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Readmission Analytics Dashboard

Interactive visualization of hospital readmission data and model insights

Total Patients
71,518
↑ 2.1% from previous period
Readmission Rate
18.2%
↑ 0.8% from previous period
Avg. Length of Stay
4.3 days
↓ 0.5 days from previous period
Readmission Trends Over Time
30-day readmission rates by month
Top Risk Factors
Factors most predictive of readmission
Diagnosis Distribution
Primary diagnoses with highest readmission rates
Ethical Considerations
Implications of false positives and negatives in healthcare

False Positives

When a patient is incorrectly predicted to be readmitted, this may lead to:

  • Unnecessary interventions and follow-ups
  • Increased healthcare costs
  • Resource allocation inefficiencies
  • Patient anxiety and inconvenience

False Negatives

When a high-risk patient is incorrectly predicted as low-risk, this may lead to:

  • Missed opportunities for preventive interventions
  • Potentially worse patient outcomes
  • Higher emergency readmission costs
  • Decreased trust in predictive systems

Our Approach

We've carefully balanced our model to minimize both false positives and negatives, with a slight preference toward minimizing false negatives due to the higher potential harm. Additionally, we emphasize that this tool should support, not replace, clinical judgment.