A Beginner’s Guide to Leveraging a Healthcare Data Warehouse

Medical organizations are turning to massive volumes of healthcare data for addressing issues associated with care quality, healthcare costs, patient satisfaction, and operational inefficiencies. This need for healthcare analytics is driving providers to deploy healthcare data warehouses of their own. Eventually, organizations utilize data through data warehousing technologies to improve clinical outcomes, patient experiences, and administrative functions.

Given the complicated nature of data warehousing processes and large volumes of disparate healthcare data sources, data warehouse development becomes a challenge in the healthcare industry. Consequently, healthcare DWH projects often incur hefty financial costs and take years to complete.

Therefore, to streamline and simplify complex data warehousing development, this blog introduces the fundamentals of the healthcare data warehouse including DWH architecture design, key features, and a beginner’s roadmap for its implementation.

The Growing Importance of Healthcare Data Warehouses

The healthcare data warehouse is a central database for storing analysis-ready data coming from multiple sources. This data can come from electronic health records (EHRs), medical records (EMRs), prescriptions, radiology reports, health claims, etc. By utilizing a DWH, providers improve patient-care delivery and experience, optimize service efficiency, and reduce operating costs.

The growing importance of healthcare data warehouses is rooted in three major factors: digital optimization, innovative medical solutions, and prudent decision-making.

Digital optimization for Healthcare Stakeholders

Healthcare data warehouse solutions ensures quick, digital access to all forms of valuable data, from patient history and scan reports to insurance claims and payment records. Different stakeholders in healthcare, such as clinicians, physicians, administrators, and claim adjusters can automatically access the relevant information and subsequently provide optimized services to their customers.

Innovative Medical Solutions

Healthcare organizations use descriptive, predictive, or prescriptive analytics to design innovative health solutions through data warehouse models. For example, a descriptive analysis of historical clinical data can be used for monitoring the spread of infectious diseases. Likewise, prescriptive analysis of a patient’s history can reveal other pre-existing conditions, allowing doctors to provide customized health plans. Similarly, healthcare data analysts can forecast both individual health-conditions and public health crises by using predictive analysis for future outcomes.

These approaches rely on DWHs to provide both historical and current data in a detailed, cleansed, and consolidated form.

Reliable Decision-Making

Providers also rely on the accuracy of the data held in a data warehouse to improve the quality of their own decision-making. Firstly, the DWH consolidates data from a variety of health systems to give a unified view of healthcare data to decision makers. Secondly, it maintains the data in an analysis-ready form by ensuring data quality and consistency. Thirdly, it provides faster access to both historical and real-time data for accurate healthcare data analysis and agile decision-making.

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Data Warehouse Architecture for Healthcare Providers

Before developing a healthcare data warehouse, it is imperative to understand its fundamental layers. An enterprise data warehouse architecture, capable of satisfying healthcare intelligence and analytics needs, consists of the following layers:

Data Source Layer

This consists of internal and external data sources carrying data relevant to healthcare providers. Different types of data used in healthcare can come from sources like ERP systems, EHR reports, radiology reports, prescriptions, CRM systems, claim management systems, and surveys.

Staging Layer

A staging area temporarily stores and processes data coming in from the disparate data sources. Here, ETL (extract, transform, and load) processes are employed to transform, cleanse, and prepare large swathes of data for unified storage and analysis.

Data Storage Layer

Data storage is the unified repository for storing data which is loaded through ETL/ELT processes. It stocks and manages structured data for reporting and analytics. The data storage layer can also carry data marts; these are DWH subsets designed for specific business areas such as HR, Finance, Technology, Operations, etc.

Analytics and Reporting Layer

This final layer consists of data analytics and business intelligence tools used for drawing actionable insights from data. BI tools query data from the healthcare data warehouse and share analytics and insights  via detailed reports, visualizations, graphs, charts, and summaries.

Key Features of a Healthcare Data Warehouse

A powerful healthcare data warehouse enables providers to adopt a value-based approach towards healthcare delivery and patient based care. However, to be effective, the healthcare DWH needs to meet certain standards. Moreover, health-related data needs to be handled with care and precision given its sensitive nature.

Here are a few key features which should be prioritized in every healthcare data warehouse.

Data Warehouse Performance

Reliable healthcare data warehouses must be able to perform quick querying and fast retrieval of analysis-ready data. The following features reflect the performance of data warehousing processes:

Parallel-processing power

With parallel-processing, data warehouses can break large querying tasks into smaller chunks of work which can be performed in parallel.

Data Warehouse Automation

To reduce time-to-market, powerful data warehouse automation tools, such as Astera DW Builder, can automate entire data pipelines within a DWH architecture. With automation, the data warehouse itself processes the data and carries it from the source to data storage layer without any manual intervention. This ensures that healthcare users get timely access to critical information without relying on the laborious process of updating the data warehouse manually.

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Elastic Scaling

A healthcare DWH should allow flexible scaling—including increases and decreases in size—of data storage layers and computational power so that dynamic healthcare demands can be met.

Security and Compliance

These features help guarantee high healthcare data security and meet crucial regulations, such as HIPPA, for managing private data.

Role-based access control

Healthcare DWH administrators can restrict or limit data access based on specific job roles. Role-based access control ensures that information is extended only to relevant personnel.

User authentication methods

Healthcare data warehouses ensure security by gating interactions by employing different user-authentication methods such as bearer-token or multiple-factor authentication.

Automated Backups

Many data warehouses automatically store data backups in different places on the cloud to prevent the loss of data in case of disaster recovery.

Robust Data Integration

Integration of disparate data sources lies at the center of every healthcare data warehouse. Data warehouses store data from different sources in a structured format to enable reporting and analysis.

ETL/ELT based data integration

Healthcare data warehouses should come with the ability to choose either ETL or ELT for data integration and loading. This would allow end-users to build healthcare data pipelines according to their specific architectural requirements.

Support for complex healthcare transformations

Support for transformations that can cleanse, profile, and structure data is crucial for making healthcare data usable. Moreover, healthcare data warehouses should also be able to set custom transformation and rules for special cases.

Incremental or bulk loading of data

Quality healthcare data warehouses allow for both complete and incremental extraction and loading of data, as per the organization’s individual requirements. With incremental loading, the data warehouse can be updated as the need arises.

Roadmap for Implementing a Healthcare DWH

Healthcare DWH development is a complex endeavor. Considerable time, effort, and technical expertise go into designing and deploying a DWH which meets the needs of a healthcare facility.

The following 5 phases act as the roadmap for development of a robust healthcare data warehouse.

Planning

Healthcare data warehousing starts with planning where potential users come together to assess the contextual need for a DWH. Limitations in the existing healthcare data management system are analyzed and strategic objectives for alleviating them are mapped out.

At this stage, business decision makers and solution architects lay down a phased strategy of adding various data sources and defining subsequent data warehousing processes. Custom requirements for performance, security, compliance, and integration are taken into consideration as well. Finally, required physical assets and data science based human resources are gathered accordingly.

Designing

In the healthcare data warehouse design phase, the main data architecture is designed according to the organization’s requirements and business rules. Tools like Astera DW Builder are used to design a data model and define the healthcare data warehouse schema. Here, a visual data model is used to draw relationships between different entities of various data sources in the most optimized way through machine learning. Moreover, all the required data sources of the healthcare provider are made part of the data model.

Development

Development phase involves building out data pipelines for extracting data from relevant sources, transforming and preparing it in a structured format, and loading it into data storage. Here, requisite infrastructure is rolled out to make way for data warehouse deployment.

Deployment

In the deployment phase, the data models and schemas are implemented on the data storage layer of your choice. Deployment can be carried on premises or any cloud provider such as Azure, Amazon Redshift, or Snowflake.

Here, functionals data pipelines are used to populate the deployed schemas. Moreover, the data storage layer is connected to analytics and reporting tools for generating actionable healthcare insights.

Maintenance

Post-development maintenance involves updating the data warehouse in accordance with changes in healthcare requirements and business needs. Here, maintenance involves making sure that various data warehouse objects including columns, tables, schemas are fully up to date with current requirements.

For example, the healthcare provider might have to add columns to measure new medical metrics or create different views of existing metrics. Data warehouses require routine maintenance for adapting to changing internal and external environments.

Deploying a Data Warehouse with Astera DW Builder

If you’re looking to develop a healthcare data warehouse, or update your legacy architecture, you can use our automated, meta-data driven tool: Astera DW Builder. Equipped with in-depth pipeline automation, a code-free environment, intelligent data-mapping, and integration capabilities, Astera DW Builder simplifies complex healthcare data warehousing processes. From implementing specific healthcare data rules to creating complex data models and populating them with a wide range of medical data sources, healthcare providers can truly leverage data warehousing with Astera DW Builder.

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