EXERCISE 5 AND 6

Week 1, Exercise:

The attached dataset, provides some information about hospitals in 2011 and 2012, download the data and then complete the descriptive table. Please use the following format to report your findings.

Table 1. Descriptive statistics between hospitals in 2011 & 2012

Based on your findings in which years hospitals had better performance? Please write a short paragraph and describe your findings. The hospitals had better performance in 2012 compared to 2011. The mean number of hospital beds in 2012 was slightly higher than the mean number of hospital beds in 2011. In terms of revenue, the mean revenue in 2012 was higher than the mean revenue in 2011. The total cost in 2011 was also higher than the total cost in 2012. For these variables, the p.value is less than 0.05 hence the null hypothesis is not rejected at 95% confidence interval. This implies that the means between the two groups are not different.

(Note: Master RStudio script is available for this exercise, but you need to modify that for this analysis)

Week 2, Exercise:

Use the dataset from week1 exercise and then answer the following questions:

1) Compare the following information between teaching and non-teaching hospitals.

2) What are the main significant differences between teaching and non-teaching hospitals? (use ttest)

3) Comparing hospital net-benefit which hospitals has better performance? To answer this question first compute the hospital net benefits with subtracting hospital costs and revenues and then use ttest to compare the significant differences between teaching and non-teaching hospitals.

4) Use a box-plot and compare hospitals-cost and hospital-revenues between teaching and non-teaching hospitals.

The costs were higher for teaching hospitals (1) compared to non-teaching hospitals (0)

The Revenues were higher for teaching hospitals (1) compared to non-teaching hospitals (0)

5) Write a short paragraph and describe your findings.

Based on the t-.test results shown below, there was a significant differences between teaching and non-teaching hospitals for all the variables. This is because as shown below, the p.value is less than 0.05 in all cases hence at 95% confidence Interval, we reject the null hypothesis (There is a significant difference in the means).

For the hospital net benefit, the p. value is also less than 0.05 hence at 95% confidence interval, the null hypothesis is rejected, hence there is a significant difference between teaching and non-teaching hospitals in terms of performance.

Table 2. Descriptive statistics between teaching and non-teaching hospitals, 2011 & 2012

(Note: Master RStudio script is available for this exercise, but you need to modify that for this analysis)

Week 3 & 4, Exercise:

The dataset provides Herfindahl–Hirschman Index, and herfindahel index categories, please use the herf_cat variable and answer the following questions:

Note: “The Herfindahl–Hirschman Index is a commonly accepted measure of market concentration used by antitrust enforcement agencies and scholars in the field. The HHI is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers” (NASI, 2015; pp: 14-16). read more from here:

https://www.urban.org/sites/default/files/publication/50116/2000212-Addressing-Pricing-Power-in-Health-Care-Markets.pdf

For this exercise you do not need to compute the HHI, but if you have any questions, please do not hesitate to ask me, but try to learn more about this you will need that to report your findings.

Use the dataset from week1 exercise and then answer the following questions:

1. Compare the following information between hospitals located in high, moderate and low competitive markets? (table 1)

Table 3. Comparing hospital characteristics and market, 2011 and 2012

2. What are the main significant differences between hospitals in different markets? (use Anova test)

Hypothesis statement

H0: There is no significant difference between the three competitive market levels

H1: There is a significant difference between the competitive market levels

The main significant difference among the three different markets are on variables Hospital beds, System membership, total hospital revenues, Available medical days, Total hospital discharge, Medicare discharge and Herfindahel index. On these 7 variables the P values are less than the level of significance of 0.05 in all cases, therefore we reject the null hypothesis and conclude that there is a significant difference in the three market levels on these 7 variables. On the rest on the variables the P value of the Anova tests is greater than the level of significance of 0.05 hence we do not reject the null hypothesis and therefore conclude that there is no significant difference.

3. Use the density curves and compare hospitals cost and revenues between three markets.

For hospital cost as competition reduces the mean of the total hospital increases. This is evident by the decreasing frequency on figure 5.

For the hospital revenue, from the descriptive statistics it is clear high competitive have markets have the least revenue and moderate competitive markets have the greatest revenue. This has clearly been brought out by the distribution on figure 6.

4. What is the impact of being in high-competitive market on hospital revenues and cost? Do you think being in high-competitive market has positive impact on net hospital benefits? What about the number of Medicare and Medicaid discharge? Do you think hospitals in higher completive market more likely to accept more Medicare and Medicaid patients? What are the impact of other variables? Please discuss your findings in 1-2 paragraphs

(Note: to answer to the last question, please compute the ratio-medicare-discharge and ratio-medicaid-discharge first and then run 2 ttest ) high vs. moderate and high vs. low competitive market), please support your findings with box-plot

In high competitive market both the total hospital cost mean and the total hospital revenue mean are lowest compared to the other two levels of market. This implies that” a high competitive market leads to low hospital cost and subsequent low revenue. This is despite the fact that Anova test shows that total hospital cost shows there is no significant difference in the three market levels while total hospital revenue shows a significant difference.

The mean net hospital benefit in high competitive market is 4,013,058, that of moderate competitive market is 15,320,472 and in low competitive market is 13,353,106. It is very clear that net hospital benefit is lowest in high competitive market from the mean. This implies that a high competitive market does not have a positive impact on the net hospital benefit. Despite this, there is no significant difference in net hospital benefit in the three competitive market levels.

The medicare discharge is lowest at the highest competitive market level(0) and greatest at moderate competition market level(1).

The medicaid discharge is lowest at the highest competitive market level (0) and greatest at moderate competition market level (1) although the difference in moderate competitive market level (1) and low competitive level (2) is minimal.

Run a linear model and predict the difference between hospital beds (use the bed-tot) and hospital’s ownership on hospital net-benefit? Discuss your finding, do you think having higher beds has positive impact on the hospital net benefit? What about the ownership?

2nd Model:

Now, estimate the impact of being a member of a system on hospital net benefit? And discuss your finding (nor more than 2 lines)? Is it significant? 


3nd Model:

Now, include the ratio of ratio-Medicare-discharge and ratio-Medicaid-discharge in your model? How do you evaluate the impact of having higher Medicare and Medicaid patients on hospital revenues?

Based on your finding please recommend 3 policies to improve hospital performance, please make sure to use the final model for your recommendation.
 
(Note: Master RStudio script is available for this exercise, but you need to modify that for this analysis)

Please use this file to answer the questions and submit to the exercise submission folder.
 

01
0e+00
1e+09
2e+09
3e+09
Figure 1. Boxplot of Hospital Costs for teaching & Non-teaching hospitals
Teaching/Non-Teaching
Hospital Costs
01
0e+00
1e+09
2e+09
3e+09
4e+09
Figure 1. Boxplot of Hospital Revenues for teaching & Non-teaching hospitals
Teaching/Non-Teaching
Hospital Revenue

Week 1, Exercise:

 

The attached dataset, provides some information about hospitals in 2011 and 2012, download the data and then 

complete the descriptive table. Please use the following format to report your findings. 

 

Table 1. Descriptive statistics 

between hospitals in 2011 & 2012

 

Variables

 

2011

 

2012

 

p

-

value

 

 

N

 

Mean

 

St. Dev

 

N

 

Mean

 

St. Dev

 

 

Hospital beds

 

1505

 

376.6086

 

560.8998

 

1525

 

376.8

 

579.8366

 

 

< 2.2e

-

16

 

Number of paid Employee

 

1498

 

1237.276

 

1615.797

 

1515

 

1491.121

 

1961.637

 

 

< 2.2e

-

16

 

Number of 

non

-

paid 

Employee

 

30

 

39.973

 

72.58805

 

30

 

44.76976

 

81.29861

 

6.653e

-

05

 

Total hospital cost

 

1505

 

216873322

 

304570722

 

1525

 

214748023

 

294143536

 

< 2.2e

-

16

 

Total hospital revenues

 

1505

 

228706319

 

323339811

 

1525

 

229978391

 

321273114

 

< 2.2e

-

16

 

Available Medicare 

days

 

1499

 

16739.16

 

19214.29

 

1516

 

17110.14

 

19765.74

 

< 2.2e

-

16

 

Available Medicaid days

 

1484

 

5301.199

 

9207.699

 

1501

 

5366.333

 

9340.373

 

< 

2.2e

-

16

 

Total Hospital Discharge

 

1500

 

9492.326

 

10898.6

 

1517

 

9544.051

 

10994.17

 

< 2.2e

-

16

 

Medicare discharge

 

1499

 

3230.624

 

3388.957

 

1516

 

3598.248

 

3785.675

 

< 2.2e

-

16

 

Medicaid discharge

 

1481

 

1130.727

 

1757.158

 

1498

 

1119.547

 

1740.423

 

< 2.2e

-

16

 

 

Based on your findings in which years hospitals had better performance? Please write a short paragraph and 

describe your findings.

 

The hospitals had better performance in 2012 compared to 2011. The mean number of 

hospital beds in 2012 was slightly higher than the mean number of hospital beds in 2011. In terms of revenue, 

the mean revenue in 2012 was higher than the mean revenue in 201

1. The total cost in 2011 was also higher 

than the total cost in 2012. For these variables, the p.value is less than 0.05 hence the null hypothesis is not 

rejected at 95% confidence interval. This implies that the means between the two groups are

 

not

 

diffe

rent.

 

 

(Note: Master RStudio script is available for this exercise, but you need to modify that for this analysis)

 

 

 

Week 1, Exercise: 
The attached dataset, provides some information about hospitals in 2011 and 2012, download the data and then 
complete the descriptive table. Please use the following format to report your findings.  
Table 1. Descriptive statistics between hospitals in 2011 & 2012 
Variables 2011 2012 p-value 
 N Mean St. Dev N Mean St. Dev  
Hospital beds 1505 376.6086 560.8998 1525 376.8 579.8366  < 2.2e-16 
Number of paid Employee 1498 1237.276 1615.797 1515 1491.121 1961.637  < 2.2e-16 
Number of non-paid 
Employee 
30 39.973 72.58805 30 44.76976 81.29861 6.653e-05 
Total hospital cost 1505 216873322 304570722 1525 214748023 294143536 < 2.2e-16 
Total hospital revenues 1505 228706319 323339811 1525 229978391 321273114 < 2.2e-16 
Available Medicare days 1499 16739.16 19214.29 1516 17110.14 19765.74 < 2.2e-16 
Available Medicaid days 1484 5301.199 9207.699 1501 5366.333 9340.373 < 2.2e-16 
Total Hospital Discharge 1500 9492.326 10898.6 1517 9544.051 10994.17 < 2.2e-16 
Medicare discharge 1499 3230.624 3388.957 1516 3598.248 3785.675 < 2.2e-16 
Medicaid discharge 1481 1130.727 1757.158 1498 1119.547 1740.423 < 2.2e-16 
 
Based on your findings in which years hospitals had better performance? Please write a short paragraph and 
describe your findings. The hospitals had better performance in 2012 compared to 2011. The mean number of 
hospital beds in 2012 was slightly higher than the mean number of hospital beds in 2011. In terms of revenue, 
the mean revenue in 2012 was higher than the mean revenue in 2011. The total cost in 2011 was also higher 
than the total cost in 2012. For these variables, the p.value is less than 0.05 hence the null hypothesis is not 
rejected at 95% confidence interval. This implies that the means between the two groups are not different.  
(Note: Master RStudio script is available for this exercise, but you need to modify that for this analysis)