The CFHealthHub Learning Health System: Using Real-Time Adherence Data to Support a Community of Practice to Deliver...

Area of Science:

• Health Systems Science
• Behavioral Science
• Chronic Disease Management

Background:

• Adherence to prescribed therapies in long-term conditions like cystic fibrosis (CF) is often low, despite proven benefits.
• Low adherence negatively impacts long-term health outcomes for individuals with CF.
• Effective interventions are needed to improve self-care and adherence in CF management.

Purpose of the Study:

• To describe the implementation and evolution of CFHealthHub, a learning health system for CF self-care.
• To explain the conceptual frameworks and strategies underpinning CFHealthHub's development and integration.
• To highlight CFHealthHub's capacity for real-time health technology assessment, quality improvement, and research.

Main Methods:

• Implementation of a behavior change intervention focused on adherence to inhaled therapies.
• Collection of objective adherence data in adult CF centers.
• Application of implementation and quality improvement strategies to scale the intervention.

Main Results:

• CFHealthHub is active in over 50% of adult CF centers in England.
• The system has progressed from pilot to a large randomized control trial and is now a learning health system.
• CFHealthHub supports real-time health technology assessments, quality improvement, and research trials.

Conclusions:

• CFHealthHub effectively supports self-care habits and adherence to preventative inhaled therapy in CF patients.
• The learning health system model facilitates the integration of evidence-based interventions into routine clinical care.
• CFHealthHub's ongoing implementation across centers demonstrates its scalability and potential for widespread impact on CF outcomes.