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BSAN 160 - Activities and Quiz Questions (Drexel - AJ Raven Winter 2024) With Complete Solutions
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BSAN 160 - Activities and Quiz Questions (Drexel - AJ Raven Winter 2024) With Complete Solutions
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BSAN 160 - Activities and Quiz Questions (Drexel - AJRaven Winter 2024) With Complete Solutions
Which of the following statement describes the key take away points of Analytics Applications in Healthcare – Humana Example 1?
a) Humana’s prevention efforts aim to treat all injuries after a fall more efficiently.
b) As a strategy to enhance well-being of its members, Humana promotes increased utilization of physical therapy as a better alternative than investing in high-cost analytics.
c) Using analytics can reduce healthcare costs by identifying subgroups of Humana members that are at high risk of falling and managing their fall risk.
d) For Humana members, cost of treatment and recovery are less
expensive than preventative measures.
c) Using analytics can reduce healthcare costs by identifying subgroups of Humana members that are at high risk of falling and managing their fall risk.
According to Humana Example 1, and the article by Gates et
al. (2008), many risk assessment methods, such as screening tools to identify fall risk factors, already exist. In your own words, give two reasons to why your team should not use one
of the existing risk assessment methods?
· Existing models have limited reach - Different test attributes may be needed to predict falls successfully in different populations; for example, the timescale over which a prediction is needed varies from a few days or weeks in hospitalized patients to a year or more for community-living populations. Tools developed for one population may therefore be less accurate when used in a different setting.
· Lack sufficient predictive power - not based on sound evidence
that they are useful in discriminating between people who will fall and those who will not.
Which methods would your team use to forecast the risk factors that are most likely to result in a newly enrolled senior member’s fall at home using existing Humana members’ data?
a) Descriptive analytics methods
b) Central tendency methods
c) Predictive analytics methods
d) Prescriptive analytics methods
c) Predictive analytics methods According to the Humana Example 3 (Section 1.6, Pg. 32-33),
which data source(s) mentioned in the example can be used to identify the patterns of risk factors that increase an individual’s risk of falling?
a) A: Medical claims
b) B: Self-reported health risk assessment data
c) C: Pharmacy data
d) A, B, and C
d) A, B, and C
Congratulations! The model your team developed to identify
the factors that increase the risk of a senior individual to fall at home and experience an injury was well received by the upper-level management. Now, your team is tasked with turning this predictive model to a prescriptive analytics model. Which of the below action would make your analytics
model a prescriptive model?
a) Create a data visualization tool that shows the fall risk distribution of Humana members
b) Identify at-risk members soon after their enrollment and offer those individuals devices and services that detect risk factors to prevent falls at home
c) Describe the variation in fall risk of Humana members to better understand low and high risk levels d) Predict the top 5% highest risk members within the newly enrolled member population
b) Identify at-risk members soon after their enrollment and offer those individuals devices and services that detect risk factors to prevent falls at home
________ analytics help managers understand what is happening in the organization by analyzing trends and patterns in data.
a) Descriptive
b) Predictive
c) Prescriptive
d) Data warehouse
a) Descriptive
Is the following statement true or false?
"Decision support system are computer-based support systems that have precise definitions agreed to by practitioners."
True
False
False
What type of analytics seeks to forecast the future/unknown values of outcomes of interest using past and current available data?
a) descriptive
b) predictive
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