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SHIF

July 11, 2025 by
Benedict Ouma
Title: Evaluating SHIF/SHA Health System Impact with SQL and Multi-Tool Dashboards
Tools Used: SQL, Power BI, Tableau, Excel
Focus: Healthcare System Performance Analytics
This project tackled the complex task of evaluating the effectiveness of Kenya’s Social Health Insurance Fund (SHIF) and Social Health Authority (SHA) using synthetic data. I applied advanced SQL queries to analyze member demographics, contributions, claims processing, legal risks, and overall satisfaction.
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Objectives & Tasks:
  1. Enrollment Patterns: Distribution by income, subsidy status, and region
  2. Contributions & Penalties: Total monthly inflows, top contributors, high-penalty members
  3. Service Utilization: Most accessed services, out-of-pocket costs, underinsured populations
  4. Provider Analytics: High-volume providers, approval rates, new accreditation analysis
  5. Claims Review: Breakdown by claim status, fraud/discrepancy flags
  6. Satisfaction & Trust: Sentiment analysis across regions
  7. Legal Cases: Average case durations and impact scores
Key Insights:
  • Certain regions had more subsidized members, hinting at equity or access issues
  • Out-of-pocket costs remained high for essential services in rural zones
  • A cluster of high-penalty, low-satisfaction members was identified as high-risk
  • Claim rejections were higher among newly accredited providers
  • Sentiment scores correlated strongly with regional trust in SHIF/SHA systems
Visual Tools:
  • Power BI: Claims approval vs rejections, provider heatmaps
  • Tableau: Service usage and satisfaction dashboards
  • Excel: Summary charts for contributions, demographic breakdowns
Use Cases:
  • For policy evaluators to assess regional performance
  • For SHIF management to address service delivery gaps
  • For researchers exploring health financing systems
Note: All findings are based on a synthetic dataset and do not reflect real SHIF/SHA data.
Dashboards available: Power BI.