Engraft: A Collaborative Learning Health Network for Enhanced Transplant and Cellular Therapy Outcomes

Area of Science:

• Transplant and Cellular Therapy (TCT)
• Learning Health Systems
• Quality Improvement (QI)

Background:

• Transplant and cellular therapy (TCT) outcomes vary significantly across institutions.
• There is a need for collaborative, data-driven approaches to standardize care and accelerate innovation in TCT.

Purpose of the Study:

• To introduce the Engraft Learning Health Network (LHN) design and objectives.
• To outline Engraft's strategy for improving TCT patient outcomes through collaboration and data sharing.
• To highlight the network's focus on rapid-cycle quality improvement and implementation research.

Main Methods:

• Establishment of a multicenter, real-world clinical registry for TCT.
• Development of quality improvement (QI) toolkits and best practice guidelines.
• Facilitation of de-identified patient data sharing among network members.
• Engagement of patients and caregivers as equal partners in network governance.

Main Results:

• Early successes include the development of QI toolkits and consensus-building around best practices.
• Focus on reducing non-relapse mortality and improving patient survivorship.
• Demonstrated commitment to patient and caregiver engagement in network direction.

Conclusions:

• The Engraft LHN model facilitates collaborative, data-driven improvements in TCT.
• Standardizing practices and sharing real-world data can reduce outcome variability.
• Patient and caregiver partnership is integral to the network's success and future goals.