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Patient Satisfaction Score Analysis
Patient satisfaction scores drive quality ratings and reimbursement decisions. This project analysed HCAHPS survey data to identify the strongest drivers of overall satisfaction.
3 key satisfaction drivers identified
2025PythonPandasScikit-learnPower BI
Dataset
CMS: HCAHPS Patient Experience
Key Questions
- Which survey dimensions most strongly correlate with overall hospital rating?
- Are there regional or hospital-size patterns in satisfaction scores?
- Can we predict a hospital's overall rating from its individual dimension scores?
Methods
- Correlation and regression analysis across HCAHPS dimensions
- Principal component analysis to reduce survey dimensionality
- Random forest feature importance for rating prediction
- Benchmarking analysis against national and regional averages
Results
The three strongest predictors of overall satisfaction were: nurse communication (r=0.82), responsiveness of hospital staff (r=0.78), and care transition communication (r=0.75). Smaller hospitals (<100 beds) scored significantly higher on communication dimensions.
Satisfaction Score Correlation with Overall Rating
Recommendations
- Prioritise nurse communication training as the highest-impact quality improvement initiative
- Implement structured handoff protocols to improve care transition communication
- Set up real-time patient feedback mechanisms rather than relying solely on post-discharge surveys
- Benchmark against peer hospitals of similar size for more meaningful comparisons
Limitations
HCAHPS data represents respondents only (typically 25-30% response rate), which may introduce selection bias. Scores reflect patient perception, which may not always align with clinical quality measures.