthcare that runs its own hospitals has recently seen a spike in their healthcare cost for their chest pain patients. Based on the anecdotal evidence, they believe that most of their costs are because patients do not have a choice to visit an urgent care facility and they end up in an emergency room. An urgent care visit is usually a lot more economical than an emergency room visit. However, investing in a new urgent care facility is also a huge undertaking. Cardinal Healthcare would like to undertake a large study to test the belief that they can reduce healthcare costs by opening an urgent care facility.

Cardinal Healthcare has conducted a pilot study (note: this data is completely fictional). However, the data for this study is in a format that may not be easy to analyze. Your goal is to transform this data and make it easy for analysis. You have the following objectives:

1. Convert Gender into a categorical variable. Code male as 0 and female as 1.

2. A new disturbing trend is that patients are driving or taking Uber to the hospital instead of taking an ambulance because ambulance services are prohibitively expensive. Patients that arrive via any other means that are not via ambulance are categorized as “walked in.” Convert this variable into a categorical variable such that code Ambulance as 0 and everything else as 1.

3. Convert FastingBloodSugarOK into a categorical variable. Code Yes as 0 and No as 1.

4. Convert RestingECGOK into a categorical variable. Code Yes as 0 and No as 1.

5. There are four types of chest pains in the dataset, i.e., Gastro Oesophageal Reflux Disease (GORD), Pulmonary embolism (PE), Pericarditis, and Angina. Recode this variable into a new variable such that each type of the chest pain is assigned the following number:  

a. GORD = 1

b. PE = 2

c. Pericarditis = 3

d. Angina = 4

Note: to really compare the different types of chest pains, you need to conduct dummy variable coding on this variable. That is out of scope for this particular course. But it will be covered in detail in the Advanced Analytics course that you would have in your elective choices.

6. Perform the following operation on this recoded data. 

a. This data does not include Ambulance cost. Intuitively, not using an ambulance would save patients money. But the increased time in reaching the hospital for “walked in” patients may increase the hospital stay cost which is in the data, thereby mitigating the benefits. What is the average hospital cost for patients that use Ambulance? Compare that with the average cost for patients that did not use Ambulance. To do this, you would have to compare the patients that used “Ambulance” (you coded this s 0) and compare that with patients that did not use Ambulance (you coded this as 1).

b. What is the average hospital cost for patients that stayed in the ICU for longer than 10 days?

c. What is the average hospital cost for patients that stayed in the ICU for longer than 10 days and were diagnosed with Pulmonary embolism (PE)? HINT: You can add as many conditions you would like in the IF statement by using the keyword “AND”. For example, condition 1 AND condition 2 AND condition 3….. this question only has two conditions.

d. What is the average hospital cost for patients that stayed in the ICU for longer than 10 days, were diagnosed with Pulmonary embolism (PE), and were also “Walked-in” patients?

e. This exercise uses the dataset Healthcare.xlsx, which you can download above.

 

 

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