FOR PART 2 – DATA VISUALIZATIONYou will have completed Orientation to Data Analytics I and Applied Data Analytics II in previous weeks which helped provide a foundation for this assignment. Additionally, the completion of the prerequisite activity below is recommended to successfully complete Part 2 of this week’s assignment:Prerequisite: Applied Data Analytics IIIFOR PART 3 – NATIONAL DATA SET ANALYSISThe informaticists in the city health department in the program evaluation section above wants to demonstrate the value of its tobacco prevention and cessation program by using comparative data.Website: Center for Disease Control and Preventionhttps://data.cdc.gov/
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General Instructions for submission of work
Provide your answers directly on this activity document as you complete all 3 Parts of this final Week 8
assignment. When you are finished, save this activity document to your device and upload this activity
document with your answers to your Learning Management System (LMS), also referred to as Engage.
Note that this Week 8 Assignment has 3 Parts:
• Part 1 – Answer the Program Evaluation Case Study Questions
• Part 2 – Data Visualization
• Part 3 – National Data Set Analysis
Part 1: Program Evaluation (Does not use EHR Go)
Instructions
Answer the questions below based on the Casey Study provided in your course – see Week 8
course content to download the Case Study. While this is not a formal paper, please be sure to
cite your sources (you must cite a minimum of at least 1 source) using APA format when
providing your answers.
Please note that by completing this assignment you are abiding by the following academic
honesty statement:
I hereby attest that by submitting this assignment, that the work and writing are my own, that any
direct quotations have been properly cited and that I have properly cited references where I have
used someone else’s ideas.
Additional resources for completing Part 1:
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Centers for Disease Control and Prevention. (2017). A framework for program evaluation.
Retrieved from https://www.cdc.gov/eval/framework/
Measure Evaluation: http://www.measureevaluation.org/
Questions
Answer the following questions about the program evaluation using an established framework for
program evaluation.
1. Identify a framework that can be used for the program evaluation and its key features.
2. Who are the potential stakeholders for this program. Please list and indicate why each person, or
representative of a group should be included.
3. What strategies will be used to engage the stakeholders with the program evaluation goals, share
power and resolve conflicts?
4. Provide a description of the intervention that is being evaluated. What is it that the program is trying
to accomplish? What need is the program serving? How does the program fit into the larger goals of the
organization?
5. What types of evaluation questions would you ask? Is a formative or summative evaluation most
appropriate, or would you use both? Explain your answer.
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6. What are the intended results of the program? Specify the outcomes. What will the program need to
do to be considered successful?
7. What criteria can be used to judge program performance? What standards of performance on the
criteria must be reached for the program to be considered successful? You may use a table for this
answer.
8. What evidence will indicate performance on the criteria relative to the standards?
9. What are the sources of the evidence or data for each of the criteria for the program evaluation?
10. What processes could be put in place to make the data collection for the program evaluation is easy
to use, valid, and reliable? Reliability refers to the reproducibility of the data. Validity refers to whether
the measure is truly measuring the concept, or outcome.
11. How can the program evaluation data be shared with stakeholders? Health care providers? Health
information exchanges? Funding agency?
12. Summarize how the health informaticist can create value in the transformation of data to
information to support goals of a program evaluation.
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Part 2: Knowledge Activity: Orientation to Data
Visualization IV (Informatics)
Learning objectives
1. Analyze data to identify trends.
2. Evaluate administrative reports using appropriate software.
Prerequisites
1. Use of Microsoft Excel® is required to complete this activity
2. This activity is the fourth activity in a sequential 5-activity series. Completion of the
activities below is required to successfully complete this activity:
• Orientation to Data Analytics I
• Applied Data Analytics II
• Applied Data Analytics III
Student instructions
1. If you have questions about this activity, please contact your instructor for assistance.
2. You will review the de-identified chart that accompanies this activity. Your instructor
has provided you with a link to the Orientation to Data Visualization IV (Informatics)
activity. Click on 2: Launch EHR to review the patient chart and begin this activity.
3. Refer to the patient chart and any suggested resources to complete this activity.
4. Document your answers directly on this activity document as you complete the activity.
When you are finished, you will save this activity document to your device and upload
this activity document with your answers to your Learning Management System (LMS).
Introduction
The goal of data visualization is to extract meaningful insights from data to help with identifying
trends, making decisions, and driving change. (Meyer, 2017). In the healthcare field, this may
involve analyzing population health data, operational data (like claims and costs), performance
data (like length of stay), and other patient health data. With the growth of EHRs, more and
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more data are becoming available, and the importance of accurately interpreting results is
concurrently on the rise.
Methods for data visualization vary from summary tables, to graphs and charts, to sophisticated
storyboards and dashboards. Regardless of the type of visual, it’s important to know the target
audience and what information they value – then summarize it as clearly and concisely as
possible. In general, simpler is better. (Bresnick). This activity and subsequent data visualization
activities in EHR Go will use Microsoft Excel®. There are many other software options available
and you are encouraged to experiment with using additional tools.
Another important aspect of data visualization is accuracy so as not to mislead the audience
with how the data are presented. Ensuring your data set is comprehensive, accurate, and
unbiased is the first step before beginning to analyze it. Understanding the difference between
correlation and causation is critical when drawing conclusions. Correlation is a statistic that
describes the relationship of two or more variables. Two sets of data may be strongly
correlated but a change in one may not be the cause of a change in the other. Whereas with
causation, one event is the result of the occurrence of the other event. (Statistics). A popular
example of this in healthcare is that smoking is correlated with alcoholism but is not the cause
of alcoholism. However, smoking does cause an increase in the risk of developing lung cancer.
(iPerceptions, 2016). If you’re not careful, it’s easy for correlation to confound the results of a
study.
The activity
Review the de-identified EHR that accompanies this activity under 2: Launch EHR and answer
the following questions.
Microsoft Excel® for data visualization
Using the leading causes of death data set provided from the Centers for Disease Control and
Prevention (CDC) Data Visualization Gallery website, replicate the chart pictured below to
present the results. Then explore other ways to view the data and draw conclusions.
In the following steps, the bar chart shown below will be re-created using Excel. Follow each
step carefully.
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(Tejada-Vera B, 2019).
Open the resource CDC Leading Causes of Death Data Set Excerpt (found under 1: Overview &
Resources along with this activity document) in Excel. A data set with 5 columns should appear
as pictured below.
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Making pivot tables
Click and drag to select the 5 columns of data.
Select Insert|PivotTable. Leave the default settings at the dialog box and select OK.
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A new worksheet will open to begin the pivot table process.
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Use the pivot table to summarize the age-adjusted death rate for each type for the year
2015. To do so:
Click and drag the ‘Year’ field to the Filters area. This will allow each year to display.
Click and drag the ‘Cause Name’ field to the Rows area. This will list each cause as a row
in the table.
Click and drag the ‘Age-adjusted Death Rate’ field to the ? Values area. It should appear
as ‘Sum of Age-adjusted Date Rate’. This will display the total number of deaths for each
cause.
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Next, specify the year in the resulting table. Select the dropdown arrow in cell B1 where
it currently states ‘All’. Then check the Select Multiple Items box and specify only 2015.
Then OK. The data table will automatically update to only show the number of deaths
for the year 2015.
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Remove the data listing for ‘All Causes’ so the table only displays the specific causes of
death. Select the dropdown arrow in cell A3 where it states ‘Row Labels’ and uncheck
the ‘All Causes’ option followed by OK.
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To create a similar bar graph as displayed by the CDC, the table needs to be sorted to display
the lowest number first, instead of alphabetically.
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Select the death rate for Alzheimer’s (cell B4). Right click and select Sort followed by
Smallest to Largest.
The resulting data table should look like this:
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Making bar graphs
Select any cell in the pivot table then select ‘Analyze’ in the top, center of the Excel window.
Choose PivotChart. Select the Bar option then OK.
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The chart will be inserted.
Move the chart on the page and make it larger (click and drag one of the corners) to see
all the data.
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Adjust the chart to make it more visually appealing.
Click on the small legend box on the right where it says ‘Total’ and delete it.
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Next, click on the title field where it currently says ‘Total’ right above the bar graph.
Type in the new title, “Age-adjusted Death Rates for the 10 Leading Causes of Death: United
States, 2015”.
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The chart is showing the 10 leading causes. Remove the six least common causes.
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Select the ‘Cause Name’ dropdown and uncheck the following causes: Septicemia,
Chronic liver disease and cirrhosis, Essential hypertension…,Parkinson’s disease,
Homicide, and Pneumonitis… Then OK. The chart will automatically update.
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Add some color. Right click on any of the existing bars in the chart and select Format
Data Series…
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Select the Fill & Line icon (emptying paint bucket) then select the Vary colors by point
option. Hold off on closing this window.
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Also use the Format Data Series options to change the thickness of each bar. Select the
Series Options (column chart) icon then decrease the Gap Width field to about 10%.
Close the window by clicking the small ‘x’ in the upper right. The chart will update.
(Note: the colors won’t be the same as the CDC display – that’s okay).
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Next, scale the x-axis. Double-click on any of the x-axis values to open the Format Axis
settings. Change the Maximum Bounds to 250. In the Number section, select ‘Number’
from the Category dropdown and select 1 for the Decimal places
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Finally, add a label to the x-axis. Click anywhere on the chart then select the Design
menu at the top of the screen. Select Add Chart Element then Axis Titles and Primary
Horizontal. Click on the new text box that currently says Axis Title and enter the new
title “Rate per 100,000 U.S. standard population”.
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Now that the chart is complete, right click on the white space above the chart, just
above the title, and choose Copy. Paste the chart into a Word document or PowerPoint
and it will appear without the pivot data fields.
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Communicating the data
One of the most important parts of data visualization is being able to clearly communicate the
findings, both visually and through text.
Questions
13. Paste the bar graph you created here.
14. Describe the bar graph in 50 words or less.
Part 3. Data Set or Chart Analysis
(Does not use EHR Go)
Instructions: The following questions relate to publicly available data available through the Centers for
Disease Control and Prevention. Data.cdc.gov
The informaticists in the city health department in the program evaluation section above wants to
demonstrate the value of its tobacco prevention and cessation program by using comparative data.
15. Identify a data set or chart from the Centers for Disease Control and Prevention that is related to
tobacco prevention and cessation. Provide the full name of the data set or chart and a link to it. Indicate
why this data or chart would be useful for comparative data for the program evaluation.
16. What variables or data elements are available in this data set or chart that can be used for this
analysis?
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17. Identify a specific observation that can be made in examining this data set or chart that would be
useful to the city health department.
18. Go back to the week 1 assignment and review the community health needs assessment (CHNA) that
you selected for that assignment. Did the CHNA identify tobacco use as a problem in the community? If
so, what was the data source and what is the plan for the organization to address the problem? Please
provide a link to the CHNA and identify the page number. If the CHNA didn’t identify tobacco use as a
problem, does the CDC data set or chart downloaded provide any clues for its lack of inclusion?
Submit your work
Document your answers directly on this activity document as you complete the activity. When you are
finished, save this activity document to your device and upload this activity document with your answers
to your Learning Management System (LMS). You answers for all 3 Parts of this assignment should be
included on this ONE document. If you have any questions about submitting your work to your LMS,
please contact your instructor.
References
Bresnick, J. (n.d.). Using Visual Analytics, Big Data Dashboards for Healthcare Insights. HealthIT Analytics.
Retrieved from https://healthitanalytics.com/features/using-visual-analytics-big-datadashboards-for-healthcare-insights
iPerceptions. (2016). Causation vs Correlation – What’s the difference. Retrieved from
https://www.iperceptions.com/blog/causation-vs-correlation
Meyer, M. (2017). The Rise of Healthcare Data Visualization. Journal of AHIMA. Retrieved from

The Rise of Healthcare Data Visualization

Statistics, A. B. (n.d.). Statistical Language – Correlation and Causation. Retrieved from
http://www.abs.gov.au/websitedbs/a3121120.nsf/home/statistical+language++correlation+and+causation
Tejada-Vera B. (2019). Leading causes of death: United States, 1999–2017. National Center for Health
Statistics. Retrieved from https://www.cdc.gov/nchs/data-visualization/mortality-leadingcauses/index_2.htm (with “Select Data Year” adjusted to “2015”)
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