Hospital Readmission Analysis - Statistical Significance
MY ROLES
TIMELINE
2020
TOOLS USED
Statistical Analysis, Healthcare Analytics, Hypothesis Testing, Python, Pandas, Data Visualization, Policy Analysis, Decision Science, Confidence Intervals, P-value Interpretation
DESCRIPTION
This project critically analyzed hospital readmission data to evaluate both statistical and practical significance in the context of small vs large hospitals. The analysis examined whether statistically significant differences in readmission rates translated to meaningful practical implications for healthcare policy. By applying rigorous statistical methods and considering operational contexts, the project helped healthcare administrators make evidence-based decisions that balance statistical findings with real-world implementation considerations.
OUTCOMES
- Identified statistically significant difference in readmission ratios between small and large hospitals (p < 0.01)
- Quantified the practical difference (0.044) in excess readmission ratios and its implications
- Developed framework for evaluating when statistical significance warrants policy changes
FEATURES
Rigorous hypothesis testing, confidence interval analysis, practical significance thresholds, cost-benefit analysis methodology, policy recommendation framework
CHALLENGES
Distinguishing between statistical significance and practical implications. Translating p-values and confidence intervals into actionable insights. Helping stakeholders understand when statistical findings warrant operational changes.
APPROACH
Applied hypothesis testing to determine statistical significance (p < 0.01). Calculated confidence intervals for both small hospitals [0.93, 1.13] and large hospitals [0.72, 1.19]. Analyzed the proportion of hospitals exceeding benchmark ratios (59% small vs 44% large). Developed a balanced assessment framework considering both statistical findings and implementation costs to guide policy recommendations.