How FAIR Principles Can Bring Equilibrium to Healthcare Data

Data is a driving force behind healthcare. This information can better guide providers, patients and resources, enabling all these stakeholders to act proactively, effectively and correctly.

However, these insights aren’t without their issues. There are myriad challenges facing data management and usage within healthcare, including accessibility, accuracy and security, to name a few. How can stakeholders from across the healthcare ecosystem better anticipate and address those challenges?

Integrating the FAIR data principles empowers people with the knowledge and technological support to face unique healthcare data issues with confidence and care to create quality care outcomes.

Understanding the FAIR principles

The FAIR principles were developed with the goal of fostering data-driven and machine-assisted open science. The overarching aim is to make data more:

• Findable: The first step in (re)using data is ensuring it’s discoverable to both humans and computers. Machine-readable metadata is crucial to automatic discovery and forms an essential part of the FAIR verification process, which includes: 

• • (Meta)data is assigned a globally unique and persistent identifier. 

• • Data is described with rich metadata (defined by R1 below). 

• • Metadata explicitly includes the identifier of the data it describes. 

• • (Meta)data is registered or indexed in a searchable resource.
    

• Accessible: Once data is located, users need to understand how to access it to authenticate and authorize processes. 

• • (Meta)data is retrievable by its identifier using a standardized communications protocol. 

• • The protocol is open, free and universally implementable. 

• • The protocol allows for an authentication and authorization procedure, where necessary. 

• • Metadata remains accessible even when the data is no longer available. 
      

• Interoperable: Data often requires integration with other datasets and must seamlessly interface with applications and workflows for analysis, storage and processing. 
  

• • (Meta)data uses a formal, accessible, shared and broadly applicable language for knowledge representation. 

• • (Meta)data uses vocabularies that adhere to FAIR principles. 

• • (Meta)data includes qualified references to other (meta)data. 
      

• Reusable: The ultimate goal of FAIR is to optimize data reuse. Achieving this requires comprehensive descriptions of metadata and data to facilitate replication and combination in diverse settings. 

• • (Meta)data is richly described with a plurality of accurate and relevant attributes. 

• • (Meta)data is released with a clear and accessible data usage license. 

• • (Meta)data is associated with detailed provenance. 

• • (Meta)data meets domain-relevant community standards. 

  
It is worth mentioning that despite recent attention, FAIR is not positioned as a revolutionary concept. Rather, it’s a practical guide to unify individuals and institutions striving to create a globally operational Internet of FAIR Data and Services.

These principles encompass data, metadata and infrastructure, emphasising the importance of a searchable resource for both metadata and data. As we navigate the landscape of FAIR principles, it becomes evident that they serve as a guiding beacon for fostering a collaborative and globally interconnected Internet of FAIR Data and Services – a system that can provide unlimited value to the healthcare and life sciences landscape.

The value of FAIR in healthcare

In recent years, the healthcare sector has experienced a surge in the adoption of FAIR principles, marking a significant shift in how health data is handled. These principles have gained prominence, especially with initiatives like the European-Commission-backed FAIR4Health project.

What advantages do the FAIR principles incorporate into healthcare from day to day? They create patient and provider experiences that are:

1. Efficient

In healthcare, finding relevant data swiftly is critical. FAIR’s “Findable” principle ensures that health-related data, whether on chronic diseases or precision health, is easily discoverable. This is paramount for efficient research and patient care, allowing practitioners to swiftly locate and apply pertinent information.

2. Compliant

Accessibility, particularly in the health domain, is imperative. The accessible principle ensures that health data can be retrieved under well-defined conditions. This is vital for preserving the ethical considerations surrounding health data, especially when used beyond academic realms. The FAIR principles, by design, complement ethical considerations in healthcare data usage so insights are discovered, secured and applied as they were intended.

3. Multi-modal

The interoperability of health data is crucial for seamless integration into systems driving precision and learning health systems. FAIR's "Interoperable" principle ensures that health data uses a formal, accessible language for knowledge representation. This facilitates the coexistence of diverse datasets, enhancing the potential for comprehensive health system improvement and more personalized touchpoints.

4. Repeatable

The ultimate goal in healthcare is leveraging information to create lasting results. The "Reusable" principle emphasizes rich descriptions of health data, along with clear usage licenses and detailed provenance. This not only streamlines replication and combination of data in various settings, but it also aligns with evolving community standards and creates touchpoints that are more customised and enjoyable for all healthcare stakeholders.

The application of FAIR principles in healthcare is not merely a trend; it's a strategic response to the increasing availability of large datasets afforded by digital healthcare and life sciences. 

FAIR principles act as a guiding framework, ensuring that health data is handled responsibly and made available both for human and machine processing. As the healthcare landscape evolves, FAIR principles stand as beacons, illuminating a path where efficiency, transparency and community-centric governance converge to shape the future of health data utilisation.