Strategy and Solutions
The client is a multi-hospital health system managing a diverse patient population across acute, ambulatory, and post-acute care settings. Fragmented data across claims, clinical systems, pharmacy records, and social determinants limited their ability to run effective population health programs. To address these challenges and enable advanced analytics, the organization partnered with Zymr.
The health system operated with siloed data sources, including claims, EHRs, pharmacy systems, and external datasets. This fragmentation made it difficult to gain a unified view of patient populations and limited the effectiveness of care gap identification and risk stratification initiatives.
Without a centralized data platform, analytics teams struggled to generate reliable insights for readmission prediction and population health management. Data inconsistencies and latency further impacted decision-making and delayed interventions for high-risk patients.
The absence of a scalable data architecture also made it challenging to support value-based care reporting requirements. Quality metrics, care outcomes, and contract reporting required significant manual effort, increasing operational overhead and reducing efficiency.
The organization needed a modern lakehouse platform capable of integrating diverse data sources, enabling real-time analytics, and supporting machine learning use cases for population health.
Zymr designed and implemented a population health lakehouse that unified enterprise data and enabled advanced analytics and ML-driven insights. This transformation improved care outcomes, operational efficiency, and reporting capabilities.
Zymr built a scalable lakehouse architecture designed to support population health analytics, machine learning, and regulatory reporting.
.svg)
.svg)
.svg)
.svg)
.svg)
.svg)