Performance analytics for medical certifications
Social Security Institute strengthens oversight, monitoring, and Electronic Health Record adoption
INDUSTRY
Government
Headcount
+3,000
employees
Location
Montevideo,
Uruguay
Challenge
Uruguay’s Social Security Institute needed a comprehensive view of medical certification management and the level of adoption of the National Electronic Health Record (HCEN) among healthcare providers.
Information was fragmented and did not allow clear evaluation of performance, system usage, or improvement opportunities. Operational data needed to be transformed into reliable indicators to support informed decision-making.
Solution
We designed and implemented a business analytics and data governance solution that transforms operational medical certification data into reliable management indicators.
The tool enables evaluation of HCEN adoption, analysis of provider performance, and the creation of comparable metrics to support auditing and executive decision-making.
The solution establishes a consistent measurement framework that enables continuous monitoring, transparency, and sustained improvement over time.
2
Annual medical certifications analyzed
1.4
Individuals certified in one year
360
Comprehensive view of each certification
Benefits
The value behind the solution
Centralized monitoring and auditing
Comprehensive management of medical certifications based on consolidated data.
Comparative analysis
Benchmarking to identify gaps and improvement opportunities.
Agile access to key information
Detailed and summarized data to optimize decisions.
Our methodology
The project was approached through a structured and collaborative framework, integrating business and technology teams from BPS together with Quanam specialists. From the outset, strategic objectives, prioritization criteria, and effectiveness metrics were aligned to ensure measurable impact on management performance.
An integrated data architecture was designed to consolidate multiple internal and external sources into a unified repository, ensuring consistency, traceability, and scalability. On this foundation, analytical and predictive models were developed to identify behavioral patterns and prioritize cases with the highest probability of irregularities.
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