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BANA7015 Advanced Health Data Analytics

tag 0 Download 0 Pages / 0 Words tag 21-06-2022
  • Course Code: BANA7015
  • University: University Of Cincinnati
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  • Country: United States

Answer:

Data Analytics and Business Intelligence

The current research paper highlights advantages of business intelligence and data analytics in healthcare industry. In this paper, reviewed are models constructed from given dataset. The concepts that are presented are in terms of big data (WONG). The paper also identifies a research question that is related with dataset and later, deliverables are provided which are used to provide insights relevant to healthcare organizations and are used to influence their activities and actions (DAVIS).

Nowadays, presenting role of business technology in health sector is of importance. Big data is referred to as vast data being captured and generated in variety of ways. In medical field, it is important we move from papers that are overwhelming the doctors today; we should continually move towards the electronic capture of data. With this, we can have right information during right time (GREENWALD).

Discussing the healthcare analytics, of importance is to ask how statistics regarding use of analytics in the healthcare affects end user’s. The main disadvantage of not using the analytics feature is not being able to use all of the data because management of data is becoming difficult. Those who are using the analytics are also missing the new insights meaning they are unable to imagine all potential (OHLHORST). The power to collect insights can be realized in these steps:

  • Engage: predicting the demand and the supply of the supply chain
  • Visualize: understanding customer’s thoughts
  • Predict: providing proper services and offers to customers and predicting emerging market trends with innovative products.

Healthcare data is used in vast cases such as surgical analytics, sharing of processes among clinicians, and sharing of healthcare visualizations (TELEA). Quality and profitability analysis in management provides critical insights used in obtaining organization gains and goals. The analytical applications are used in developing base for use in analytics as an enterprise (SAQUIB).

General Objective

The general objective of this research project is to determine whether there is a significant difference in Medicare deaths in United States between 1994 and 2013. This will be achieved by finding averages across all states in the United States and later on t-test will be used to test for difference in means.

Specific Objectives

The specific objective of this research project will be as follows:

  • To determine whether there is a significant difference in Medicare deaths among different States.
  • To determine the average Medicare deaths that occur within hospitals.
  • To determine the average hospital days per decedent during hospitalization during death.
  • To determine whether there is a relationship between Medicare deaths and the number of days a decedent spent in ICU/CCU.

Various number of performance measures or outcome measures will be used in this research paper and they include, average number of Medicare deaths in United States, average number of days in ICU/CCU, and average number of days hospitalized before death among other performance measures.

Fig 1 above shows the Medicare deaths trend in the United States between 1994 and 2013. From the line graph above, 2009 had the least number of Medicare death while 2003 had the highest number of Medicare deaths. The trend indicates that there have a decline in Medicare deaths across United States. This can be attributed to changes in technology, innovation of new drugs and vaccines, and increased awareness in health sector.

Testing whether there have been a significant mean difference in Medicare deaths in the United States we test the below hypothesis:

H0: There is no significant mean difference in Medicare deaths in the United States

H1: There is a significant mean difference in Medicare deaths in the United States

Table 1

t-Test: Two-Sample Assuming Equal Variances

 

Average Medicare Deaths in United States

Mean

27986.3

Variance

1485101

Observations

20

Pooled Variance

742567.9

Hypothesized Mean Difference

0

df

38

t Stat

95.3494

P(T<=t) one-tail

3.77E-47

t Critical one-tail

1.685954

P(T<=t) two-tail

7.54E-47

t Critical two-tail

2.024394

Table 1 above represents the mean test results at both 0.01 and 0.05 level of confidence. Both at 0.01 and 0.05, the p-value is less than α therefore, we fail to accept the null hypothesis and conclude that there’s a significant mean difference in Medicare deaths in the United States (HARRINGTON). With this in mind, we can confidently say that the deaths have significantly reduced through the years. From table 1 we can also see that the mean average of Medicare deaths in United States is 27, 986.

Fig 2 above shows the percentage of Medicare deaths that occurred within a hospital between 1994 and 2013. It is clearly shown that there have been a decline in deaths occurred within a hospital. 2013 has the least number of Medicare of deaths that have occurred within a hospital.

Testing for hypothesis that there is no significant difference in Medicare deaths occurring within a hospital we get the results as below

H0: There is no significant difference in Medicare deaths occurring within a hospital

H1: There is a significant difference in Medicare deaths occurring within a hospital

t-Test: Two-Sample Assuming Equal Variances

 

Percent of Medicare deaths occurring within a hospital

Mean

0.280208949

Variance

0.00152519

Observations

20

Pooled Variance

17.50076259

Hypothesized Mean Difference

0

df

38

t Stat

-1514.258832

P(T<=t) one-tail

9.46917E-93

t Critical one-tail

1.68595446

P(T<=t) two-tail

1.89383E-92

t Critical two-tail

2.024394164

The p-value from table 2 is less than α at both 0.01 and 0.05, therefore, we fail to accept the null hypothesis and conclude that there is a significant difference in Medicare deaths occurring within a hospital in the United States.

Using the latest data for 2013, we will embark on analyzing the specific objectives of this research paper.

Specific objective one: To determine whether there is a significant difference in Medicare deaths among different States.

From the dataset provided, we have 51 States. It can be assumed that these states do have different Medicare deaths. To investigate this, a descriptive statistic is performed to check on the state with the highest and lowest Medicare deaths.

From fig 3 above, California had the highest Medicare deaths. Florida was second, and Texas was the third State with the highest Medicare deaths, Alaska had the least Medicare deaths followed by District of Columbia and Wyoming respectively.

Medicare deaths (2013)

 

Mean

26435.5098

Standard Error

3460.325747

Median

19166

Mode

#N/A

Standard Deviation

24711.66866

Sample Variance

610666568.2

Kurtosis

2.048620823

Skewness

1.543886813

Range

101457

Minimum

2186

Maximum

103643

Sum

1348211

Count

51

Table 3 above summarizes the descriptive statistics on the number of Medicare deaths in 2013. The mean Medicare death for 2013 was 26,435 with the highest being 103, 643 and the lowest 2,186.

Specific objective two: To determine the average Medicare deaths that occur within hospitals.

Table 4

Percent of Medicare deaths occurring within a hospital (2013)

Mean

0.20221781

Standard Error

0.004056915

Median

0.202125767

Mode

#N/A

Standard Deviation

0.028686723

Sample Variance

0.000822928

Kurtosis

0.816274378

Skewness

0.37155581

Range

0.147722161

Minimum

0.145602866

Maximum

0.293325027

Sum

10.11089051

Count

50

Table 4 above shows the average percent of Medicare deaths occurring within a hospital in 2013. The mean of deaths occurring within a hospital was 20% of the total deaths in the United States.

Specific Objective three: To determine the average hospital days per decedent during hospitalization during death.

Hospital days per decedent during the hospitalization in which death occurred (2013)

Mean

1.316571507

Standard Error

0.051746911

Median

1.260677995

Mode

#N/A

Standard Deviation

0.373152279

Sample Variance

0.139242623

Kurtosis

4.048855485

Skewness

1.528030798

Range

2.079327487

Minimum

0.719699539

Maximum

2.799027026

Sum

68.46171834

Count

52

The average number of hospital days per decedent during in which death occurred is 1.3 days. The longest number of days a decedent spent in hospital is 3 days with the shortest being half day.

Specific objective four: To determine whether there is a relationship between Medicare deaths and the number of days a decedent spent in ICU/CCU.

In order to check whether there is a significant relationship between the numbers of days spent in ICU/CCU, a correlation test is performed to ascertain whether there is an association between the two variables.

Variable

Medicare deaths (2013)

Number of days spent in ICU/CCU in which death occurred (2013)

Medicare deaths (2013)

1

0.451199256

Number of days spent in ICU/CCU in which death occurred (2013)

0.451199256

1

From table 6, we can deduce that there is an association between Medicare deaths and the number of days spent in ICU/CCU in which the death occurred. The Pearson correlation value is 0.45 which can described as moderate association between the variables. To explain these relationship, a regression model is constructed using number of days spent in ICU/CCU as the independent variable and Medicare deaths as the dependent variable.

H0: There is no significant relationship between number of days spent in ICU/CCU in which death occurred and Medicare deaths.

H1: There is a significant relationship between number of days spent in ICU/CCU in which death occurred and Medicare deaths.

Table 6

Regression Statistics

 

Multiple R

0.451199256

R Square

0.203580768

Adjusted R Square

0.187327315

Standard Error

22277.16409

Observations

51

From table 6 above, the R square is 20.3%, an indication that as long as there exists a relationship between the two variables, only 20.3% of variation in the model is explained by the number of days spent in ICU/CCU in which death occurred (PARDALOS).

ANOVA

Source

df

SS

MS

F

Significance F

Regression

1

6215998458

6215998458

12.52539

0.000890334

Residual

49

24317329950

496272039.8

 

 

Total

50

30533328409

 

 

 

Table 7 has a p-value of 0.00089 which is less than α, therefore the model passes the goodness of fit test.

 

Coefficients

Standard Error

t Stat

P-value

Intercept

-9776.28

10696.79

-0.913

0.365

Number of days spent in ICU/CCU in which death occurred (2013)

48811.61

13792.01

3.534

0.0008

Table 8 has a p-value 0.0008 > 0.05, therefore we fail to accept the null hypothesis and conclude that there is a significant relationship between significant relationship between number of days spent in ICU/CCU in which death occurred and Medicare deaths. The variation explained in this model is quite small and adding other control variables in the model, we get the following results:

Regression Statistics

 

Multiple R

0.652606

R Square

0.425894

Adjusted R Square

0.375972

Standard Error

19521.09

Observations

51

Increasing the number of independent variables increases R squared to 43%, therefore more variation is catered for.

From the results and findings from this research we can conclude that United States have experienced a decline in Medicare deaths across the years under study. This may be attributed to innovation, technology, and other changes in the health sector including new drugs in the market and new vaccinations.

Different States have witnessed different number of Medicare deaths across United States with California recording all time high Medicare death number while Alaska had the lowest number of Medicare deaths. This can be due to the size of States, the bigger the State, the higher the number of Medicare deaths and vice versa.

The model describing the relationship between numbers of days spent in ICU/CCU in which death occurred and Medicare deaths indicated a significant relationship but still there may be other underlying factors that could attribute to Medicare deaths as only 20.3% of variation is explained in the model. Increasing independent variables increases the variation explained in the model to 43%. Therefore, in future studies more factors should be studied.

Works Cited

Davis, K. and Patterson, D. Ethics of big data. Farnham: O'reilly, 2012. Book.

Greenwald, H. P. and Beery, W. Health for all. Chicago: Health Administration Press, 2002. Book.

Harrington, D. Design for clinical trials. New York: Springer, 2012. Book.

Ohlhorst, F.J. Big Data Analytics. Hoboken: Wiley, 2012. Book.

Pardalos, P. M. Systems analysis tools for better health care delivery. New york: Springer, 2013. Book.

Saquib, N. Mathematica data visualization. Birmingham: Packt Publishing, 2014. Book.

Telea, A.C. Data Visualization. Boca Raton: CRC Press, 2015. Book.

Wong, P. W., KAO, D.L., HAO, M. C. AND CHEN, C. Visualization and data analysis. Beijing, 2013. Book.

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