The policy chosen for this discussion was the Hospital readmissions reduction program (HRRP). Working in the emergency department we see our share of people who are re-admitted into the hospital for distinct reasons. Finding ways to reduce this number can allow for a healthier community and a longer quality of living. It will also of course reduce the number of returns after discharge to the hospital, which will leave more room for unavoidable health concerns instead of preventable ones. This is why it is essential to determine its effectiveness and how social factors can impact this problem.

This program is organized by the Center of Medicare and Medicaid Services (CMS). To determine the effectiveness of this project they issue a payment reduction that depends on the hospital’s performance during a certain period. This reduction helps decrease hospital payments which gets applied to Medicare’s fee-for service base operating diagnosis-related group payments at a reduction payment that is capped at three percent. The way the achieve this is by monitoring and sending and annual report to the hospital. Giving the hospital a chance to question and review the report before getting paid (Hospital readmissions and reduction program, n.d.).

Plans and programs such as this one allows for hospital accountability. It also gives the facility a reason to ensure and educate patients in ways to decrease the chance for readmission. Society is entitled to understand the true meaning of becoming part of their rehabilitation process. This will give them tools and confidence that they need to be successful in their healthcare goals and outcomes. Once we understand how this affects our community, we can create better plans on how to resolve this ongoing issue.

Studies show that the group of people most affected by hospital readmissions are the elderly and those with other comorbidities such as chronic heart failure (Turrise et al., 2023). Research has also shown that factors such as elderly socioeconomics play a crucial role in the success of this circle of individuals. For example, family presence, risk for falls, nutrition and having other comorbidities are great determinants for these members of society to reach their goals and reduce readmission numbers (Cilla et al., 2023). Relying on this and other evidenced based research and studies is how we can ensure policies like this one can be utilized to their potential (Salcido, 2014). These can also make sure we are putting policies and regulations into practice and at same time keeping our communication

#Print your name at the top of the script. Include the prefix: “Plotting Basics:” such that it

#appears “Plotting Basics: Firstname Lastname ”

print(“Plotting Basics : Tianyu Zhang”)

#Import libraries including: FSA, FSAdata, magrittr, dplyr, plotrix, ggplot2, and moments

#NOTE: You must use R version 3.6.3 to gain access to the FSA data set. If you installed a

#later version of R, you must uninstall Rstudio and R. Then reinstall R version 3.6.3; then

#reinstall Rstudio

install.packages(“FSA”)

install.packages(“FSAdata”)

install.packages(“magrittr”)

install.packages(“dplyr”)

install.packages(“plotrix”)

install.packages(“ggplot2”)

install.packages(“moments”)

library(FSA)

library(FSAdata)

library(magrittr)

library(dplyr)

library(plotrix)

library(ggplot2)

library(moments)

#Load the BullTroutRML2 dataset (BullTroutRML2.csv)

data(BullTroutRML2)

# Print the first 5 records

print(head(BullTroutRML2, 5))

# Print the last 5 records

print(tail(BullTroutRML2, 5))

#Remove all records except those from Harrison Lake (hint: use dplyr::filter() function)

#NOTE: From this point forward any reference to BullTroutRML2 always refers to the

#filtered dataset (Harrison Lake only data is used). You may choose to rename the

#dataset at this point.

BullTroutRML2_filtered <- dplyr::filter(BullTroutRML2, lake == “Harrison”)

#Display the first 4 and last 4 records from the filtered BullTroutRML2 dataset

# Display the first 4 records

print(head(BullTroutRML2_filtered, 4))

# Display the last 4 records

print(tail(BullTroutRML2_filtered, 4))

#Display the structure of the filtered BullTroutRML2 dataset

str(BullTroutRML2_filtered)

#Display the summary of the filtered BullTroutRML2 dataset

summary(BullTroutRML2_filtered)

#Create a scatterplot for “Age (yrs)” (y variable) and “Fork Length (mm)” (x variable)

#with the following specifications:

#• Limit of x axis is (0,500)

#• Limit of y axis is (0,15)

#• Title of graph is “Plot 1: Harrison Lake Trout”

#• Y axis label is “Age (yrs)”

#• X axis label is “Fork Length (mm)”

#• Use small solid circles for the plotted data points

plot(BullTroutRML2_filtered$fl, BullTroutRML2_filtered$age,

xlim = c(0,500), ylim = c(0,15),

main = “Plot 1 : Harrison Lake Trout”,

xlab = “Fork Length (mm))”, ylab = “Age(yrs)”,

pch = 21, cex = 0.5)

#Plot an “Age” histogram with the following specifications

#• Y axis label is “Frequency”

#• X axis label is “Age (yrs)”

#• Title of the histogram is “Plot 2: Harrison Fish Age Distribution”

#• The color of the frequency plots is “lightblue”

#• The color of the Title is “red”

hist(BullTroutRML2_filtered$age, xlab = “Age (yrs)”, ylab = “Frequency”,

main = “Plot 2: Harrison Fish Age Distribution”,

col = “lightblue”)

title(main = “Plot 2: Harrison Fish Age Distribution”, col.main = “red”)

#Create a plot using the same specifications as the previous scatterplot. But,

#• Title the plot “Plot 3: Harrison Density Shaded by Era”

#• Y axis label is “Age (yrs)”

#• Y axis limits are 0 to 15

#• X axis label is “Fork Length (mm)”

#• X axis limits are 0 to 500

#• include two levels of shading of blue for the data points based on era values.

#• Plot solid diamonds as data points

ggplot(BullTroutRML2_filtered, aes(x = fl, y = age, color = era)) +

geom_point(shape = 4, size = 3) +

ggtitle(“Plot 3: Harrison Density Shaded by Era”)+

xlab(“Fork Length (mm)”) +

ylab(“Age (yrs)”) +

scale_color_manual(values = c(“darkblue”,”lightblue”)) +

xlim(0,500) + ylim(0,15)

#Create a new object called “tmp” that includes the first 3 and last 3 records of the

#BullTroutRML2 dataset.

head(BullTroutRML2, 3)

tail(BullTroutRML2, 3)

tmp <- rbind(head(BullTroutRML2, 3), tail(BullTroutRML2, 3))

#Display the “era” column (variable) in the new “tmp” object

tmp$era

#Create a pchs vector with the argument values for + and x.

#Create a cols vector with the two elements “black” and “red”

pchs <- c(“+”, “x”)

cols <- c(“black”, “red”)

# Convert the tmp era values to numeric values.

# Initialize the pchs and cols vector conditional on the tmp era values

tmp$era <- as.numeric(tmp$era)

tmp$pch <- pchs[tmp$era]

tmp$col <- cols[tmp$era]

#Create a plot of “Age (yrs)” (y variable) versus “Fork Length (mm)” (x variable) with the

#following specifications:

# • Title of graph is “Plot 4: Symbol & Color by Era”

#• Limit of x axis is (0,500)

#• Limit of y axis is (0,15)

#• Y axis label is “Age (yrs)”

#• X axis label is “Fork Length (mm)”

#• Set pch equal to pchs era values

#• Set col equal to cols era values

ggplot(tmp, aes(x = fl, y = age, pch = pch, col = col)) +

geom_point() +

xlim(0, 500) +

ylim(0, 15) +

xlab(“Fork Length (mm)”) +

ylab(“Age (yrs)”) +

ggtitle(“Plot 4: Symbol & Color by Era”)

#Plot a regression line (blue color) overlay on Plot 4 and title the new graph “Plot 5:

#Regression Overlay”.

ggplot(tmp, aes(x = fl, y = age)) +

geom_point() +

geom_smooth(method = “lm”, se = FALSE, color = “blue”) +

xlim(0, 500) +

ylim(0, 15) +

xlab(“Fork Length (mm)”) +

ylab(“Age (yrs)”) +

ggtitle(“Plot 5: Regression Overlay”)

#Place a legend of on Plot 5 and call the new graph “Plot 6: Legend Overlay”

ggplot(tmp, aes(x = fl, y = age, pch = pch, col = col)) +

geom_point() +

geom_smooth(method = “lm”, se = FALSE, color = “blue”) +

xlim(0, 500) +

ylim(0, 15) +

xlab(“Fork Length (mm)”) +

ylab(“Age (yrs)”) +

ggtitle(“Plot 6: Legend Overlay”)

Here is the case for week 9 SOAP note. As with prior assignments, please answer the questions at the end of the SOAP note. Make sure to outline your specific plans in your SOAP note.

Marcia is a 28 year old female who comes in with a complaint of nipple drainage. She says that the discharge started about three months ago and it appears milky. She says that she has also felt tired, and has been constipated lately. Her periods have been very light. She is worried that she “might have cancer”.

1. What are the three classifications of nipple discharge?
2. Based on the above description, what classification does this fit into?
3. Aside from the above concerns, name three other things this woman may be experiencing.
4. Women with a strong family history of breast cancer may be candidates for genetic testing for __________ mutations.
5. You do a thorough breast examination on this pt. What are two important components of this exam on anyone with nipple drainage?
6. You do a complete medication history as well as elicit past medical history, including endocrine and reproductive history. What are four important parts of the exam on this pt and why?
7. Name five chemical agents that can cause nipple discharge and galactorrhea.
8. What should the nipple discharge be tested for?
9. Why are cytologic studies not recommended?
10. Should diagnostic mammography be performed?
11. Following diagnostic mammography, what other imaging study might be performed and why?
12. What are two important lab tests to obtain?
13. When would an MRI of the brain be indicated?
14. Name three possible diagnoses for this patient.
15. If galactorrhea is suspected due as a result of a chemical origin, what must be done?
16. Galactorrhea is more common in _____________ (premenopausal/postmenopausal) women.
17. All patients with spontaneous or unilateral nipple discharge regardless of color, should be referred for surgical evaluation.
18. Patients should be educated that most causes of breast discharge are malignant.
19. In women with galactorrhea, where the prolactin level is normal and menses are normal, women should be informed of the normal physiologic association with ______________________.
20. Why is nipple discharge so concerning to most women?

1) What sources of news do you usually trust? What sources do you rarely trust? Why?
I usually trust news that can be obtained through databases. For example, the CSI library
database allows us to access the New York Times, which is edited and verified prior to
publication. Another newspaper that I often read is the Washington Post, which my family has a
subscription to. Obtaining news from reliable sources, such as via databases, ensures that the
information provided is both accurate and informative. Sources that I do not trust are posts on
social media. For example, it is easy for someone to read an article and then summarize it on
their Facebook wall or Instagram story. However, this information is now reworded and can
incorporate some biases from the person who rewrote it. I also do not trust news obtained from
Google, because anyone can publish writing on the Internet without it being verified first.
2) How do you check to make sure what you are sharing or commenting on is real when using
FaceBook, Instagram, or any other social media tool?
One way that we can make sure that what we are sharing or commenting on is real is by doing
a reverse image search in Google images. This will verify the original source of the image and
see if the photo has been used in different places. It is common for old images to be reused and
portrayed falsely in order to spread fake news. Another way to see if information is true is by
seeing if it is being shown in major news sources. If the information is not covered in major news
sources, then it is most likely fake. There are even websites, such as Snopes.com, that sort
through the fake news that is widely spread on social media. They even prove these stories
wrong to prevent people from believing this false information. Facebook posts can even have a
“disputed” symbol to indicate that this information is not true. Finally, librarians are also
extremely helpful at showing us how to detect fake news on social media sites, as they are all
versed in this area

• Embassy Club Condominium, located on the west coast of Florida, is undertaking a summer renovation of its main building. The project is scheduled to begin May 1, and a September 1 (17-week) complete date is desired. The condominium manager identified the following renovation activities and their estimated times:
• Doug Casey is in charge of planning and coordinating next spring’s sales management training program for his company. Doug listed the following activity information for this project:

• Draw a project network.
• What are the critical activities?
• Will the project be completed by September 1?

• Draw a project network.
• What are the critical activities and what is the expected project completion time?
• If Doug wants a 0.99 probability of completing the project on time, how far ahead of the scheduled meeting date should he begin working on the project?

Within the Discussion Board area, write 400–600 words that respond to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions by your classmates. Be substantive and clear, and use examples to reinforce your ideas.

Theresa and Mike understand that building the culture they envision for the newly merged company JEANSTYLE requires effective communication, collaboration, and participative problem solving. They envision creating an organization such that Joe and other employees will know exactly what to do when faced with choices that involve ethics. As the first step in embedding clear ethical processes in the organization, they realize they need to devise a code of conduct. Both companies have operated with an unwritten code that was never formalized. You have been asked to help them understand the purpose and content of an excellent code of conduct and how they should go about developing it.

Review the scenario for this course, and answer the following questions:

• What is the purpose of a code of conduct?
• Evaluate the code of conduct of Levi Strauss, and describe the ethical principles it includes.
• In your own work experience, have you seen similar ethical principles used in organizations and, if so, which ones?

• Ross White’s machine shop uses 2500 brackets during the course of a year, and this usage is relatively constant throughout the year. These brackets are purchased from a supplier 100 miles away for $15 each, and the lead time is 2 days. The holding cost per bracket per year is $1.50 (or 10% of the unit cost) and the ordering cost per order is $18.75. There are 300 working days per year.

• Develop a total cost model for this system.
• What is the EOQ for this problem?
• What is the cycle time?
• What is the reorder policy?
• What are the total annual holding and ordering costs associated with your recommended EOQ?
• What is the total annual cost associated with your recommended EOQ?

• Critically evaluate the significance and the relationship between ethics and Business as explained by the author. (Not less than 500 words-5 Marks)
• With suitable examples (at least 2) discuss the importance of trust in business. What happens when trust is lost? (Not less than 500 words-5 Marks)
• Discuss how ethics could be incorporated in education, particularly for business students. (Not less than 500 words-5 Marks)

This part is worth 30 points.
Before getting started, practice writing Python expressions and assignment statements using
the Python interpreter! If nothing else, repeat some of the problems we worked on during lecture
to see if you can solve them yourself…
For this part of the homework you are going to write one Python (.py) file that outputs the
results of several calculations. Your code should use variables, expressions, assignment statements
and print statements. You may write a function to make your code shorter, as discussed in Lecture 3,
but while it is desirable it is not necessary in order to earn full credit. To make your code easier to
read, your .py file should have blank lines separating the groups of calculations.
The distances (in miles) of the planets to the sun and the diameters of the five planets closest
to the sun are as follows:
Planet Diameter Distance
Mercury 3,032 35,983,610
Venus 7,531 67,232,360
Earth 7,926 92,957,100
Mars 4,222 141,635,300
Jupiter 88,846 483,632,000
Write Python code that outputs the name ‘Mercury’ and then outputs on each successive line
• its distance to the sun relative to the distance of the earth from the sun,
• its diameter relative to the earth’s diameter,
3
• the time, in minutes, that light takes to travel from the sun to Mercury (use 186,000 miles
per second as the speed of light), and
• Mercury’s volume relative to that of the earth.
When you run the program (in the Wing IDE) the output should look something like
Mercury
relative distance 0.387099102704
relative diameter 0.382538480949
light time (minutes) 3.22433781362
relative volume 0.0559790323342
Repeat the calculations for Venus, Mars and Jupiter, outputting a blank line after the output
for each planet. At this point, you can introduce a function if you wish to perform the calculations
for a single planet and then call this function once for each of the four planets.
The final output from your program should be the average distance of the five planets from the
sun and the average diameter of the planets. You may use integer arithmetic to complete these
calculations.
The complete output from your program should look very close to
Mercury
relative distance 0.387099102704
relative diameter 0.382538480949
light time (minutes) 3.22433781362
relative volume 0.0559790323342
Venus
relative distance 0.723262236021
relative diameter 0.950164017159
light time (minutes) 6.02440501792
relative volume 0.857477483265
Mars
relative distance 1.52366306608
relative diameter 0.532677264698
light time (minutes) 12.6913351254
relative volume 0.151144546868
Jupiter
relative distance 5.20274406151
relative diameter 11.209437295
light time (minutes) 43.3362007168
relative volume 1408.48243619
Average planetary distance in miles is 164288074
Average planetary diameter in miles is 22311
In order to complete Part 1 of the HW 1, please submit the file (e.g. part1.py) containing your
solution to the submission server. Be sure to submit for Part 1, which is the second submission line
on the website. Do not submit anything other than your single Python file!
4
Final note: You could write a Python program that simply prints the output provided above.
It would start with something like
print “Mercury”
print ” relative distance 0.387099102704″
print ” relative diameter 0.382538480949″
print ” light time (minutes) 3.22433781362″
print ” relative volume 0.0559790323342″
print “”
etc. If you do this, you will earn 0 points for the assignment. You must let Python do all of the
calculations. To illustrate, if we suddenly realized that we had mistyped Earth’s distance from the
sun, you should have to change only one line of your code and the rest of your program should
work correctly! The same is true of Part 2.
Part 2
This part is worth 30 points.
Before getting started, practice writing Python functions using the Python interpreter! If
nothing else, repeat some of the problems we worked on during lecture to see if you can solve them
yourself…
The (fictional) Acme computing and social networks company has large data centers distributed
throughout the world and these centers consume a large amount of energy each year. Environmental
organizations would like Acme to reduce its carbon footprint, and pay in “carbon credits” for the
pollution it produces.
• Each data center is formed by a sequence of standard shipping “containers”, each containing
1,160 servers, and each requiring about 250 kilowatts of energy. A common measurement of
energy usage is to multiple the kilowatts times a unit of time, typically an hour, yielding the
kilowatt-hour. As a sanity check to make this clear, these numbers imply that a container
uses 250 × 24 × 365 ≈ 2 million kilowatt-hours per year. To give a sense of the scale of this
number, an efficient refrigerator uses about 500 kilowatt-hours of energy per year.
• Each data center uses about 200 containers, and Acme has about 15 data centers.
• The unnamed environmental organization wants Acme to pay $12 per ton of carbon used,
and Acme consumes about 0.0006 tons of carbon per kilowatt-hour.
Using this information, write a Python program to calculate and output how much enery Acme
uses for the year 2012 and how much Acme must pay in carbon credits. Assuming Acme adds 4
containers to each center in each year and adds 2 data centers in 2013, 5 in 2014, and 3 in 2015,
how much must Acme pay in carbon credits each year?
The central part of your program is that you must write a function that takes the number of
centers that Acme has and the number of containers per center and calculates the amount of money
Acme must pay.
The output from your program should be
In 2012 Acme should pay $ 47304000.0
In 2013 Acme should pay $ 54683424.0
In 2014 Acme should pay $ 72154368.0
In 2015 Acme should pay $ 83570400.0