Conceptualizing and redefining successful patient engagement in patient advisory councils in learning health networks

Using a collaborative learning health system approach to improve disease activity outcomes in children with juvenile idiopathic arthritis in the Pediatric Rheumatology Care and Outcomes Improvement Network

Introduction

The Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN) is a North American learning health network focused on improving outcomes of children with juvenile idiopathic arthritis (JIA). JIA is a chronic autoimmune disease that can lead to morbidity related to persistent joint and ocular inflammation. PR-COIN has a shared patient registry that tracks twenty quality measures including ten outcome measures of which six are related to disease activity. The network's global aim, set in 2021, was to increase the percent of patients with oligoarticular or polyarticular JIA that had an inactive or low disease activity state from 76% to 80% by the end of 2023.

Methods

Twenty-three hospitals participate in PR-COIN, with over 7,200 active patients with JIA. The disease activity outcome measures include active joint count, physician global assessment of disease activity, and measures related to validated composite disease activity scoring systems including inactive or low disease activity by the 10-joint clinical Juvenile Arthritis Disease Activity Score (cJADAS10), inactive or low disease activity by cJADAS10 at 6 months post-diagnosis, mean cJADAS10 score, and the American College of Rheumatology (ACR) provisional criteria for clinical inactive disease. Data is collated to measure network performance, which is displayed on run and control charts. Network-wide interventions have included pre-visit planning, shared decision making, self-management support, population health management, and utilizing a Treat to Target approach to care.

Results

Five outcome measures related to disease activity have demonstrated significant improvement over time. The percent of patients with inactive or low disease activity by cJADAS10 surpassed our goal with current network performance at 81%. Clinical inactive disease by ACR provisional criteria improved from 46% to 60%. The mean cJADAS10 score decreased from 4.3 to 2.6, and the mean active joint count declined from 1.5 to 0.7. Mean physician global assessment of disease activity significantly improved from 1 to 0.6.

Conclusions

PR-COIN has shown significant improvement in disease activity metrics for patients with JIA. The network will continue to work on both site-specific and collaborative efforts to improve outcomes for children with JIA with attention to health equity, severity adjustment, and data quality.

Cultural health capital and patient partner recruitment into healthcare improvement work

A rising emphasis on patient involvement in clinical research and healthcare improvement has led to the steady incorporation of patients and caregivers into this work. However, interactional factors shaping recruitment processes are not well understood. In this paper, we present a qualitative analysis of interviews with twenty-six patients, family members, engagement staff and healthcare providers who are engaged in healthcare improvement work in the United States. We focus on how stakeholders account for recruitment decisions to participate in healthcare improvement work. We find that expressions of and judgments about patients' and caregivers' cultural health capital shape providers' decisions to extend invitations to participate in healthcare improvement work. These findings extend current conceptualizations of cultural health capital beyond the clinical encounter to reveal factors shaping patient recruitment into healthcare improvement work. In theorizing how cultural health capital shapes action in this new setting, we found that healthcare providers, engagement staff, and patients/caregivers attended to different aspects of cultural health capital when accounting for why they extended or received a recruitment pitch. We further found that participating in healthcare improvement work led to a boost in cultural health capital for patients and caregivers, which they could use to develop transmissible forms of cultural health capital for less centrally involved patients and caregivers. Finally, we describe how participants in healthcare improvement collaboratives account for a lack of diversity among partners. These findings help us hypothesize the consequences of recruitment processes that rely on displays and judgments of cultural health capital and identify possibilities for change. Using the case of healthcare improvement work in Collaborative Learning Health Systems, our findings advance past work on cultural health capital in medical sociology by theorizing the role of cultural health capital in recruitment processes.

A holistic approach to integrating patient, family, and lived experience voices in the development of the BrainHealth Databank: a digital learning health system to enable artificial intelligence in the clinic

Artificial intelligence, machine learning, and digital health innovations have tremendous potential to advance patient-centred, data-driven mental healthcare. To enable the clinical application of such innovations, the Krembil Centre for Neuroinformatics at the Centre for Addiction and Mental Health, Canada's largest mental health hospital, embarked on a journey to co-create a digital learning health system called the BrainHealth Databank (BHDB). Working with clinicians, scientists, and administrators alongside patients, families, and persons with lived experience (PFLE), this hospital-wide team has adopted a systems approach that integrates clinical and research data and practices to improve care and accelerate research. PFLE engagement was intentional and initiated at the conception stage of the BHDB to help ensure the initiative would achieve its goal of understanding the community's needs while improving patient care and experience. The BHDB team implemented an evolving, dynamic strategy to support continuous and active PFLE engagement in all aspects of the BHDB that has and will continue to impact patients and families directly. We describe PFLE consultation, co-design, and partnership in various BHDB activities and projects. In all three examples, we discuss the factors contributing to successful PFLE engagement, share lessons learned, and highlight areas for growth and improvement. By sharing how the BHDB navigated and fostered PFLE engagement, we hope to motivate and inspire the health informatics community to collectively chart their paths in PFLE engagement to support advancements in digital health and artificial intelligence.