Healthcare Data Warehouse: Overview
ScienceSoft has been providing a full range of data warehousing services since 2005.
A data source layer
– healthcare data from internal and external data sources (ERP, EHR/EMR, CRM, claims management system, pharmacy management systems, etc.).
A staging area
– intermediate temporary storage, where healthcare data undergoes the extract, transform and load (ETL) or the extract, load and transform (ELT) process.
Data storage layer
– includes centralized structured storage. It may also have data marts – healthcare DWH subsets oriented to a specific business line (HR, accounting, etc.) or department (radiology, intensive care, pediatrics, etc.).
Analytics and BI
– business analytics, data mining, data reporting and visualization tools.
Data integration
- Ingesting structured, semi-structured, unstructured healthcare data (from EHR systems, ERP, HR management systems, public medical databases, claims management systems, etc.).
- ETL/ELT-based healthcare data integration.
- Full and incremental healthcare data extraction/load.
- Controlled healthcare data loading/management.
- Healthcare data transformation of varying complexity (data type conversion, summarization, etc.).
- Healthcare data loading and querying using SQL.
- Big data ingestion.
- Streaming data ingestion.
Data storage
- Integrated, historical, summarized, subject-oriented healthcare data storage.
- Protected Health Information (PHI) storage.
- Metadata storage.
- Options of healthcare data storage environments (cloud, on-premises, hybrid).
Performance
- Healthcare data indexing.
- Materialized view support.
- Result-caching.
- Elastic automated scaling of storage and compute resources.
- High performance query processing.
- ML capabilities to dynamically manage performance and concurrency.
Security and compliance
- Granular row and column level security control.
- Multi-factor authentication.
- Healthcare data encryption at rest and in transit (including backups and network connections).
- Dynamic healthcare data masking.
- Ongoing threat detection and vulnerability assessment.
- Compliance with healthcare regulations (HIPAA, FDA, HITECH, etc.).
A data lake
While DWHs store highly structured healthcare data ready for analysis, data lakes serve as a cost-effective repository of semi-structured and unstructured healthcare data (clinical data, physicians’ notes, etc.). Data stored in the data lake can be further used to develop ML models (for example, predicting hospital demand).
Enabling healthcare business users to be flexible and self-reliant in analyzing, visualizing and reporting healthcare data structured in the DWH, which results in the easy transfer of analytics insights to the decision-makers and a shorter time-to-insight.
What determines medical data warehousing success
Proof of Concept
Validate your healthcare DWH solution with a PoC to better understand its real potential and get real-life user feedback.
Healthcare DWH scalability and flexibility
To instantly upload any type (structured, semi-structured, unstructured) and amount of healthcare-related data to efficiently address new data analytics objectives.
Focus on security and healthcare data protection measures
Store and process sensitive patient data within highly secure environments (AWS, Microsoft Azure, Google Cloud or private servers), ensure all-time data encryption and dynamic data masking, restrictive data access, multi-factor user authentication, healthcare DWH vulnerability assessment and penetration testing, etc.
Key financial outcomes of a healthcare DWH
As a central element of a BI solution, healthcare DWH enables consolidation of disparate healthcare data sources into a structured healthcare data repository ready for analysis to improve business and clinical decision-making and to:
- Improve health outcomes.
- Improve healthcare resource management.
- Decrease healthcare operating costs.
- Accelerate data-driven labor management.
- Personalize care delivery and improve patients’ experience.
Amazon Redshift
Best for: big data warehousing
Features
- Integration of all healthcare data types (structured, semi-structured, unstructured) for storing and SQL-querying.
- Integrations with the AWS ecosystem (including S3, AWS Glue, Amazon EMR) and third-party tools (Power BI, Tableau, Informatica, Qlik, Talend Cloud).
- Federated queries support.
- ML capabilities for optimized performance under varying workloads.
- Separate scaling of compute and storage.
- Healthcare data encryption and fine-grained access control.
- HIPAA-compliant.
Pricing
- On-demand pricing – $0.25 – $13.04/hour.
- Reserved instance pricing offers saving up to 75% over the on-demand option (a 3-year term).
- Data storage (RA3 node types): $0.024/GB/month.
Azure Synapse Analytics
Best for: advanced data analysis
Features
- SQL-querying of structured, semi-structured, unstructured healthcare data.
- Native integrations with a data lake, operational databases, BI and ML software.
- Integration with third-party BI tools, including Tableau, SAS, Qlik, etc.
- Separate billing for compute and storage.
- Healthcare data encryption, dynamic healthcare data masking, column- and row-level security.
- HIPAA-compliant.
Pricing
- Compute on-demand pricing – $1.20–$360/hour.
- Compute reserved instance pricing allows saving up to 65% over the on-demand option (a 3-year term).
- Data storage: $122.88/TB/month.
Oracle Autonomous Data Warehouse
Best for: hybrid healthcare DWHs
Features
- Querying across multiple healthcare data types (structured, semi-structured, unstructured).
- Built-in connectivity to Oracle Cloud Infrastructure Object Storage, Azure Blob Storage, Amazon S3.
- Integration with Oracle Analytics Desktop and third-party BI tools (Microsoft Power BI, Tableau, MicroStrategy, Qlik, etc.).
- Healthcare data encryption, privileged user and multifactor access control.
- Independent scaling of storage and compute.
- HIPAA-compliant.
Pricing
- Compute costs: $1.3441/CPU/hour
- Data storage: $118.40/TB/mo (in the public cloud).
Healthcare DWH implementation with ScienceSoft
Since 2005, ScienceSoft has offered IT solutions to healthcare organizations and provided a full range of data warehousing services to help them build robust healthcare DWHs and support the decision-makers with high-quality healthcare data.
Healthcare DWH consulting
- Eliciting requirements for a future healthcare DWH solution.
- Designing a healthcare DWH implementation/migration strategy.
- Outlining the optimal healthcare DWH vendors, technology stack and its configurations.
- Advising on healthcare data integration and data quality procedures.
- Conducting admin training
Healthcare DWH implementation
- Healthcare data storage needs analysis and DWH solution architecture design.
- Healthcare data sources (ERP, EHR/EMR, CRM, claims management system, pharmacy management systems, etc.) integration into a healthcare DWH.
- DWH platform integration into the data environment (a data lake, big data platform, BI tools, etc.).
- Set up data management and metadata management procedures.
- Conduct healthcare data cleaning and data migration.
- User training.
- DWH support and evolution (if required).
About ScienceSoft
ScienceSoft is a global IT consulting and IT service vendor headquartered in McKinney, TX, US. Since 2005, we have provided a full range of data warehousing services to help healthcare organizations build from scratch or enhance their existing data warehouse platforms within the set timeframes and with minimal investments.