Healthcare Data Warehousing [Full Guide]

The advantages of healthcare warehousing are numerous for healthcare stakeholders. While the benefits of having repositories for healthcare data are evident, there are equally substantial challenges. This article aims to dissect these challenges and provide insights into constructing a national clinical data repository from the ground up. Our goal is to enable you to harness the benefits of data warehousing in the healthcare sector.

The World Health Organization (WHO) recently shared the second edition of their shared language to describe the uses of digital technology for health. This document suggests the data warehouse as a healthcare data storage and aggregation solution. 

Data warehouse has many benefits to it for all healthcare stakeholders. In addition to the apparent advantages of repositories for healthcare data, there are no less significant challenges. 

In this article, we will break down data warehouse challenges and share our knowledge of building a national clinical data repository from scratch so you can leverage the advantages of data warehousing in healthcare.

What is data warehousing in healthcare?

A healthcare data warehousing is a centralized repository for storing data retrieved from EHRs, EMRs, laboratory databases, and other sources. Data from various sources undergo a transformation process to meet the standardized data format of a warehouse to simplify further analysis.

A clinical data warehouse in healthcare can serve as a single source of truth for any healthcare organization since its primary goal is to collect and provide access to accurate data for advanced decision-making support. The data warehouse provides a robust platform for data analysis. The warehouse allows for applying data mining algorithms in healthcare to identify patterns, trends, and relationships between data to solve complex issues. 

Below, you can find a detailed chart of the data warehouse architecture for healthcare, illustrating its various components and functionalities.

Healthcare Data Warehouse Architecture

We can see from the scheme that the healthcare data warehouse model involves several components that make warehousing possible, namely the data sources, staging area, and storage layer.

What are the benefits of a data warehouse in healthcare? 

Data warehousing impacts multiple aspects of healthcare, including data management and exchange. Even though building a data repository demands investments and technical expertise, the many benefits of a clinical data warehouse make the effort entirely worthwhile.

One of the main advantages of a clinical data warehouse is access to a complete picture of a patient’s health. Thanks to the ability to translate and collect data from different sources, a data warehouse can impact outcomes by gathering, for instance, data from a laboratory repository, a radiology database, and an EHR. Analysis of data from different sources and timely access to healthcare information significantly improves the efficiency of healthcare services.

Seamless Information Exchange

A healthcare data warehousing enhances collaboration between different entities by consolidating information from electronic health records (EHRs), laboratory databases, and insurance claims into a unified repository for the best convenience of all healthcare actors. 

Precise resource and cost management

Data warehouse provides a centralized repository for diverse healthcare data, simplifying information data collection, storage, and management. Hence, healthcare professionals can access vital data promptly, streamlining healthcare workflows and enabling effective resource allocation.

Fast healthcare operations

Healthcare professionals deal with EHRs, insurance claims, lab results, and other types of healthcare data daily. Efficient collection, storage, and processing of such diverse data can significantly speed up decision-making. An example of high-quality analysis is digital twin technology in healthcare.

The warehouse helps automate various processes by providing prompt access to all types of healthcare data. When all data is gathered in one place in a unified format, healthcare professionals spend less time processing healthcare data and performing everyday tasks quickly and effectively.

Advanced predictive analytics

The analysis of data gathered from different sources can become actionable insights. A data warehouse provides comprehensive data storage for collecting big volumes of data in one centralized repository. Integrating data from various sources helps identify patterns and trends that may be impossible to see when analyzing data from a single source.

Moreover, data warehouses often provide a real-time data analysis option, which helps track data correlations as they emerge. Real-time data analysis is important for effective predictive analysis. For example, an increased number of patients with specific symptoms can help with effective early intervention and pandemic prevention.

One of the key challenges in data analytics is ensuring the accuracy and consistency of data from diverse sources, which is crucial for generating meaningful insights and facilitating real-time analysis.

Medical research support

A healthcare data warehouse provides researchers access to large amounts of cleansed clinical data to extract insights. Thus, analysis of risk factors and treatments for specific conditions can improve medical research. 

In addition, data warehousing can promote collaboration between different organizations and groups of researchers. For instance, the integrated data about different clinical trials would help evaluate the safety and efficiency of various drugs and treatment procedures.

Challenges of a healthcare data warehouse

Lack of data interoperability 

One of the main challenges of an enterprise data warehouse in healthcare is the complexity of the data integration process. Data warehouses retrieve data from a wide range of sources which store data in different sources such as EHRs, wearables, insurance companies, etc. Most sources of healthcare information store data in various formats, potentially complicating data collection, integration, and analysis.

Healthcare data security threats

As we discussed in one of our previous articles, the value of data privacy in healthcare and rapid attempts of cyber attacks cause many security concerns. Therefore, protecting patients’ data must be a top priority for all healthcare stakeholders. Developing a robust data protection plan and using effective healthcare data security solutions are important for creating a secure healthcare data repository.

Lack of enterprise-level technical expertise

Healthcare data management is a complex process itself, but for a clinical data warehouse, it requires deep knowledge and experience to maintain a repository accurately. Therefore, the design and implementation of a healthcare data warehouse should be performed by specialists that know all the ins and outs of the healthcare IT world and can use their experience toward achieving the needs of a specific organization. 

Examples of data warehouse in healthcare

The Global Health Observatory | WHO

WHO established the Global Health Observatory (GHO) as a public health observatory to facilitate the exchange of global health data. The GHO repository provides extensive data, tools, analysis, and reports.

WHO’s data warehouse is organized around various themes, covering critical aspects, including estimates of mortality and global health, health systems, public health and environment, and more. Each theme unfolds statistics and reports available for download.

The GHO’s Themes List includes:

  • Environment and Health
  • Child Malnutrition and Mortality
  • Global Health Estimates
  • Health Workforce
  • Immunization Coverage and Vaccine-Preventable Diseases
  • Mental Health
  • World Health Statistics, and many more. 

The GHO is a trusted source for health statisticians, epidemiologists, economists, and public health researchers, providing a comprehensive overview of global health and empowering them to make informed decisions and implement targeted interventions.

National Clinical Data Repository | Edenlab

The National Clinical Data Repository (NCDR) in Ukraine was created as part of the digitalization of the national eHealth system. In Ukraine’s eHealth system, the NCDR serves as the key instrument in overcoming the challenges of paper-based inefficiencies by navigating the accuracy and reliability of a data storage solution while ensuring strict data security concerns.

Key Features:

  • Ensuring Data Reliability and Deduplication: The NCDR includes the MPI system, which provides deduplication and ensures the integrity of healthcare data in the repository. In addition, the system contributes to the enhancement of claims management processes.
  • Preserving Data Accuracy with Electronic Signatures:  All the changes applied to medical records must be signed by a doctor with an electronic signature, making clinicians responsible for the accuracy of the data stored in the national repository.  
  • Supporting Data Security: Using pseudonymization for health records and implementing an Attribute-Based Access Control mechanism ensures advanced security for data stored in the repository by restricting access with precision based on specific attributes. 
  • Providing Data Interoperability: The NCDR is built with the Fast Healthcare Interoperability Resources (FHIR) standard. FHIR is one of the most prevalent healthcare data standards recommended by government agencies globally for achieving semantic interoperability in healthcare. FHIR allows for supporting the highest level of data interoperability within the NCDR.

In addition to supporting the creation of 1.5 billion electronic medical records in the e-health system, the National Clinical Data Repository in Ukraine enables a modern and secure healthcare data storage and management system. Furthermore, the implementation of robotic process automation (RPA) in healthcare within this context can offer notable advantages. RPA use cases in healthcare may include automating routine tasks such as data entry, appointment scheduling, and claims processing.

How Can Kodjin Help you to Overcome Challenges of Data Warehouse in Healthcare

Considering all the challenges of data warehousing in healthcare, Edenlab’s FHIR experts created the Kodjin FHIR Server to serve as an enterprise-level data management solution. Its advanced validation functionality ensures data accuracy by validating information against FHIR profiles and maintaining a unified data format, making it more straightforward to use the information gathered in the data warehouse for further analysis. Additionally, the integration of text mining in healthcare with the Kodjin FHIR Server enhances the extraction of valuable insights from large data sets.

The FHIR API establishes a standardized communication channel between diverse systems and provides a seamless solution for data exchange in a standardized format.

Do you need a solution for housing a vast volume of healthcare data? Contact us for more details about building a data repository and leveraging FHIR. We’ll gladly discuss your health IT project and develop a strategy for implementing FHIR in your particular use case to upgrade your data warehouse capabilities.


1. Which warehouse is best suited for the healthcare sector?

A FHIR-first repository will support the most important aspects of healthcare data warehousing data accuracy, security, and interoperability.

2. How is a data warehouse different from a clinical repository?

A data warehouse is a repository crafted to gather and house data from diverse source systems across an enterprise or industry.

3. Does a clinical data warehouse contain structured data?

Clinical data warehouses typically store structured data, offering a standardized format for precise analysis, reporting, and informed decision-making in the healthcare field.

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