Operationalizing and digitizing person-centered daily functioning: a case for functionomics

Area of Science:

• Health Informatics
• Data Science
• Biomedical Research

Background:

• Vast amounts of daily functioning data are collected but remain inaccessible.
• Unstructured data limits the potential for revolutionizing person-centered healthcare.
• Integrating and analyzing this data is crucial for advancing health knowledge.

Purpose of the Study:

• Introduce functionomics as a complementary 'omics' initiative to leverage daily functioning data.
• Operationalize functionomics using the International Classification of Functioning, Disability and Health (ICF).
• Demonstrate the application of FAIR principles for making functionomics data machine-readable and accessible.

Main Methods:

• Applying the FAIR (Findable, Accessible, Interoperable, Reusable) principles.
• Illustrating a step-by-step application of FAIR principles in a practical example.
• Utilizing a federated data infrastructure for analyzing functionomics data.

Main Results:

• Functionomics data can be made machine-readable and accessible under certified conditions.
• Established FAIR functionomics data repositories facilitate new knowledge generation.
• The proposed methods enable improved health outcomes and person-centered healthcare.

Conclusions:

• Implementing FAIR principles is essential for unlocking the potential of functionomics data.
• Federated data infrastructures are key to analyzing functionomics data effectively.
• Adoption of these methods by the research community will advance health and healthcare.