The learning health system for pediatric nephrology: building better systems to improve health

Pediatric Learning Health Networks in Solid Organ Transplantation-Engaging all Stakeholders to Achieve Health for Children Who Require Transplantation

BACKGROUND

Learning Health Networks (LHN) have evolved within medicine over the past two decades, but their integration into transplantation has been more recent.

OBJECTIVES AND METHODS

In this paper, we describe three LHNs in end-stage organ disease/transplantation, their common and unique features, and how their "actor-oriented" architecture allowed for rapid adaptation to meet the needs of their patients and practitioners during the recent COVID-19 pandemic.

RESULT

The structure and focus of the Improving Renal Outcomes Collaborative (IROC), Starzl Network for Excellence in Pediatric Transplantation (SNEPT), and the Advanced Cardiac Therapies Improving Outcomes Network (ACTION) are reviewed. We discuss the critical role of patient and family engagement, focusing on collaboration with Transplant Families. Finally, we review challenges common to the LHN concept and potential common areas of alignment to achieve the goal of more rapid and sustained progress to improve health in pediatric transplantation.

CONCLUSION

LHN in transplantation are essential to accelerate knowledge dissemination and improve outcomes.

Twelve tips for creating a longitudinal quality improvement and safety education for early health professions students

Despite the numerous calls for integrating quality improvement and patient safety (QIPS) curricula into health professions education, there are limited examples of effective implementation for early learners. Typically, pre-clinical QIPS experiences involve lectures or lessons that are disconnected from the practice of medicine. Consequently, students often prioritize other content they consider more important. As a result, they may enter clinical settings without essential QIPS skills and struggle to incorporate these concepts into their early professional identity formation. In this paper, we present twelve tips aimed at assisting educators in developing QIPS education early in the curricula of health professions students. These tips address various key issues, including aligning incentives, providing longitudinal experiences, incorporating real-world care outcomes, optimizing learning environments, communicating successes, and continually enhancing education and care delivery processes.

Beyond Human Limits: Harnessing Artificial Intelligence to Optimize Immunosuppression in Kidney Transplantation

The field of kidney transplantation is being revolutionized by the integration of artificial intelligence (AI) and machine learning (ML) techniques. AI equips machines with human-like cognitive abilities, while ML enables computers to learn from data. Challenges in transplantation, such as organ allocation and prediction of allograft function or rejection, can be addressed through AI-powered algorithms. These algorithms can optimize immunosuppression protocols and improve patient care. This comprehensive literature review provides an overview of all the recent studies on the utilization of AI and ML techniques in the optimization of immunosuppression in kidney transplantation. By developing personalized and data-driven immunosuppression protocols, clinicians can make informed decisions and enhance patient care. However, there are limitations, such as data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Future research should validate and refine AI models for different populations and treatment durations. AI and ML have the potential to revolutionize kidney transplantation by optimizing immunosuppression and improving outcomes. AI-powered algorithms enable personalized and data-driven immunosuppression protocols, enhancing patient care and decision-making. Limitations include data quality, small sample sizes, validation, computational complexity, and interpretability of ML models. Further research is needed to validate and enhance AI models for different populations and longer-term dosing decisions.