All Cases For
SM TB DATA VISUALIZATION EXPLORING AND EXPLAINING WITH DATA 2ND EDITION COPYRIGHT
2025 BY J EFFREY D. CAMM, J AMES J. COCHRAN, MICHAEL J. FRY, J EFFREY W. OHLMANN DATA,
MULTI-CHAPTER CASES
TABLE OF CONTENTS
Case Problem 1: Piedmont General Hospital ........................................................................................... 1
Case Problem 2: Bayani Bulb Bases.......................................................................................................... 5
Case Problem 3: International Monetary Fund Housing Affordability .............................................. 13
Case Problem 4: Short Term Rental Market ......................................................................................... 17
CASE PROBLEM 1: PIEDMONT GENERAL HOSPITAL
Piedmont General Hospital (PGH) is a large healthcare facility located in the southeastern United States, renowned for its
commitment to quality care of its patients. Megan Avery is the Vice President of Quality for PGH. Megan manages the
team responsible for implementing and overseeing all quality and risk control programs.
PGH has recently noticed an uptick in the readmission rate. A readmission is when a discharged patient returns to
the hospital for the same or related care within 30 days of being discharged from the hospital. Readmissions strain
resources and negatively impact patient outcomes and patient satisfaction. Furthermore, for patients on Medicare,
the Hospital Readmissions Reduction Program passed by Congress financially penalizes hospitals by reducing the
amount paid for services when the hospital exceeds their expected number of readmissions. PGH has, to date, never
exceeded its expected number of readmissions, and Megan wants to ensure that PGH continues to have fewer
readmissions than expected.
Megan has asked her team to investigate factors that lead to higher readmission rates. Megan believes that if factors
that seem related to higher readmission rates can be identified from the analysis of patient records, the team will be
able to create strategies to mitigate the number of readmissions. To that end, David Moore, a data analyst on
Megan’s team, has collected a sample of 500 records for patients admitted for congestive heart disease.1 The data set
in the file readmissions includes the following variables:
Patient ID Number – patient identification numbers from 1 to 500 to protect the privacy of the patient
Age – the age of the patient at the time of the original admission to the hospital
Comorbidities – health conditions other than congestive heart failure present in the patient
Length of Stay – duration of the stay in the hospital in the original admission
Discharge Instructions – instructions given to the patient when discharged (exercise and/or medication)
Compliance – how well the patient complied with the discharge instructions (scored as low, medium, or
high)
1
The data in this case are simulated.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 1
accessible website, in whole or in part.
, Readmission status – whether or not the patient was readmitted for congestive heart failure within 30 days
of discharge (1 = yes and 0 = no)
Now, David needs to analyze the data and develop a report for Megan. LO 2.1, 2.2, 2.3,
2.4, 6.3, 6.5
SUMMARY REPORT
David’s goal is to develop a report that shows which factors (if any) seem to be related to patient readmission. Your
job is to conduct the analysis and help David with the report. Begin by investigating each of the following
relationships with an appropriate chart:
1. How is readmission rate related to age? Consider age as a categorical variable with categories 40–49, 50–
59, 60–69, and 70–80, and construct a column chart to answer this question.
2. How is readmission rate related to length of stay?
3. Is readmission rate higher for certain comorbidities?
4. How is readmission rate related to compliance?
5. Construct a clustered column chart with readmission rates for the comorbidities by age category.
Based on your analysis, what are the characteristics of patients that should be targeted first for interventions and/or
special follow-up care?
Your report should include the charts and tables generated to answer the questions posed and a brief description and
conclusion of each.
Solution:
If we convert age from a continuous variable to categories of ranges of ages, all of the variables except length of
stay can be viewed as categorical. Hence, we will rely on column charts to explore the 500 patient records. We begin
by answering each posed question, and then we will provide a recommendation.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 2
accessible website, in whole or in part.
, How is readmission rate related to age? Consider age categories 40–49, 50–59, 60–69 and 70–80.
The following column chart shows that the older age categories have higher readmission rates.
How is readmission rate related to length of stay?
The following chart shows that the lowest length of stay (2 days) and the two highest lengths of stay (9 and 10 days)
have the highest readmission rates.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 3
accessible website, in whole or in part.
SM TB DATA VISUALIZATION EXPLORING AND EXPLAINING WITH DATA 2ND EDITION COPYRIGHT
2025 BY J EFFREY D. CAMM, J AMES J. COCHRAN, MICHAEL J. FRY, J EFFREY W. OHLMANN DATA,
MULTI-CHAPTER CASES
TABLE OF CONTENTS
Case Problem 1: Piedmont General Hospital ........................................................................................... 1
Case Problem 2: Bayani Bulb Bases.......................................................................................................... 5
Case Problem 3: International Monetary Fund Housing Affordability .............................................. 13
Case Problem 4: Short Term Rental Market ......................................................................................... 17
CASE PROBLEM 1: PIEDMONT GENERAL HOSPITAL
Piedmont General Hospital (PGH) is a large healthcare facility located in the southeastern United States, renowned for its
commitment to quality care of its patients. Megan Avery is the Vice President of Quality for PGH. Megan manages the
team responsible for implementing and overseeing all quality and risk control programs.
PGH has recently noticed an uptick in the readmission rate. A readmission is when a discharged patient returns to
the hospital for the same or related care within 30 days of being discharged from the hospital. Readmissions strain
resources and negatively impact patient outcomes and patient satisfaction. Furthermore, for patients on Medicare,
the Hospital Readmissions Reduction Program passed by Congress financially penalizes hospitals by reducing the
amount paid for services when the hospital exceeds their expected number of readmissions. PGH has, to date, never
exceeded its expected number of readmissions, and Megan wants to ensure that PGH continues to have fewer
readmissions than expected.
Megan has asked her team to investigate factors that lead to higher readmission rates. Megan believes that if factors
that seem related to higher readmission rates can be identified from the analysis of patient records, the team will be
able to create strategies to mitigate the number of readmissions. To that end, David Moore, a data analyst on
Megan’s team, has collected a sample of 500 records for patients admitted for congestive heart disease.1 The data set
in the file readmissions includes the following variables:
Patient ID Number – patient identification numbers from 1 to 500 to protect the privacy of the patient
Age – the age of the patient at the time of the original admission to the hospital
Comorbidities – health conditions other than congestive heart failure present in the patient
Length of Stay – duration of the stay in the hospital in the original admission
Discharge Instructions – instructions given to the patient when discharged (exercise and/or medication)
Compliance – how well the patient complied with the discharge instructions (scored as low, medium, or
high)
1
The data in this case are simulated.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 1
accessible website, in whole or in part.
, Readmission status – whether or not the patient was readmitted for congestive heart failure within 30 days
of discharge (1 = yes and 0 = no)
Now, David needs to analyze the data and develop a report for Megan. LO 2.1, 2.2, 2.3,
2.4, 6.3, 6.5
SUMMARY REPORT
David’s goal is to develop a report that shows which factors (if any) seem to be related to patient readmission. Your
job is to conduct the analysis and help David with the report. Begin by investigating each of the following
relationships with an appropriate chart:
1. How is readmission rate related to age? Consider age as a categorical variable with categories 40–49, 50–
59, 60–69, and 70–80, and construct a column chart to answer this question.
2. How is readmission rate related to length of stay?
3. Is readmission rate higher for certain comorbidities?
4. How is readmission rate related to compliance?
5. Construct a clustered column chart with readmission rates for the comorbidities by age category.
Based on your analysis, what are the characteristics of patients that should be targeted first for interventions and/or
special follow-up care?
Your report should include the charts and tables generated to answer the questions posed and a brief description and
conclusion of each.
Solution:
If we convert age from a continuous variable to categories of ranges of ages, all of the variables except length of
stay can be viewed as categorical. Hence, we will rely on column charts to explore the 500 patient records. We begin
by answering each posed question, and then we will provide a recommendation.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 2
accessible website, in whole or in part.
, How is readmission rate related to age? Consider age categories 40–49, 50–59, 60–69 and 70–80.
The following column chart shows that the older age categories have higher readmission rates.
How is readmission rate related to length of stay?
The following chart shows that the lowest length of stay (2 days) and the two highest lengths of stay (9 and 10 days)
have the highest readmission rates.
© 2025 Cengage Learning, Inc. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly 3
accessible website, in whole or in part.
