Our Work
Real challenges. Data-driven solutions. Measurable results.
The Challenge
The School District of Philadelphia faced multi-year enrollment declines, uneven academic performance, and widening equity gaps across its 218 district schools. Existing systems produced fragmented reports, making it difficult to track attendance patterns, staffing impacts, demographic shifts, or predict which schools were most at risk. The district needed a unified analytics approach that could reveal root causes and guide timely interventions.
Our Solution
IPC Global helped the district integrate enrollment, demographic, attendance, staffing, climate, and performance data into a single analytics framework, powered by Qlik on AWS. Predictive models surfaced the strongest contributors to underperformance, including school climate, teacher experience, and early-year absenteeism. Leadership dashboards were built to flag emerging risks, compare progress across schools, and identify where targeted supports, such as multilingual outreach, attendance initiatives, or staffing reinforcements, would have the greatest impact. For the first time, district teams could move from reactive reporting to proactive, evidence-based action.
The Results
+3.4pts
Graduation Rate
-1,400
Fewer Dropouts
+1,841
Enrollment Rebound
How Philadelphia Used Data to Reverse Declines and Improve Student Outcomes
The Challenge
A multi-hospital health system serving over 500,000 patients annually was struggling with rising operational costs, inefficient resource allocation, and difficulty predicting patient readmission risks. Emergency departments experienced overcrowding, while some specialty units operated below capacity. Clinical teams lacked real-time visibility into patient flow, bed availability, and staffing needs. The organization needed a comprehensive analytics solution to optimize operations, reduce unnecessary readmissions, and improve quality of care while controlling costs.
Our Solution
IPC Global implemented an integrated healthcare analytics platform using Qlik on AWS, consolidating data from EHR systems, billing, scheduling, and clinical operations. The solution included predictive models for patient readmission risk, real-time dashboards for capacity management, and analytics to identify patterns in care delivery. Machine learning algorithms helped identify high-risk patients for proactive intervention, while operational dashboards enabled dynamic staffing adjustments and resource optimization across the health system's network of facilities.
The Results
–$4.2M
Cost Reduction
–28%
ER Wait Time
–15%
Readmission Rate
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Healthcare
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