R&D

Addressing Healthcare Data Management Challenges with Technology

The introduction of EHDS regulation marks a significant step towards unified health data exchange across EU member states. In this article, we will explore the interoperability challenges within the EHDS and provide suggestions for addressing these challenges.

In healthcare, data is crucial for patient care, research, and system management. Accurate and comprehensive clinical data enables informed decision-making, improves patient outcomes, and enhances operational efficiency. While the importance of data is undeniable, its value is highly dependent on the standards governing its collection, storage, and usage within healthcare organisations. Data standards in healthcare ensure that data is consistent, accurate, and interoperable across different systems and institutions.

This is where technologies play a vital role; healthcare software development equips professionals with the tools needed to manage data effectively and ensure compliance with standards.

Navigating the landscape of health data standards

In April 2024, the European Parliament approved the European Health Data Space (EHDS) framework. It is the initiative by the European Union aimed at creating a common framework for healthcare data exchange across its member states.

The EHDS focuses on several key goals:

Interoperability and standartisation. Ensuring medical data can be seamlessly shared and understood across different systems, healthcare providers and countries.
Enhanced data quality. Improving health data collection to support effective secondary use in research, policymaking, and innovation.
Data safety and anonymity. Implementing high-security requirements to safeguard the data.

The healthcare industry uses various standards to work with critical data. However, the large number of these standards makes the process more complicated than it should be. As part of the European Health Data Space (EHDS), all healthcare organisations must ensure their data is interoperable. Unfortunately, the current framework does not specify which standards should be adopted. Interoperability, in this context, refers to the ability of different systems to exchange information securely.

Researchers opinion: healthcare data types and corresponding standards

Different data standards satisfy different needs, and no single standard can meet all healthcare system requirements. The scientific paper "Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care" identifies three types of healthcare data: clinical care and administration, data exchange, and longitudinal analysis. Each of the types has specific standards to address:

Clinical care and administration: Care documentation should capture a wide range of hospital data, including unexpected patient-related events. The OpenEHR standard can cover this.
Data exchange: Healthcare data should be transmitted in small, relevant quantities, including all the necessary information from medical records and nothing extra. The FHIR standard can address this.
Longitudinal analysis: This focuses on identifying patterns, trends, and predictors. Since these patterns are often complex, data from multiple systems and organisations need to be aggregated, collected, or compared to make reliable conclusions. The OMOP standard can cover this.

Detailed overview of healthcare data standards

1. OpenEHR

OpenEHR is an open standard specification in health informatics that uses a two-level modelling approach—informational and knowledge-based—to facilitate flexible and consistent data management. By using predefined structures, OpenEHR ensures accurate data capture and sharing.

A centralised database with a tree structure allows for retrieving a complete patient record. The database schema is built on reusable archetypes and templates: patient-centric and clinical, and DB schema is extensible by design.

2. FHIR

3. OMOP

The picture above illustrates the architecture diagram of a health data platform. This can be used for data storage, analysis, and transfer between health data systems. The platform combines three health data standards: OpenEHR, OMOP, and FHIR.

Diagram description:

Data Sources: "Data Source 1", "Data Source 2", ..., "Data Source N". These are the input data sources. The objective of the Raw Data Storage is to collect everything in raw form.
Raw Data Storage: Collect raw data from multiple data sources. Acts as a central repository for initial data collection before processing.
OMOP Storage: An analytics storage system. It consists of a vocabulary mapping module and a data table mapping module and uses a "Common Data Model" to standardise data. It is the basis for various systems that analyse data, such as Data Science, Artificial Intelligence, and Data Analytics.
OpenEHR Storage: The goal is to collect data and store it in a standardised format. Contains the "Information Data Module" and "Knowledge Data Module". Flexible and consistent data management through predefined structures. Transfers data to Primary and Secondary Use Systems using FHIR.

Azure healthcare data platform implementation

The context diagram of the data health platform above describes the general architecture of a health data platform that is not connected to any technology. For instance, by leveraging Azure services, the platform can be effectively implemented as follows:

Data from multiple input sources is either pulled via Azure Functions or pushed through Azure Front Door, which integrates with Azure WAF Policy for security. The data is managed through API Management and directed to an Event Hub, from where Azure Functions writes the data into a Storage Account. The Storage Account acts as a centralised repository for further processing into either OMOP or OpenEHR storage.

In OMOP storage, data is transformed and standardised using the Data Tables Mapping Module and Azure Data Factory. Processed data is analysed through Machine Learning, Cognitive Services, and Power BI. In OpenEHR storage, data is managed through Information and Knowledge Data Modules, facilitated by Azure Functions and API Management, and transferred to primary and secondary use systems via Azure API for FHIR.

The platform also integrates Azure's Identity, Security, and Monitoring services, including Azure Active Directory, Log Analytics, Application Insights, Key Vault, Defender for Cloud, Notification Hub, Azure Monitor, and Role Assignment, ensuring secure and efficient operations.

Conclusions

The EHDS framework unifies health data exchange across Europe. By using standards like OpenEHR, FHIR, and OMOP, the proposed health data platform can overcome interoperability challenges, improve data quality, and ensure data security. The use of Azure services shows that modern cloud technologies can help build a flexible, secure, and efficient health data system. The suggested unified health data management platform can support EHDS' goals and improve access to advanced data analytics and better patient outcomes throughout Europe.

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FAQs

What is the meaning of data in health care?

Data in healthcare refers to any information related to patient health, medical history, treatment plans, outcomes, and operational metrics within healthcare organizations. This includes clinical data, administrative data, and financial data.

What are data models in healthcare?

What are the three V's of healthcare data?

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