MIS771 Descriptive Analytics Assignment Sample

Here’s the best sample of MIS771 Descriptive Analytics Assignment, written by the expert. 

Introduction

The UnitedHealth Group: America’s most prominent health insurance provider provides data related to claim payment amounts, presiding physician and the claimant.  The main purpose of analysis is to develop understanding of the claims paid out for medical malpractice lawsuits with focus on sample of 200. In addition, it also provides descriptive and inferential analyses and produces a report based on the findings. This report will be categorized into four sections. First section will provide summary of claim payment amount and second section will provide information on average age of claimants and the proportion of claimants with no insurance. Third section will examine the claims made by the industry reports on the drop of the average amount of paid claims below $77500 and difference in proportion of ‘MILD’ or ‘MEDIUM’ claims based on gender and the average claim amount with the involvement of a private attorney and no private attorney. It also determines claim related to lower percentage of ‘SEVERE’ claims with the involvement of an Orthopaedic surgeon than that of other specialists. In final section, it provides conclusion and limitations of the study.

Summary of claim payment amount

From data analysis, it can be determined that the average claim payment amount is $73457 for the sample of 200. The range of claim payment amount is between $1547 and $228725. It shows that the claim payment differs for the claimants with a large difference. Total value of the claim payment amount is $14,691,499. The value of standard deviation and sample variance is so high implies a large deviation of the data to the mean of this data set. It means there are large difference between the average value and the different data points. Some of the claimants get little amount while some of them get big amount of claim. In addition, Kurtosis and Skewness are highly positive indicating that data distribution of claim payment amount is asymmetrical due to high Skewness. Positive or right skewed distributions show that many people are getting relatively little while increasingly few people are getting big amount of claim.

a) The average Age of claimants

Based on analysis, it can be determined that the average age of claimants is 44.49 years. However the lower limit of average age of claimants is 42 years while upper limit of average age is 47 years.

b) The proportion of claimants with ‘No Insurance’.

The analysis suggests that the proportion of claimants with no insurance is between 5.03% and 12.97%. It means there are more claimants with Medicare/Medicaid, private, unknown and workers compensation.  One of the factors that can be considered in the profile of claimant for the medical malpractice insurance claims is income level. Income level of the claimants may influence the premium value of the insurance claim. At the same time, it can be possible to determine the relationship between the income level and claim amount. In addition, education can be significant factor in the profile of claimant to determine the awareness of the claimant about the medical malpractice and related aspects. It is because there is a significant relationship between the claimants’ factors with malpractice intentions including education, being affluence and gender and age. Moreover, type of patient (in- versus out-patient) is also a considerable factor that could be effective in valuing the medical malpractice insurance claims.

a) An industry report suggests that the average amount of paid claims has dropped below $77,500. Is there any evidence to support this argument?

From ANOVA test, it can be determined that the average amount of paid claims has dropped below $77,500. It is because the most malpractice claims are settled out of the court and payments for the claims judged in the court are lower than those settled out of court.

b) A similar study last year reported that 3 out of 4 claims are with either ‘MILD’ or ‘MEDIUM’ severity conditions. Check if this statement is still valid for all patients?

From the Hypothesis test, it can be determined that the null hypothesis is ≤ 75% and alternative hypothesis is > 75%. It shows that 3 out of 4 claims are with either MILD or MEDIUM severity conditions so it fails to reject the null hypothesis as well as the statement are still valid for all patients.

c) Is there a difference in proportion of ‘MILD’ or ‘MEDIUM’ claims by patient’s Gender?

From the hypothesis test, it is determined that ‘t value’ is 0.0627 that is more than 0.05. So, there is no difference in the Gender. It can be said that the Confidence Interval for µ1 – µ2 has no difference in the hypothesis testing. At the same time, the level of confidence is 5% and intermediate calculations shows that standard error of the mean is 0.0688. So, the MILD or MEDIUM claims by patient’s Gender has no difference in the proportion.

Can we conclude that there is a difference in the proportion of ‘MILD’ or ‘MEDIUM’ claims by female patients compared to that of male patients?

From the hypothesis test, it can be determined that ‘t value’ is 0.0628 that is more than 0.05. So, it can be concluded that there is no difference in the proportion of ‘MILD’ or ‘MEDIUM’ claims. In this hypothesis test, the confidence Interval for µ1 – µ2 has no difference in the proportion of MILD or MEDIUM claims by the comparison of female patients to male patients.

d) As an industry standard, it is believed that the payment amounts are related to whether or not a private attorney represented the claimant. In particular, the average claim amount when a private attorney is involved is higher than when there is no private attorney involved. Does the data support this proposition?

According to the hypothesis test, it can be determined that null hypothesis (µ₁ – µ₂) is equals to zero but the alternative hypothesis is not equals to zero. It shows that that the null hypothesis is rejected so it can be said that the average claim amount is higher when a private attorney is involved as comparison than there is no private attorney involved. In addition, it is determined that the data supports this proposition that the payment amounts are related to private attorney represented the claimant or not.

e) Also, the industry stakeholders believe that private attorney representation is higher for ‘SEVERE’ claims than for claims with a ‘MEDIUM’ severity. Is this a valid statement?

From the hypothesis test, it can be determined that private attorney representation is not higher for SEVERE claims than for claims with a MEDIUM severity. The above statement is not valid as per the hypotheses test because this test failed to reject null hypothesis. So, believes of the industry stakeholders are not valid and the private attorney representation is not higher.

a) I believe that the percentage of ‘SEVERE’ claims with the involvement of an Orthopaedic surgeon is lower than that of other specialists.

From the hypothesis test, it can be determined that the percentage of SEVERE claims with the involvement of an Orthopaedic surgeon is not lower than other specialists. It is because the hypothesis shows that confidence Interval for π1 – π2 is repenting negative interval limits with 4.24% standard errors.

b) I also believe that the average claim amount for ‘SEVERE’ claims is higher when an Orthopaedic surgeon is involved than the other specialisations. Is there any evidence to support my assertions above?

In addition, the hypothesis test determined that the average claim amount for SEVERE is higher not higher when Orthopaedic surgeons are involved than other specializations. There is evidence which can support that the average claim amount for ‘SEVERE’ claims is higher in Orthopaedic surgeon than other specializations.

Conclusion

On the basis of the above analysis, it can be stated that the average age of claimants are 44.49 years. As well as, it is also concluded that the average amount of paid claims are below $77,500 with 3 out of 4 claims are with MILD and MEDIUM severity conditions. Additionally, both male and female has their proportion in ‘MILD’ or ‘MEDIUM’ claims.

Research Limitations

In this research study, researcher used primary data for collecting or gathering a relevant and accurate data efficiently. In respect to this, researcher selected sample size 200 for collecting a primary data in order to analysis the different views and opinions towards the research topic.

At the same time, researcher has an opportunity to use the secondary data in an efficient manner by collection more information and data from different online and offline sources such as books, newspaper, online blogs, and magazines and so on. On the other hand, this research study can also be used as secondary data for conduction further study on such related topic efficiently in future. This study used quantitative data by conducting a proper survey such as questionnaire efficiently for collecting a data from the selected sample size that is also a limitation regarding data collection method. For collecting a qualitative data, interview can also be conducted by the researcher in order to enhance the efficiency of the research.    

References

Abbott, D., 2014. Applied predictive analytics: Principles and techniques for the professional data analyst. USA: John Wiley & Sons.

Bruckman, L.S., Wheeler, N.R., Ma, J., Wang, E., Wang, C.K., Chou, I., Sun, J. and French, R.H., 2013. Statistical and domain analytics applied to PV module lifetime and degradation science. IEEE Access, 1, pp.384-403.

Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), p.3.

Wheeler, D., Shaw, G. and Barr, S., 2013. Statistical techniques in geographical analysis. USA: Routledge.

Zhang, C. and Ré, C., 2014. Dimmwitted: A study of main-memory statistical analytics. Proceedings of the VLDB Endowment, 7(12), pp.1283-1294.

Appendix

Exhibit 1: Summary of claim payment amount

Exhibit 2: The average Age of claimants

Exhibit 3: Proportion of claimants with ‘No Insurance’.

Exhibit 4: Hypothesis test

Exhibit 5: Check if this statement is still valid for all patients?

Exhibit 6: Is there a difference in proportion of ‘MILD’ or ‘MEDIUM’ claims by patient’s Gender?

Exhibit 7: Difference in the proportion of ‘MILD’ or ‘MEDIUM’ claims by female patients compared to that of male patients

Exhibit 8: Does the data support this proposition?

Exhibit 9: Is this a valid statement?

Exhibit 10: The percentage of ‘SEVERE’ claims with the involvement of an Orthopaedic surgeon is lower than that of other specialists.

Exhibit 11: Is there any evidence to support my assertions above?

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