San Francisco, California

AP

RIL

23

-25

20

14

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

Agenda

• Executive Summary

• Study Motivation

• Methodology

• Data

• Results

• Future Directions

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

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.

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

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

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

Several correlations were found…

Communication

Cleanliness

Explanations

Help Response At-Home Recovery

Quiet at Night

• 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

Decision Tree Analysis

More than 74.5% said that

their nurses

communicated well

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

Decision Tree Analysis

An additional variable, Pain always well controlled, that correlated with patient

satisfaction was extracted from the decision tree analysis

• 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

• 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

THANK YOU

AP

RIL

23

-25

20

14