DataToBiz Case Study: Centralized Data Warehousing Setup & Power BI Boosts Operational Efficiency in Healthcare

Our Client

• A large healthcare provider with over 5,100 employees across various hospital chains and clinics nationwide.
• They offer a wide range of services, including patient care, diagnostics, outpatient care, accident wards, and other specialized treatments.

Problem Statement

The organization managed stacks of patient data, medical records, and billing information, which led to data mismanagement and missed opportunities. The scattered data across multiple systems made it difficult to streamline workflows and ensure compliance.

• Fragmented Data Pipelines: Data was spread across multiple platforms like EHRs, financial systems, and diagnostic reports, making it difficult to consolidate and analyze for clinical reporting.
• Delayed Reports: The absence of an integrated data structure caused reporting delays, slowing down operational workflows and decision-making processes critical to patient care.
• Compliance Lag: The inability to consolidate healthcare data effectively complicated HIPAA compliance, posing risks to patient data privacy and secure data sharing.
• Manual Data Stacking: Data integration relied heavily on manual processes, which were time-consuming and prone to errors, leading to a lack of actionable insights that negatively impacted patient care.
• Limited KPI Monitoring: The organization lacked a unified system to monitor key performance indicators (KPIs) such as patient outcomes, financial health, and resource utilization, limiting their ability to optimize resource allocation.

Our Solution

After analyzing the challenges discussed in the table, our experts recommended building a centralized data platform that connects various healthcare systems. This solution provided advanced analytics, helping clinicians and administrators to securely handle data.

• Centralized Data Stage: Our developers created a cloud-based data warehouse on Microsoft Azure to consolidate data from multiple sources like EHRs (Epic, Cerner), financial databases, billing systems, and lab diagnostics into one unified platform.
• ETL Automation: Using Microsoft Azure Data Factory, we automated the process of extracting, transforming, and loading (ETL) data to ensure consistent and accurate data ingestion from all systems.
• Real-Time Analysis: We deployed machine learning(ML) and analytics tools to extract insights from both historical and real-time data, helping clinicians and administrators to go better with decisions.
• Power BI Dashboard Integration: We developed a custom, interactive Power BI dashboards to visualize real-time updates on patient outcomes, financial performance, and hospital resource usage, allowing stakeholders to monitor KPIs instantly.
• Data Security and Governance: To meet HIPAA requirements, we implemented encryption (AES-256) and role-based access control (RBAC), ensuring that only authorized personnel could access sensitive information. Automated logging and audit trails were also added for compliance.

Technical Architecture

We designed a simplified yet robust technology architecture to streamline data processing and ensure scalability later on. Here’s how we implemented it:

• Data Collection: Data was gathered from various sources, including Excel sheets, EHR systems, financial databases, and SQL, ensuring all critical operational data was included for comprehensive analysis.
• Data Integration: Leveraging Azure Data Factory, we standardized and integrated data from various healthcare systems to maintain consistency across all formats.
• Power BI Reporting: Customized Power BI dashboards and dynamic reports were created to track patient outcomes, financial health, and performance, supporting scalability as the organization’s data needs evolved.

Business Impact

• Automating data aggregation and reporting cut the time required to generate key reports from 6 hours to 2 hours, helping departments access crucial insights faster, improving operational efficiency and patient flow.
• The new system ensured full compliance with HIPAA and other healthcare regulations, reducing the risk of non-compliance penalties and avoiding potential fines up to $90,000 annually.
• Operational costs were reduced by approximately $250,000 per year due to the automation of manual data handling and reporting tasks, freeing up resources for reinvestment in patient care and operational improvements.
• The solution scaled effectively, allowing the healthcare network to handle 30% more data and integrate new data sources, supporting its expansion across multiple locations.

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Originally published on DataToBiz