Healthcare Data Warehouse: Everything You Should Know

Topics:

• What is a Healthcare Data Warehouse?
• Is Investing in Medical Data Warehousing a Smart Choice?
• Benefits of Data Warehousing in Healthcare
• Data Warehouse Architecture for Healthcare
• Key Features to Look For in a Healthcare Data Warehouse
• Most Commonly Used Healthcare Data Warehouse Models
• Why You Really Need a Healthcare Data Warehouse
• Beneficial Integrations for Healthcare Data Warehouse Solutions
• How to Ensure Successful Implementation of Healthcare Data Warehouse Model
• Top-rated Data Warehouse Platforms for Healthcare
• Why Rely on Rishabh to Implement Your Health Data Warehouse Solution

It is a centralized repository for electronic health records and clinical data retrieved from disparate sources, processed, analyzed, and distributed to facilitate data-driven decisions in minutes.

They help transform the organizational data into accessible, actionable information to drive quality improvement across clinical outcomes, patient experience, and operational efficiency. An experienced data analytics company can help you develop data and metrics as valuable assets to improve standardization and transparency.

• Timely & Cost-effective Decisions
• Improved Treatment Outcomes
• Enhanced Patient Experience
• Personalized & Improved Care Quality
• Efficient Resource Management

A database is the core unit of a business intelligence solution.

It integrates:

1. A Data Source Layer
Contains medical data generated from internal and external data sources (EHR, EMR, ERP, CRM & claims management system)

2. A Staging Zone
This is the temporary intermediary storage. All the medical data is processed through the extract, transform, and load (ETL) framework.

3. Data Storage Layer
This layer is typically structured and includes centralized storage. It features data marts such as clinical subsets oriented to a specific line of business (accounting, HR, inventory) or specialty (pediatrics, radiology, intensive care unit).

4. Business Intelligence & Analytics
Data mining, reporting, visualization tools, and business analytics

Performance

• Materialized view support
• Indexing of patient data
• Result caching
• Automated scaling of computing resources
• High-efficiency query processing
• Machine learning capabilities
• Dynamic management of concurrency and performance

Data Security and Regulatory Compliance

• Granular level security for rows and columns
• Multi-factor authentication
• Dynamic masking of healthcare data
• Periodic assessment and detection of threats
• Compliance with applicable regulations (HIPAA, HITECH, FDA)
• Encryption of healthcare data at rest and in transit

Data Integration

• Assimilate structured, semi-structured and unstructured medical data generated from electronic health record systems, ERP software, HR management system, claims management system and public health databases.
• ELT-based clinical data integration
• Controlled healthcare data management
• Comprehensive and incremental health data extraction
• Ingestion of big data & streaming data
• Medical data loading & querying using SQL

Storage

• Storage of metadata
• Storage of protected health information (PHI)
• Storage of integrated, summarized, historical or subject-oriented data
• Cloud, on-premise, or hybrid storage environments

Choosing the suitable DWH model for your practice is essential. It helps develop and map the capabilities that impact the future adaptability & time-to-value of healthcare systems.

Enterprise-wide Data Model Approach

Providers can opt for an enterprise-wide data model when they need additional power to keep up with data sets residing in every corner of the organization.

It is one of the most highly recommended models by analytics vendors. It takes a top-down approach to model a comprehensive database that determines, in advance, the KPIs to analyze for improved patient safety, satisfaction and treatment outcomes.

However, this model is the best choice if you’re building a new system from scratch. But, in the context of healthcare, you need to create a secondary system that extracts data from existing systems and makes it all work in sync. This is not only downright difficult but also tedious, time-consuming and expensive. However, with the right skills and experience, it is possible.

Independent Data Mart Approach

The design of this model takes a bottom-up approach. You start small with building individual data marts as and when you need them. Accordingly, if you want to analyze insurance claims or revenue cycle, you need to develop an individual data mart for that particular process. This model brings in data from different sources that apply to a specific area.

One can start implementing the model and tracking data much faster. The independent data mart approach works more quickly and efficiently than the typical 2-5 year life cycle of the enterprise-wide data model.

A data storage solution can be implemented on-premise, within the four walls of your healthcare organization, or located in the cloud. But, with the markets for analytics-as-a-service (AaaS) and infrastructure-as-a-service (IaaS) gaining momentum, it could bring down the burden of an immediate up-front cost for providers looking to unlock the potential of big data.

It enables healthcare organizations to systematically monitor and measure different chronic conditions, care delivery processes, payments, and standard operating procedures. With the volume and type of clinical data constantly increasing and evolving, it has become different for organizations to store, share and analyze their data on time.

Enterprise data warehouse in healthcare is a single-point platform that offers an end-to-end view of data stored across different systems. It includes – public health records, claims, cost accounting systems, inventory, supply chain and more.

For your business, it can;

• Improve performance of both systems with separate processing and analysis of big data from transactional databases
• Better decision making with the managed data that brings outstanding quality, consistency and accuracy
• Optimization of reporting and analysis helps guaranty a higher quality of data
• Central platform offers elimination of duplicate records, errors and inconsistent information
• Standardizing and centralizing the internal reporting helps manage possible inconsistencies in data sources

A Data Lake

While an enterprise DWH for healthcare can store highly structured data, a data lake works as cost-effective storage of semi-structured and unstructured data (manual patient records, image-based test reports, practitioner’s notes, etc.)

The machine learning models (for instance, healthcare demand forecasting) get created based on the information stored in the data lake.

Business Intelligence Software

A self-service BI system enables healthcare organizations to be agile and self-reliant in visualizing, analyzing and reporting the medical data structured in the EDW. This facilitates quick and easy transfer of analytics insights to key decision-makers.

Security & Regulatory Compliance
Store and process confidential medical data within highly secure infrastructures (Google Cloud, AWS, Microsoft Azure), ensure dynamic data masking and all-time encryption, multi-factor authentication, penetration testing, restricted data access, and vulnerability assessment.

Agility & Scalability
To upload any type and amount of medical data (structured, semi-structured, unstructured) instantly and address the objectives of new data analytics.

Proof of Concept
Validate your healthcare data warehouse solution with a PoC. Give it a spin to get the hang of its true potential, along with feedback from real-life users.

There are plenty of options available on the market. However, the following three DWH platforms are shortlisted for their above-average performance and excellent customer satisfaction reviews.

Rishabh Software has helped several healthcare organizations expand and revolutionize their care-delivery systems with end-to-end data warehousing services. Because data accuracy is paramount to running any healthcare facility efficiently, we’ll work with you to implement a reliable EDW that drives informed decisions and profitable actions in the long-term.

We provide our clients with healthcare data warehouse that comprises of:

1. A fully compliant and secure technical infrastructure
2. Industry-leading data analysis tools
3. Flexibility to integrate all healthcare data types for SQL-querying
4. Ongoing assistance from data science experts
5. Scalability of storage and computing resources
6. Custom-tailored architecture design
7. Seamless integration of EMR, EHR, HIE & CDS systems
8. Healthcare data cleaning & migration
9. Organized metadata management procedures
10. DWH maintenance and support