Predicting Patient Satisfaction Scores

**Overview**
Patient satisfaction has evolved from a quality metric to a critical business imperative directly tied to organizational success. Traditional approaches to satisfaction management are reactive, addressing issues only after poor experiences have already occurred and patients have potentially been lost. Predictive analytics transforms this paradigm by identifying at-risk encounters before dissatisfaction materializes, enabling healthcare organizations to intervene proactively and prevent negative outcomes.

**Objective**: This demonstration analysis develops predictive models to identify patients at risk for poor satisfaction scores before their care episode concludes, enabling real-time interventions to improve experiences. By leveraging machine learning techniques on historical satisfaction data, clinical indicators, and operational metrics, we create actionable intelligence that transforms reactive satisfaction management into proactive experience optimization.

**Key Focus Areas**
- Development of risk stratification models using satisfaction survey responses and clinical data
- Predictive modeling to identify at-risk encounters
- Integration of operational variables including wait times, staffing levels, and service utilization

For more details, refer to the embedded notebook.

**Dataset Overview**

- 1,000+ patient responses across 4 major service lines (Emergency Department, Surgery, Inpatient, ICU)
- 2+ years of longitudinal data enabling robust trend analysis

**Key Data Elements**
- Weekly aggregated metrics with department-level segmentation
- Core HCAHPS measures including communication, cleanliness, pain management, discharge coordination

**Key Performance Indicators Tracked:**
- Overall Hospital Rating (0-10 scale)
- Likelihood to Recommend (0-10 scale)
- Communication Quality Scores
- Environmental Cleanliness Ratings
- Pain Management Effectiveness
- Discharge Information Quality

*This product uses synthetic data and simplified models for illustration purposes. Results shown are not indicative of actual performance. Health Catalyst's production platform includes comprehensive data security, HIPAA compliance, clinical validation, and continuous monitoring that cannot be fully represented in this demonstration environment.*