According to a study published in the US
News and World Report in 2010, the cost of medical malpractice in the United
States is $55.6 billion a year, which is 2.4 per cent of annual health-care
spending *. Another 2011 study published in the New England
Journal of Medicine revealed that annually, during the period 1991 to 2005, 7.4
per cent of all physicians licensed in the US had a malpractice claim against
them. These staggering numbers not only contribute to the high cost of health
care in the US, but the size of successful malpractice claims also contributes
to high premiums for medical malpractice insurance.
A report from
McKinsey (May 2014)† Unleashing the Value of Advanced Analytics in
“The proliferation of
third-party data sources is reducing insurers’ dependence on internal data.
Digital “data exhaust” from social media and multimedia, smartphones,
computers, and other consumer and industrial devices — used within privacy
guidelines and assuring anonymity — has become a rich source for behavioural
insights for insurance companies, as it has for virtually all businesses.
the release of previously unavailable or inaccessible public sector data has
greatly expanded potential sources of third-party data. The US and UK
governments and the European Union have recently launched “open data”
Web sites to make available massive amounts of government statistics, including
health, education, worker safety, and energy data, among others. With much
better access to third-party data from a wide variety of sources, insurers can
pose new questions and better understand many different types of risks.”
The UnitedHealth Group: America’s most
prominent health insurance provider has collated a range of data and wants to
develop a better understanding of its claims paid out for medical malpractice
lawsuits. Its records show claim payment amounts, as well as information about
the presiding physician and the claimant for many mediated or settled lawsuits
You are a Data Analyst working for
UnitedHealth Group. Your Manager – Edmond Kendrick has asked you to conduct a
preliminary analysis of collected data. In particular, you are expected to
perform a series of descriptive and inferential analyses and produce a report
based on your findings.
The data set contains numerous variables and details about the claims.
The eight variables in the data table are described below:
|Claimant ID||Unique ID of the claimant|
Amount of the
claim payment in dollars
rating of damage to the patient (MILD, MEDIUM, SEVERE)
Age of the
claimant in years
claimant was represented by a private attorney
of the claimant
the physician involved in the lawsuit
Type of medical
insurance carried by the patient
Edmond’s email to
you is reproduced on the next page.
Email from Edmond Kendrick
From: Edmond Kendrick
Subject: Analysis of Claims(updated)
As discussed earlier, I have cleaned and
simplified the dataset to eight variables for your convenience. The cleaned
dataset contains information about 200 randomly selected claims made this year.
Is there any evidence to support my assertions
I look forward to
Chief Data Scientist – UnitedHealth Group
consists of two parts: Analysis and Report. You are
required to submit both your written report and your analysis.
Guidelines for Data
Read the case
study and questions asked by Edmond carefully. Then spend some time reviewing
the data to get a sense of the context. The analysis required for this
assignment involves material covered in Module 1, with the corresponding
tutorials being a useful guide.
should be submitted in the appropriate worksheets in the Excel file. Each
question from the email should be analysed in a separate tab (e.g. Q1, Q2 … or
Q3.1, Q3.2 …). You need to add these. Before submitting your analysis, make
sure it is logically organised, and any incorrect or unnecessary output has
been removed. Marks will be penalised for poor presentation or
disorganised/incorrect results or any unnecessary output.
For all questions
in the email, you can assume that:
You can complete all data analysis using the Excel templates provided
in the assignment data file. In choosing the technique to apply for a given
question, keep the following in mind:
You may need to
make certain assumptions about the data set we are using to answer some
questions. For other questions, there will be technical/statistical assumptions
that you need to make; for example, whether to use an equal or an unequal
variance test…etc. You need to consider and incorporate any violations
of assumptions such as unequal sample sizes as limitations of your analysis in
the Technical Report
Once you have
completed your data analysis, you need to summarise the key findings for each
question and write a response to Edmond in a report format. Your technical
report consists of four sections: Introduction, Main Body,
Conclusion, and Appendices. The report should be around 1,500 words.
headings (e.g. Q1, Q2 … or Q3.1, Q3.2…) and titles in the main body of the
report. Use sub-headings where necessary.
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