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Data Warehousing Solution for a Small and Medium Enterprise (SME): Case Study


Efficient and Scalable Data Warehousing Solutions for Small and Medium Enterprises (SMEs)

Summary

A growing SME healthcare provider faced challenges managing fragmented data, ensuring compliance, and generating actionable insights. Inginit implemented a robust, secure, and scalable on-premise data warehousing solution, enabling seamless integration of systems, automated workflows, and real-time reporting while adhering to stringent compliance standards like HIPAA and GDPR.


About the Client

• Industry: Healthcare

• Location: Saudi Arabia

• Organization Size: 3 hospitals, employing 150 staff across administrative, clinical, and diagnostic services

• Primary Focus: Unifying patient records, improving reporting efficiency, and maintaining compliance with SeHE and HIPAA regulations

 

Client Requirements and Challenges

The SME approached us with a need for a centralized, secure data warehouse to overcome several pain points:

Client Challenges

  1. Fragmented Data Across Systems:

    The client operated multiple systems, including EHR, billing platforms, and diagnostic tools, which lacked interoperability. With three facilities operating independently, patient data was fragmented across EHRs, billing systems, and diagnostic platforms, leading to inefficiencies and errors.

  2. Compliance Concerns:

    Adhering to SeHE’s interoperability requirements and HIPAA’s stringent data protection standards was proving to be a complex, resource-intensive task.

  3. Reporting Delays:

    Generating compliance reports and operational insights required significant manual effort, often taking days to complete.

  4. Limited Scalability:

    The existing setup could not handle the growing volume of patient data or support new integrations.

  5. Data Quality Issues:

    Inconsistent formats and incomplete records led to inaccuracies, impacting decision-making and compliance audits.

 

 

Solutions overview

Inginit designed and implemented a bespoke data warehousing solution, addressing the client’s specific needs while ensuring adherence to SeHE and HIPAA compliance. The approach combined secure technology, automated processes, and user-friendly interfaces to deliver a seamless, centralized platform.


1. Centralized Data Warehouse

We deployed PostgreSQL, an open-source relational database, as the foundation of the data warehouse.

  • Designed to meet SeHE and HIPAA requirements for secure data storage and processing.

  • Integrated seamlessly with existing EHR, billing, and diagnostic systems, unifying all operational data under one roof.


2. Automated ETL Workflows

Apache NiFi was used to create efficient ETL (Extract, Transform, Load) pipelines, ensuring accurate and timely data integration.

  • Data from multiple sources (EHRs, CSV files, JSON APIs) was cleaned, standardized, and loaded into the warehouse.

  • Compliant with FHIR and HL7 standards for interoperability, facilitating seamless communication between systems.

  • Outcome: Eliminated manual data entry errors and reduced data ingestion time by 70%.


3. Reporting and Visualization

For real-time analytics, we implemented Apache Superset, an open-source reporting tool that integrates directly with PostgreSQL.

Features:

  • Highly customizable dashboards showcasing patient trends, financial performance, and operational metrics.

  • Advanced visualization capabilities, including interactive charts, geospatial maps, and drill-down features for detailed insights.

  • SQL Lab for creating and testing complex queries, catering to technical users while maintaining flexibility.


Why Superset?

  • Fully on-premise: Aligns with client preferences to avoid cloud-based solutions while ensuring data security and regulatory compliance.

  • Scalable and robust: Built to handle large datasets and high query volumes without compromising performance.

  • User-friendly for non-technical staff: Intuitive interface simplifies navigation and enables easy creation of visualizations for non-technical users after basic training.

 

4. Compliance and Security

Encryption:

AES-256 encryption was implemented for data at rest, and TLS 1.3 secured data in transit.

Role-Based Access Control (RBAC):

• User roles were defined to restrict access. For example:

• Clinicians accessed medical records but not billing data.

• Admin staff viewed financial records without access to patient history.

Audit Trails:

Actions like data modifications and access attempts were logged using the ELK Stack (Elasticsearch, Logstash, Kibana) for full visibility and compliance reporting.

Consent Management:

Digital consent forms were integrated into the warehouse to ensure compliance with GDPR and other data protection regulations.


5. Scalability Optimization

To future-proof the system, we optimized scalability by:

  • Partitioning Large Tables: Split data by date to speed up queries.

  • Dynamic Hardware Allocation: Configured SSD-based servers to handle simultaneous data queries without performance lag.

 


Key Outcomes

1. Faster Reporting:

Compliance reporting time was reduced by 60%, enabling the client to meet regulatory deadlines with ease.

2. Improved Data Security:

The solution fully met SeHE and HIPAA standards, ensuring patient data was secure and auditable.

3. Operational Efficiency:

Real-time dashboards helped leadership make quicker decisions, such as allocating resources during peak hours.

4. Cost Savings:

Eliminated the need for third-party reporting tools, saving 20% in annual IT costs.



Technology Stack

  • Data Warehouse: PostgreSQL

  • ETL Tool: Apache NiFi

  • Microsoft Presidio for data anonymization

  • Integration Standards: HL7 and FHIR for interoperability

  • Security Tools: AES-256 encryption, TLS 1.3, RBAC

  • Reporting Tool: Superset (open-source, on-premise)

  • Audit Trail: ELK Stack (Elasticsearch, Logstash, Kibana)

 


Implementation Timeline

  1. Weeks 1–3: Discovery and Planning

    Conducted stakeholder workshops to gather requirements and map existing workflows.

  2. Weeks 4–6: System Design and Setup:

    Designed the data warehouse architecture and finalized the technology stack.

  3. Weeks 7–10: ETL Development and Data Migration:

    Built ETL workflows, migrated historical data, and conducted data quality checks.

  4.  Weeks 11–12: Integration Testing:

    Deployed the solution and integrated it with existing systems.

  5. Weeks 13–15: Reporting and Dashboard Customization

    Develop real-time dashboards for key stakeholders, Customize views for different departments (e.g., finance, operations, clinical), Ensure non-technical staff can navigate and use the dashboards easily.

  6. Weeks 16–17: Training and Deployment

    Delivered training sessions for staff on using dashboards and generating compliance reports.


Client Feedback: "Inginit’s solution gave us the confidence we needed to scale our operations securely. What used to take days now happens in real time, and our staff finds the system intuitive and reliable.” — CIO, SME Healthcare Provider

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