Our team won 1st place in analyzing CMS data and reporting factors of patient satisfaction.

1. San Francisco, California
APRIL23-25
2014
INNOVATION |TECHNOLOGY | DATA| PATIENT SATISFACTION
Meet the Team
Anchal Ahuja B.A. Business Administration at UC Berkeley
Nathan Chan Data Scientist at Twitter
Allan Dong Researcher at Wendon Nuclear Medicine
Ada Gu B.A. Public Health at UC Berkeley
Shawn Huang B.A. Integrative Biology at UC Berkeley
Philip Jeng Business Analytics Associate at ZS Associates
Andrea Kablanian Master of Public Health Candidate at Columbia University
Jessica Ken Master of Public Health Candidate at Columbia University

2. Agenda
• Executive Summary
• Study Motivation
• Methodology
• Data
• Results
• Future Directions

3. Executive Summary
We conducted an exploratory analysis to determine which hospital characteristics were
most likely to be correlated with patient satisfaction.
Good
Communication
Satisfactory
information
provided for at-
home recovery
Quiet Room
Explanation of
Medications
Help given
promptly when
requested
Room and
bathroom
cleanliness
Patient
Satisfaction

4. Motivation
• In the 1990s, during the “managed care” revolution helped slow
national healthcare expenditure; unfortunately, this did not last
• One proposed component for the backlash towards managed care
was the inability of payers to accurately assess the ability of hospitals
to provide quality care
• While healthcare costs continue to rise, so has the amount of
available hospital data, opening an opportunity for the application of
innovation through leveraging data
• Furthermore, patient satisfaction is interconnected with patient
health. A patient is more likely to recover and do so quicker if s/he is
more accepting of the quality of care being given.

5. Assumptions
1. The HCAHPS data was collected without error
2. Sample data is representative of patients across the country
3. Survey data is a good proxy for patient satisfaction
4. There is no non-response bias
5. Correlations found represent a direct relationship between
independent variables and outcome variable
6. Patients who responded to the survey considered each questions
independently and correlations within survey questions is not due to
cognitive bias

6. Methodology
1. Examine all the data individually to better understand their meaning
and how they might affect patient satisfaction
2. Combine all the data into one dataset to run a model on
3. Conduct pairwise correlation analysis between different variables and
one of the Patient Satisfaction variables
4. Train basic linear regression and/or a decision tree model to see what
structure exists in the data
5. Split data into training and test sets for models built using important
features from correlation charts and basic linear regression models
Unfortunately , we were only able to accomplish Steps 1-4 in the time allotted,
thus our conclusions are very exploratory in nature

7. Key Independent Variable Description Rf Value
Communication Patients who reported good communication from doctors,
nurses and other providers
0.48
Help Response Patients who reported that they received help as soon as
requested
0.52
Follow-up Explanations Patients who reported that staff explained about the
medicine before giving it to them
0.53
Cleanliness Patients who reported that their room and bathroom were
always clean
0.48
Quiet Night Environment Patients who reported that their room was always quiet at
night
0.41
At-Home Recovery Patients who reported that they were given information
about what to do at home during recovery
0.55
Summary of Correlations
Of all the variables analyzed, these variables represent the ones with the
greatest correlation with patient satisfaction
Primary Dependent Variable: Patients would definitely recommend the hospital

8. Several correlations were found…
Communication
Cleanliness
Explanations
Help Response At-Home Recovery
Quiet at Night

9. • We conducted a linear regression using 42 variables that we deemed
to be clean and well defined as datasets
• Linear regression was inconclusive as variables were not statistically
significant with respect to patient satisfaction variable
• We hypothesized that inconclusive results was due to collinearity
among variables and duplicate information, suggesting that further
stratification of the data is required
• A decision tree analysis offers:
• Minimal variable transformation
• An intuitive way to identify variables that address patient
satisfaction
Linear Regression
We proceeded to conduct a decision tree analysis

10. Decision Tree Analysis
More than 74.5% said that
their nurses
communicated well

11. Decision Tree Analysis
More than 74.5% said that
their nurses
communicated well
Of those hospitals, more
than 83.5% responded
that their nurses
communicated well
Of those respondents,
79.5% reported that they
had a quiet room

12. Decision Tree Analysis
An additional variable, Pain always well controlled, that correlated with patient
satisfaction was extracted from the decision tree analysis

13. • We identified six variables that are correlated with patient satisfaction
• Decision tree analysis helped us identify an additional factor impacting
patient satisfaction (pain control) and confirmed the correlation of our
originally identified factors
• Surprisingly, “objective” measures of health outcomes are not as
strongly correlated with patient satisfaction as the variables we
reported
• The human aspect of patient care is critical
Results and Conclusions
Good Communication
Satisfactory
information provided
for at-home recovery
Quiet Room
Explanation of
Medications
Help given promptly
when requested
Room and bathroom
cleanliness

14. • Assess impact of potential selection bias
• Examine external factors that may differentiate the providers such as:
prevalence of managed care, inherent health of population, public policy,
socioeconomic levels, etc
• Investigate if patient surveys, alone, are the best measure of provider
performance
• Research past efforts of evaluating hospital effectiveness of promoting
patient satisfaction
• Explore the strength of the relationship between patient satisfaction and
health outcomes
Future Directions

15. THANK YOU
APRIL23-25
2014