Re-Engineering a Legacy system to Cut Costs by $500K and Improving Processing Time
The customer, a subsidiary of a leading U.S.-based health insurance company, is an analytics solutions and services provider that helps healthcare stakeholders enhance clinical outcomes and reduce costs.
The Need
The customer’s population health platform helped health plans, hospitals, ACOs, providers and employers. It aggregated data from various stakeholders, transformed it, and applied healthcare-specific analysis.
The analysis enabled timely delivery of insights to tools used by various stakeholders like care teams, payers, and employers for better outcomes, reduced costs, and optimized care.
Built on top of this analytics engine were three purpose-built solutions, each addressing a specific facet of healthcare delivery:
The Challenge
The solution relied on a legacy SAS-based data warehousing system which presented operational and architectural challenges as outlined below:
- Expensive Licensing: SAS licensing alone costs $500,000 annually, straining profitability.
- Slow Data Refresh Cycles: Monthly data loads took 24 days, making insights obsolete by the time they were delivered.
- No Incremental Loads: The system only supported full data loads, limiting flexibility and agility and delays in processing when there are delta changes
- Lack of Data Quality Processes: No automated mechanisms existed to detect, correct, or reprocess errors.
- Heavy Manual Intervention: Absence of automation led to inefficiencies and prolonged testing cycles.
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