Patient-centered approaches for patients with systemic autoimmune rheumatic diseases: development and evolution

ABSTRACT

Introduction

European Reference Networks (ERNs) are dedicated to rare complex diseases. Systemic autoimmune rheumatic diseases (SARDs) comprise a group of disorders, some of which are rare, complex, and chronic, characterized by relapsing-remitting course and requiring targeted treatments for long periods; SARDs are also associated with various co-morbidities and therefore health-care infrastructures, at the highest level of expertise are required.

Areas covered

For the current work, literature on the basic characteristics of a center of excellence dedicated to SARDs, its advantages over the existing health infrastructures in order to improve health and social care, its contribution to the education of health-care workers, and the related research opportunities are presented. In addition, our experience, vision, and initiatives as a new member of the ERNs are reported.

Expert opinion

A restructure in healthcare policy and resource allocation, based on centers of expertise, is necessary to improve the medical care of patients with SARDs.

KEYWORDS:

• European reference networks (ERNs)
• ERN-RITA
• ERN-Re CONNET: center of expertise
• center of excellence
• rare and complex diseases
• musculoskeletal autoimmune diseases
• immunological disorders

Article highlights

• Systemic autoimmune rheumatic diseases (SARDs) comprise a group of disorders with many clinical unmet needs.

• Centers of expertise (CoE) constitute a framework dedicated to enhancing the care of patients with rare diseases, promoting a holistic approach.

• A CoE provides high-quality multidisciplinary health care ensuring universal and equitable access to health care.

• Within a properly running CoE research and educational advancements are promoted.

• Novel interactive health-care platforms could facilitate an augmented patient–physician relationship.

• Big data processing, employing artificial intelligence and machine learning algorithms, strengthens personalized medicine approaches.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Abbreviations

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Additional information

Funding

This paper was not funded.