In this teaching (fictitious) case study, the authors explore how actual HCAHPS survey data can affect CMO reimbursement and thus revenue. Specifically the authors focused on nurse communication scores as a way to enhance patient experience and increase reimbursements. by @cruzcerda3

1. HCAHPS, DATA-DRIVEN ANALYSIS:
HOW TO IMPROVE TOP BOX SCORES FOR
COMMUNICATION
For Vila Health (A FICTICIOUS CASE. STUDY)
By G***C**n and Cruz Cerda
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

2. Analytics Project Final Report:
An Overview
MAGIC MINUTE SUMMARY IN DEPTH
PROJECT MANAGEMENT PROCESS
DATA STRATEGY TO FACILITATE
CHANGE
ETHICS AND DATA INTEGRITY
ANALYTICS STAKEHOLDER VIEWPOINT
HCAHPS: COURTESY AND RESPECT
HCAHPS: EXPLAIN
HCAHPS: LISTEN
RECOMMENDATIONS, NEXT STEPS
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

3. Magic Minute Summary
Problem
• Evolving and changing healthcare – need new solutions for new problems
• Vila Health lacks alignment of strategic goals and HCAHPS data
Focus
• Nurse Communication HCAHPS (Top-Box Scores)
• Doctor Communication HCAHPS (Top-Box Scores)
Course of
Action
• Improve: Nurse Communication AND Patient Experience
• Financial: Positive Impact on CMS Reimbursement (REVENUE INCREASE)
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

4. Project
Management
Process:
John Kotter 8-
Step Model
First step:
Create urgency
Second step:
Form a powerful
coalition
Third step:
Create a vision
for change
Fourth steep:
Communicate
the vision,
Fifth Step:
Remove
obstacles
Sixth step:
Create short
term wins
Seventh step
Build in the
change
Eighth step:
Anchor the
change
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

5. DATA STRATEGY TO FACILITATE CHANGE
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

6. Ethics and Data Integrity
HIPAA FERPA
APA Ethics Code
4.01
Maintaining
Confidentiality
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

7. Analytics
Stakeholder
Viewpoint
‘Ineffective communication
is the most frequently cited
cause for sentinel events in
the United States.’
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

8. HCAHPS Summary
Findings
Findings
ü Doctors scored nearly 15 percentage
points more than nurses in top-box
scores
ü Nurse scores show that there is room
for improvement
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

9. HCAHPS: Courtesy
and Respect (Q1)
Finding:
Patients perceived Doctors as showing
more courtesy and respect than Nurses
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

10. HCAHPS: Listen
(Q2)
Finding:
Patients perceived Doctors as better listeners
than Nurses.
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

11. HCAHPS: Explain
(Q3)
Finding:
Patients perceived Doctor as more adept at
explaining medical conditions than Nurses.
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

12. 'Going forward, the success of healthcare
organizations will depend on internalizing patient
sentiment insights into everyday decision-making,
just as it is for nearly every other industry.'
(Anisingaraju & Kaushal, 2015)
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

13. Recommendations
Training: Age Group
specific Patient
Experience Training
Bedside Shift
Report
Purposeful Hourly
RoundingKey Findings and Opportunities:
• Goal: increase adjusted top-box communication
score for Nurse Communication Scores
• Focus on patients within specific age (18-29) range
as they are more likely to respond with a ’3’
Maternity age group: 18-29
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

14. Recommendations
Training: Age Group
specific Patient
Experience Training
Bedside Shift
Report
Purposeful Hourly
RoundingKey Findings and Opportunities:
• Goal: increase adjusted top-box communication
score for Nurse Communication Scores
• Focus on patients within specific age (70-79) range
as they are more likely to respond with a ’3’
Non-maternity age group: 70-79
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

15. Next Steps
Focus
on
Focus on non-maternity patients
within specific age (70-79) range
as they are more likely to respond
with a ’3’
Focus
on
Focus on maternity patients
within specific age (18-29) range
as they are more likely to respond
with a ’3’
Ask
and
solicit
Ask and solicit information on
communication preference as it
relates to technology.
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

16. References
Centers for Medicare & Medicaid Services. (2011). Calculation of HCAHPS Scores: From Raw Data to Publicly
Reported Results [Power Point Presentation].
DalleMule, L., & Davenport, T. (2017). The 2 Types of Data Strategies Every Company Needs. Harvard Business
Review. https://hbr.org/2017/05/whats-your-data-strategy
Dempsey, C., Reilly, B., & Buhlman, N. (2014). Improving the Patient Experience Real-World Strategies for
Engaging Nurses. The Journal of Nursing Administration, 44(3), 142–151. Retrieved from
https://doi.org/10.1097/NNA.0000000000000042
Drees, J. (2020). How 6 health systems are personalizing patient experience for baby boomers, millennials and
Gen Z. Beckers Health IT.
Kotter’s 8 Step Change Management Model - YouTube. (n.d.). Retrieved from
https://www.youtube.com/watch?v=7qlJ_Y8w5Yk
Rice, S. (2015). Using Analytics to Grow Your Ethics and Compliance Program | Chronicle.
https://chronicle.kennametal.com/using-analytics-to-grow-your-ethics-and-compliance-program/
Romani, P. W., & Schieltz, K. M. (2017). Ethical considerations when delivering behavior analytic services for
problem behavior via telehealth. Behavior Analysis: Research and Practice, 17(4), 312–324.
https://doi.org/10.1037/bar0000074
HCAHPS, DATA-DRIVEN ANALYSIS: HOW TO IMPROVE TOP BOX SCORES FOR COMMUNICATION
(a fictitious case study)

17. Data Analysis
Knowing that Vila Health is a system in the Midwest, we filtered all data for the Midwest
region: IA, KS, MN, MO, ND, NE, SD. Only eligible and complete surveys will be used to
calculate HCAHPS scores; any surveys with incomplete, incorrect or missing information were
not considered in the following calculations.
HCAHPS Composite Measure Calculation
“Top Box” = most positive response category; “Always” (or 4) for questions that
comprise Nurse and Doctor Communication (Nurse/Doctor courtesy and respect, Nurse/Doctor
listen, Nurse/Doctor explain). The top-box mean for each question is calculated by adding all
“Always” responses and dividing the sum by the total number of surveys.
Question Calculation:
“Always” Responses / Total Surveys – Incomplete Surveys
Result
Q1: Nurse Courtesy 655 / 917 0.714
Q2: Nurse listen 552 / 917 0.602
Q3: Nurse explain 568 / 917 0.619
Nurse Communication
composite mean (y)
Y = (655/917 + 552/917 + 568/917) / 3 0.645
Q5: Doctor Courtesy 744 / 917 0.811
Q6: Doctor listen 659 / 917 0.719
Q7: Doctor explain 641 / 917 0.699
Doctor Communication
composite mean (y)
Y = (744/917 + 659/917 + 641/917) / 3 0.743
1

18. Vila Health Patient Mix Variables (PMAs)
Variable Calculation: Result
HEDUC Add values from 1 through 6 and divide by total surveys 3.82
HHLTH Add values from 1 through 5 and divide by total surveys 3.34
HNENG If value is “Spanish” or “Other” = 1, else = 0, add and
divide by total surveys
0.03
H18-24
H25-34
H35-44
H45-54
H55-64
H65-74
H75-84
Calculate mean of each of the 7 age range variables 0.09
0.17
0.09
0.14
0.14
0.16
0.17
HMAT Count/sum maternity patient and divide by total surveys 0.25
HSURG Count/sum surgical patient and divide by total surveys 0.45
HMAT*AGE Count/sum maternity patient in 8 age groups and divide
by total surveys
0.45
HSURG*AGE Count/sum surgical patients in 8 age groups and divide by
total surveys
2.21
HRPCT Response Percentile = Lag Time Rank (lag time in days) /
Monthly Sample Size
2

19. Final HCAHPS Scores
The dataset did not contain dates for when the survey responses were finished by patients
in relation to their discharge date. The PMA below is missing the Response Percentile factor.
Additionally, we can’t tell into which quarter the patient’s surveys fall in relation to their
discharge date.
Survey Mode Adjustments
Top-box adjustments are made if the hospital uses telephone as the primary means of
survey. Out of Vila Health’s 917 surveys only 10 were collected via telephone, and therefore no
adjustments are made to the nurse or doctor communication composite scores.
Quarterly HCAHPS Adjusted Score
The sum of the unadjusted composite score (Y), hospital PMA, and survey mode
adjustment comprise a hospital’s quarterly adjusted HCAHPS score (Y’). Our dataset did not
Patient Mix Adjustment
(PMA)
Vila Health
Means
National
Means
Comm. w/
Nurses
PMA Equation:
Nurses
Comm. w/
Doctors
PMA Equation:
Doctors
HEDUC 3.82 3.65 0.021 0.0037 0.018 0.0031
HHLTH 3.34 2.82 0.053 0.0277 0.050 0.0260
HNENG 0.03 0.44 0.000 0.0002 -0.005 0.0020
HRPCT [not available] 0.16 0.002 [n/a] 0.002 [n/a]
AGE:
H18-24 0.09 0.05 0.042 0.0019 0.040 0.0018
H25-34 0.17 0.11 0.012 0.0008 0.013 0.0008
H35-44 0.09 0.08 -0.011 -0.0002 -0.009 -0.0001
H45-54 0.14 0.12 -0.032 -0.0008 -0.025 -0.0006
H55-64 0.14 0.18 -0.051 0.0019 -0.044 0.0016
H65-74 0.16 0.21 -0.051 0.0027 -0.052 0.0028
H75-84 0.17 0.19 -0.030 0.0006 -0.028 0.0005
Age 85+ (Reference) 0.08
SERVICE LINE: 0.0000
HMAT 0.25 0.15 -0.063 -0.0063 0.118 0.0118
HSURG 0.45 0.34 0.016 0.0018 -0.092 -0.0100
Medical (Reference) 0.51
INTERACTIONS:
HMAT*AGE 0.45 1.81 0.011 -0.0144 0.015 -0.0206
HSURG*AGE 2.21 0.38 -0.001 -0.0013 0.010 0.0180
PMA: 0.018 0.037
3

20. contain dates for when the data collection activities were completed in relation to the patient
discharge change; therefore, the data has been aggregated for all discharge dates.
• Y’ = Y + PMA + Mode Adjustment
• Nurse Communication: 0.645 + 0.018 + 0 = 0.663 (66.3%)
• Doctor Communication: 0.743 + 0.037 + 0 = 0.780 (78.0%)
The R code used to clean the data for this assignment is attached as an appendix and was
prepared in a Jupyter Notebook.
4

21. Reference
Centers for Medicare & Medicaid Services. (2011). Calculation of HCAHPS Scores: From Raw
Data to Publicly Reported Results [Power Point Presentation].
5

22. 9/15/2020 Week 10 Final Report
localhost:8888/nbconvert/html/Week 10 Final Report.ipynb?download=false 1/5
In [69]: # Read the file
data <- read.csv(file.choose())
In [70]: # Subset for Midwestern States
midwestern <- data[ which( data$FINAL_STATE == "IA" |
data$FINAL_STATE == "KS" |
data$FINAL_STATE == "MN" |
data$FINAL_STATE == "MO" |
data$FINAL_STATE == "ND" |
data$FINAL_STATE == "NE" |
data$FINAL_STATE == "SD"), ]
In [71]: # Remove any surveys with NA values
eligible <- na.omit(midwestern)
In [72]: # Further subset for needed columns
final_surveys <- subset(eligible, select =
c("FINAL_STATE", "AGE", "Q01_06", "Q02_06", "Q03_06", "Q05_06", "Q06_06", "Q07_06", "Q23_06", "Q24_06", "Q27_06",
"PRINCIPALREASONADMISSION", "PATIENTDISCHARGEDATE", "FINAL_MODE"))
In [73]: str(final_surveys)
In [74]: head(final_surveys, 10)
In [75]: # Recode the survey answers for Age
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 1, "18-24")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 2, "25-29")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 3, "30-34")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 4, "35-39")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 5, "40-44")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 6, "45-49")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 7, "50-54")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 8, "55-59")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 9, "60-64")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 10, "65-69")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 11, "70-74")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 12, "75-79")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 13, "80-84")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 14, "85-89")
final_surveys$AGE <- replace(as.character(final_surveys$AGE), final_surveys$AGE == 15, "90 or older")
In [76]: # Recode the survey answers for Self-Rated Health
final_surveys$Q23_06 <- replace(as.character(final_surveys$Q23_06), final_surveys$Q23_06 == 1, "Excellent")
final_surveys$Q23_06 <- replace(as.character(final_surveys$Q23_06), final_surveys$Q23_06 == 2, "Very Good")
final_surveys$Q23_06 <- replace(as.character(final_surveys$Q23_06), final_surveys$Q23_06 == 3, "Good")
final_surveys$Q23_06 <- replace(as.character(final_surveys$Q23_06), final_surveys$Q23_06 == 4, "Fair")
final_surveys$Q23_06 <- replace(as.character(final_surveys$Q23_06), final_surveys$Q23_06 == 5, "Poor")
'data.frame': 917 obs. of 14 variables:
$ FINAL_STATE : Factor w/ 50 levels "AK","AL","AR",..: 13 13 13 13 13 13 13 13 13 13 ...
$ AGE : int 4 8 12 13 11 6 3 12 11 12 ...
$ Q01_06 : int 4 4 4 4 4 2 3 4 3 4 ...
$ Q02_06 : int 4 4 4 4 4 2 3 3 3 4 ...
$ Q03_06 : int 4 4 4 4 4 2 4 4 4 4 ...
$ Q05_06 : int 4 3 4 4 3 4 4 3 4 4 ...
$ Q06_06 : int 4 3 4 4 3 4 4 3 4 4 ...
$ Q07_06 : int 4 4 4 4 2 4 4 3 4 4 ...
$ Q23_06 : int 1 1 2 3 4 3 2 3 3 4 ...
$ Q24_06 : int 4 3 1 2 4 6 4 3 3 3 ...
$ Q27_06 : int 1 1 1 1 1 1 1 1 1 1 ...
$ PRINCIPALREASONADMISSION: int 1 3 3 3 2 3 1 3 3 3 ...
$ PATIENTDISCHARGEDATE : int 200605 200605 200605 200605 200605 200606 200606 200606 200606 200606 ...
$ FINAL_MODE : int 1 1 1 1 1 1 1 1 1 1 ...
FINAL_STATE AGE Q01_06 Q02_06 Q03_06 Q05_06 Q06_06 Q07_06 Q23_06 Q24_06 Q27_06 PRINCIPALREASONADMISSION PATIENTDISCHARGEDATE FINAL_MO
97420 IA 4 4 4 4 4 4 4 1 4 1 1 200605
97423 IA 8 4 4 4 3 3 4 1 3 1 3 200605
97445 IA 12 4 4 4 4 4 4 2 1 1 3 200605
97446 IA 13 4 4 4 4 4 4 3 2 1 3 200605
97449 IA 11 4 4 4 3 3 2 4 4 1 2 200605
97454 IA 6 2 2 2 4 4 4 3 6 1 3 200606
97455 IA 3 3 3 4 4 4 4 2 4 1 1 200606
97463 IA 12 4 3 4 3 3 3 3 3 1 3 200606
97466 IA 11 3 3 4 4 4 4 3 3 1 3 200606
97473 IA 12 4 4 4 4 4 4 4 3 1 3 200606
6

23. 9/15/2020 Week 10 Final Report
localhost:8888/nbconvert/html/Week 10 Final Report.ipynb?download=false 2/5
In [77]: # Recode the survey answers for Education
final_surveys$Q24_06 <- replace(as.character(final_surveys$Q24_06), final_surveys$Q24_06 == 1, "8th Grade or less")
final_surveys$Q24_06 <- replace(as.character(final_surveys$Q24_06), final_surveys$Q24_06 == 2, "Some High School")
final_surveys$Q24_06 <- replace(as.character(final_surveys$Q24_06), final_surveys$Q24_06 == 3, "High School Graduate/GED")
final_surveys$Q24_06 <- replace(as.character(final_surveys$Q24_06), final_surveys$Q24_06 == 4, "Some College/2-year Degree")
final_surveys$Q24_06 <- replace(as.character(final_surveys$Q24_06), final_surveys$Q24_06 == 5, "4 Year College Graduate")
final_surveys$Q24_06 <- replace(as.character(final_surveys$Q24_06), final_surveys$Q24_06 == 6, "More than 4-Year College Degree")
In [78]: # Recode the survey answers for Language
final_surveys$Q27_06 <- replace(as.character(final_surveys$Q27_06), final_surveys$Q27_06 == 1, "English")
final_surveys$Q27_06 <- replace(as.character(final_surveys$Q27_06), final_surveys$Q27_06 == 2, "Spanish")
final_surveys$Q27_06 <- replace(as.character(final_surveys$Q27_06), final_surveys$Q27_06 == 3, "Some Other")
In [79]: # Recode the survey answers for Service Line
final_surveys$PRINCIPALREASONADMISSION <- replace(as.character(final_surveys$PRINCIPALREASONADMISSION),
final_surveys$PRINCIPALREASONADMISSION == 1, "Obstetric")
final_surveys$PRINCIPALREASONADMISSION <- replace(as.character(final_surveys$PRINCIPALREASONADMISSION),
final_surveys$PRINCIPALREASONADMISSION == 2, "Medical")
final_surveys$PRINCIPALREASONADMISSION <- replace(as.character(final_surveys$PRINCIPALREASONADMISSION),
final_surveys$PRINCIPALREASONADMISSION == 3, "Surgical")
In [81]: # Recode the survey answers for Final Mode
final_surveys$FINAL_MODE <- replace(as.character(final_surveys$FINAL_MODE), final_surveys$FINAL_MODE == 1, "Mail only")
final_surveys$FINAL_MODE <- replace(as.character(final_surveys$FINAL_MODE), final_surveys$FINAL_MODE == 2, "Telephone only")
7

24. 9/15/2020 Week 10 Final Report
localhost:8888/nbconvert/html/Week 10 Final Report.ipynb?download=false 3/5
In [82]: final_surveys
8

25. 9/15/2020 Week 10 Final Report
localhost:8888/nbconvert/html/Week 10 Final Report.ipynb?download=false 4/5
FINAL_STATE AGE Q01_06 Q02_06 Q03_06 Q05_06 Q06_06 Q07_06 Q23_06 Q24_06 Q27_06 PRINCIPALREASONADMISSION PATIENTDISCHARGEDATE F
97420 IA
35-
39
4 4 4 4 4 4 Excellent
Some
College/2-year
Degree
English Obstetric 200605
97423 IA
55-
59
4 4 4 3 3 4 Excellent
High School
Graduate/GED
English Surgical 200605
97445 IA
75-
79
4 4 4 4 4 4
Very
Good
8th Grade or
less
English Surgical 200605
97446 IA
80-
84
4 4 4 4 4 4 Good
Some High
School
English Surgical 200605
97449 IA
70-
74
4 4 4 3 3 2 Fair
Some
College/2-year
Degree
English Medical 200605
97454 IA
45-
49
2 2 2 4 4 4 Good
More than 4-
Year College
Degree
English Surgical 200606
97455 IA
30-
34
3 3 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Obstetric 200606
97463 IA
75-
79
4 3 4 3 3 3 Good
High School
Graduate/GED
English Surgical 200606
97466 IA
70-
74
3 3 4 4 4 4 Good
High School
Graduate/GED
English Surgical 200606
97473 IA
75-
79
4 4 4 4 4 4 Fair
High School
Graduate/GED
English Surgical 200606
97480 IA
18-
24
4 3 3 4 4 3
Very
Good
High School
Graduate/GED
English Obstetric 200606
97482 IA
45-
49
4 4 4 4 4 3 Fair
Some
College/2-year
Degree
English Medical 200606
97505 IA
45-
49
4 3 3 3 3 3 Good
High School
Graduate/GED
English Surgical 200608
97509 IA
30-
34
4 4 4 4 4 4 Excellent
4 Year College
Graduate
English Obstetric 200608
97515 IA
60-
64
3 3 3 4 3 4 Fair
High School
Graduate/GED
English Surgical 200608
97519 IA
55-
59
4 4 4 4 4 4 Good
More than 4-
Year College
Degree
English Medical 200608
97520 IA
85-
89
3 3 3 3 3 3 Fair
High School
Graduate/GED
English Surgical 200608
97521 IA
18-
24
4 4 4 4 4 4 Good
Some High
School
English Obstetric 200608
97522 IA
45-
49
4 4 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Surgical 200609
97525 IA
18-
24
4 4 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Obstetric 200609
97533 IA
60-
64
4 4 4 4 4 4 Good
High School
Graduate/GED
English Surgical 200609
97546 IA
55-
59
3 3 3 4 4 4 Fair
Some High
School
English Medical 200609
97549 IA
35-
39
3 3 3 4 4 4
Very
Good
4 Year College
Graduate
English Surgical 200609
97565 IA
18-
24
4 4 3 4 4 3 Good
Some
College/2-year
Degree
English Medical 200607
97568 IA
35-
39
4 4 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Medical 200607
97574 IA
80-
84
4 3 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Surgical 200607
97584 IA
70-
74
3 3 4 2 2 2 Fair
Some
College/2-year
Degree
English Medical 200607
97585 IA
50-
54
4 4 4 4 3 3 Good
Some
College/2-year
Degree
English Medical 200607
97592 IA
50-
54
4 4 4 4 4 4 Fair
High School
Graduate/GED
English Surgical 200607
97596 IA
50-
54
4 4 4 4 4 4
Very
Good
4 Year College
Graduate
English Surgical 200607
... ... ... ... ... ... ... ... ... ... ... ... ... ...
129129 ND
30-
34
2 2 2 3 3 3 Good
High School
Graduate/GED
English Surgical 200606
129132 ND
60-
64
4 4 4 4 4 4 Good
More than 4-
Year College
Degree
English Surgical 200607
129134 ND
70-
74
3 3 3 3 3 3 Fair
High School
Graduate/GED
English Medical 200607
129135 ND
30-
34
3 3 4 4 4 4 Excellent
4 Year College
Graduate
English Obstetric 200607
9

26. 9/15/2020 Week 10 Final Report
localhost:8888/nbconvert/html/Week 10 Final Report.ipynb?download=false 5/5
In [89]: # Write the file
write.csv(final_surveys, file = 'final_surveys.csv')
FINAL_STATE AGE Q01_06 Q02_06 Q03_06 Q05_06 Q06_06 Q07_06 Q23_06 Q24_06 Q27_06 PRINCIPALREASONADMISSION PATIENTDISCHARGEDATE F
129139 ND
18-
24
3 3 4 2 3 4
Very
Good
Some
College/2-year
Degree
English Obstetric 200607
129147 ND
75-
79
4 4 3 4 4 3 Fair
4 Year College
Graduate
English Medical 200607
129157 ND
18-
24
4 3 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Surgical 200607
129164 ND
75-
79
3 3 3 4 3 4
Very
Good
More than 4-
Year College
Degree
English Surgical 200608
129165 ND
25-
29
3 3 3 3 3 3
Very
Good
4 Year College
Graduate
English Obstetric 200608
129166 ND
40-
44
4 4 4 4 4 4 Excellent
Some
College/2-year
Degree
English Surgical 200608
129173 ND
65-
69
3 3 3 4 4 4 Good
4 Year College
Graduate
English Surgical 200608
129176 ND
18-
24
4 3 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Obstetric 200608
129179 ND
50-
54
4 4 3 3 3 3
Very
Good
High School
Graduate/GED
English Surgical 200608
129181 ND
30-
34
3 4 4 4 4 3 Excellent
4 Year College
Graduate
English Obstetric 200608
129183 ND
45-
49
4 4 4 4 4 4
Very
Good
More than 4-
Year College
Degree
English Surgical 200608
129188 ND
65-
69
4 4 4 4 4 3 Good
High School
Graduate/GED
English Medical 200608
129190 ND
25-
29
4 4 4 3 3 3 Excellent
More than 4-
Year College
Degree
English Obstetric 200608
129199 ND
45-
49
3 3 3 4 3 2
Very
Good
Some
College/2-year
Degree
English Medical 200608
129200 ND
30-
34
3 4 4 4 3 3 Excellent
4 Year College
Graduate
English Medical 200608
129206 ND
45-
49
4 4 4 4 4 4
Very
Good
4 Year College
Graduate
English Surgical 200608
129207 ND
25-
29
3 3 4 4 4 4 Good
4 Year College
Graduate
English Obstetric 200608
129227 ND
30-
34
4 4 4 4 4 4 Excellent
More than 4-
Year College
Degree
English Surgical 200609
129241 ND
55-
59
4 4 3 3 2 3 Good
Some
College/2-year
Degree
English Surgical 200609
129246 ND
55-
59
4 4 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Surgical 200609
129251 ND
40-
44
4 4 4 4 4 4
Very
Good
Some
College/2-year
Degree
English Surgical 200609
129257 ND
35-
39
3 3 4 4 4 4
Very
Good
4 Year College
Graduate
English Obstetric 200609
129265 ND
55-
59
4 4 4 4 4 4 Excellent
Some High
School
English Surgical 200609
129269 ND
18-
24
4 4 4 4 4 4 Fair
Some
College/2-year
Degree
English Surgical 200609
150934 SD
18-
24
4 3 3 4 4 4 Excellent
More than 4-
Year College
Degree
English Obstetric 200608
150956 SD
65-
69
4 4 3 4 4 4 Fair
Some High
School
English Medical 200607
10