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The client is a physician-led clinically integrated network committee to offer patients the best experience in care.
The challenges the client faced with an overwhelming number of requests and high data analytics expenditures highlight the need for a more efficient and scalable solution. Here’s how these issues can be addressed effectively:
Key Challenges:
Overwhelmed Systems: The influx of requests from a vast network of healthcare professionals likely strained the existing infrastructure, leading to performance bottlenecks and delays.
High Data Analytics Costs: Excessive expenditures in data analytics suggest that the current tools or processes were not optimized for efficiency, leading to inflated costs.
Inefficient Use of Resources: The informatics team spending 80% of their time on data aggregation and normalization indicates that valuable personnel resources are being consumed by repetitive tasks instead of focusing on more strategic initiatives.
The collaboration between SNP Technologies and the client to enhance their data analytics capabilities resulted in significant improvements across various dimensions. Here’s a detailed overview of the outcomes and benefits of the implemented solution:
Key Outcomes and Benefits:
- Azure SQL Data Warehouse (now Azure Synapse Analytics): This platform provides a scalable solution for handling large datasets, enabling rapid data ingestion from diverse sources. This flexibility allows the client to respond swiftly to new data requests.
- Scalable Architecture: The use of Azure PaaS technologies means the system can automatically scale based on demand, accommodating fluctuations in data volume without performance degradation.
- Empowering Users: With self-service analytics capabilities, staff can access and analyze data independently, reducing reliance on the informatics team and enabling faster decision-making.
- Real-Time Analytics: Implementing real-time data processing allows the client to gain insights instantly, facilitating timely responses to emerging trends or issues.
- Customized Algorithms: By integrating predictive analytics, the client can forecast trends and outcomes, enabling proactive decision-making that enhances operational efficiency.
- Estimated Savings: The implementation led to an estimated savings of $100,000 per year for the first three years, demonstrating a strong return on investment.
- Reduction in Data Aggregation Time: Staff now spends only 10% of their time on data aggregation and normalization, allowing them to dedicate 90% of their time to valuable data analysis.
- Historical Data Optimization: Reducing historical data storage by 25% not only streamlines data management but also cuts down costs associated with data storage and processing.
- Operational Reports: The ability to generate detailed operational reports based on real-time and predictive analytics empowers the client to make informed, data-driven decisions that enhance business outcomes.
