📑 Portfolio Case Study: Hospital Readmission Drivers & Root Cause Analysis

1. Project Overview

Title: Hospital Readmission Drivers & Root Cause Analysis

Objective: Identify, monitor and optimize drivers of readmission across hospital, community and daycare services. Developed 20 KPIs and 3 thematic dashboards to support leadership decisions. The analysis confirmed a 10% readmission rate.

2. Data Cleaning & Preparation

Sources: Hospital Support, Community Services, Daycare records.

• Removed duplicated using Hospital Number + Admission Date.
• Recalculated Age, Age band, Length of care (LOC) and LOC Group.
• Standardized dates, identifiers and categorical fields.
• Feature DAX engineering: 30‑Day Readmission Flag.
• Appended datasets into a single analytical model to enable cross‑sector analysis.

3. Data Quality Improvements

• Introduced validation rules and mandatory fields for high‑impact clinical attributes.
• Recommended monthly audits and department data stewards.
• Prepared the dataset for equity reporting and pathway optimization.

4. Analytical Approach

Conducted sectoral and cross‑sector analysis to identify drivers and preventative opportunities.

• Hospital: Readmission (overall & 30‑day), LOS, diagnosis drivers, discharge patterns.
• Community: Demographics, SES, vulnerable population profiling.
• Daycare: Post‑discharge services, follow‑up effectiveness, contact type impact.
• Cross‑sector: Combined effects of diagnosis, demographics and continuity of care.

5. KPI Suite (Selected)

6. Dashboard Design

• Patient Demographics & Equity: Bar charts, maps, stacked visuals.
• Trend and performance & Continuity: trends by year, month, sector and professional type charts.
• Clinical Outcomes & Pathways: KPI cards, histograms, diagnosis drilldowns.

7. Recommendations

With the benchmark achieved (@10% below the 13.9% standard), the strategy focuses on sustaining performance, targeted micro‑improvements and predictive prevention.

1. Maintain Strong Discharge Practices: Quarterly reviews, standardized checklists, ensure scheduled follow‑ups.
2. Target High‑Risk Diagnoses: Identify top 3 high‑risk conditions and pilot small condition‑specific bundles.
3. Early Follow‑Up for At‑Risk Groups: 48–72‑hour calls, telehealth, community nurse monitoring, automated reminders.
4. Predictive Insights: Simple risk‑scoring and dashboard alerts integrated into discharge planning.
5. Address Equity Variations: Improve demographic capture, design targeted community programs, monitor gaps.
6. Optimize Data Capture & Monitoring: Automated validation, weekly KPI refreshes, ward‑level tracking.
7. Sustain Team Accountability: Governance integration, highlight and share best practices.
8. Pilot Low‑Cost Micro‑Interventions: SMS reminders, brief virtual check‑ins, digital care plans.

8. Insights & Impact

• Validated hospital performance at 10%.
• Identified focused opportunities for incremental improvement.
• Demonstrated daycare/post‑discharge protective effects.
• Delivered a clear roadmap for proactive, equity‑aware optimization.

9. Conclusion

This project demonstrates proficiency in data Analysis, DAX modeling, KPI development and Power BI dashboards — delivering an actionable, equitable, and sustainable strategy to manage and improve hospital readmissions.