Training the next generation of learning health system scientists

The Case for Assessing the Drivers of Measles Vaccine Uptake

Global measles cases are on the rise following disruptions to routine immunisation programs during the COVID-19 pandemic, with devastating consequences. According to the World Health Organization, the behavioural and social drivers of vaccination include what people think and feel about vaccines, social processes, motivation to vaccinate and practical barriers to vaccination. However, the drivers of measles vaccine uptake are not necessarily the same as those for other childhood vaccines, and we lack data on how these drivers specifically have changed during and since the COVID-19 pandemic. Without accurately measuring the behavioural and social drivers for measles vaccination, and ideally measuring them serially over time, countries cannot design, target and implement interventions that effectively increase and sustain measles vaccine coverage. This paper outlines what is and is not known about the behavioural and social drivers of measles vaccination and provides recommendations for improving their post-pandemic assessment.

Understanding the training, mentorship, and professional development priorities of early career embedded researchers

INTRODUCTION

Health systems are constantly evolving in response to existing and emerging health challenges and are increasingly adopting the Quintuple Aim to guide transformation and improvement efforts. Addressing health challenges and achieving the Quintuple Aim (enhancing patient experience, improving healthcare provider experience, promoting population health, optimising the value of healthcare services, and advancing health equity) may be enhanced with the use of a Learning Health Systems approach that fosters the real-time use of data and evidence to inform improvement efforts and harnesses embedded researchers to co-produce timely, relevant evidence to address priorities. Training programs have emerged to build embedded research capacity within health system organisations and have focused predominantly on the postdoctoral career stage, with little attention paid to the early career researcher (ECR) stage. The objective of this study was to understand ECR training and mentorship needs in the embedded research context to inform the creation new or adaptation of existing programs to build embedded ECR capacity.

METHODS

This study used a qualitative approach to garner insight from embedded and applied scholars and health systems leaders in Canada from various professional backgrounds and at various career stages using a combination of focus group discussions, key informant interviews, and an online survey. Thematic content analysis was used to examine the responses of study participants within the interview themes.

RESULTS

Twenty-six (26) participants were included in the study. Results were organised according to four key themes: (1) key competencies and skills needed by embedded ECRs; (2) additional training and capacity development needs; (3) training delivery approaches; and (4) enablers and challenges faced by embedded ECRs. Results highlight the importance of supporting ECRs to develop their leadership and organisational management capabilities; their knowledge of and ability to use research approaches that are well-suited to real-world, complex, evolving environments; and their opportunities to learn with and from each other and mentors. Results underscore the perceived importance of context, including being embedded in a supportive environment that values research and evidence and of academic incentives that recognise and value real-world research impact. The challenges of responding to shifting organisational and system priorities were identified. Additional insights from health systems leaders were also highlighted.

CONCLUSION

This study identified the multifaceted needs of embedded ECRs and the challenges they face within healthcare systems. Designing new programs or tailoring existing ones to address these needs would build their capacity, foster career progression, and ensure their impact as leaders of evidence-informed health system improvement which is crucial for achieving the Quintuple Aim.

Engaging Interdisciplinary Innovation Teams in Federally Qualified Health Centers

To foster bottom-up innovations, health care organizations are leveraging interdisciplinary frontline innovation teams. These teams include workers across hierarchical levels and professional backgrounds, pooling diverse knowledge sources to develop innovations that improve patient and worker experiences and care quality, equity, and costs. Yet, these frontline innovation teams experience barriers, such as time constraints, being new to innovation, and team-based role hierarchies. We investigated the practices that such teams in federally qualified health centers (FQHCs) used to overcome these barriers. Our 20-month study of two FQHC innovation teams provides one of the first accounts of how practices that sustained worker engagement in innovation and supported their ideas to implementation evolve over time. We also show the varied quantity of engagement practices used at different stages of the innovation process. At a time when FQHCs face pressure to innovate amid staff shortages, our study provides recommendations to support their work.

Diffusion of excellence: evaluating a system to identify, replicate, and spread promising innovative practices across the Veterans health administration

Introduction

The Veterans Health Administration (VHA) Diffusion of Excellence (DoE) program provides a system to identify, replicate, and spread promising practices across the largest integrated healthcare system in the United States. DoE identifies innovations that have been successfully implemented in the VHA through a Shark Tank style competition. VHA facility and regional directors bid resources needed to replicate promising practices. Winning facilities/regions receive external facilitation to aid in replication/implementation over the course of a year. DoE staff then support diffusion of successful practices across the nationwide VHA.

Methods

Organized around the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) Framework, we summarize results of an ongoing long-term mixed-methods implementation evaluation of DoE. Data sources include: Shark Tank application and bid details, tracking practice adoptions through a Diffusion Marketplace, characteristics of VHA facilities, focus groups with Shark Tank bidders, structured observations of DoE events, surveys of DoE program participants, and semi-structured interviews of national VHA program office leaders, VHA healthcare system/facility executives, practice developers, implementation teams and facilitators.

Results

In the first eight Shark Tanks (2016-2022), 3,280 Shark Tank applications were submitted; 88 were designated DoE Promising Practices (i.e., practices receive facilitated replication). DoE has effectively spread practices across the VHA, with 1,440 documented instances of adoption/replication of practices across the VHA. This includes 180 adoptions/replications in facilities located in rural areas. Leadership decisions to adopt innovations are often based on big picture considerations such as constituency support and linkage to organizational goals. DoE Promising Practices that have the greatest national spread have been successfully replicated at new sites during the facilitated replication process, have close partnerships with VHA national program offices, and tend to be less expensive to implement. Two indicators of sustainment indicate that 56 of the 88 Promising Practices are still being diffused across the VHA; 56% of facilities originally replicating the practices have sustained them, even up to 6 years after the first Shark Tank.

Conclusion

DoE has developed a sustainable process for the identification, replication, and spread of promising practices as part of a learning health system committed to providing equitable access to high quality care.

Advancing healthcare equity through dissemination and implementation science

OBJECTIVE

To provide guiding principles and recommendations for how approaches from the field of dissemination and implementation (D&I) science can advance healthcare equity.

DATA SOURCES AND STUDY SETTING

This article, part of a special issue sponsored by the Agency for Healthcare Research and Quality (AHRQ), is based on an outline drafted to support proceedings of the 2022 AHRQ Health Equity Summit and further revised to reflect input from Summit attendees.

STUDY DESIGN

This is a narrative review of the current and potential applications of D&I approaches for understanding and advancing healthcare equity, followed by discussion and feedback with Summit attendees.

DATA COLLECTION/EXTRACTION METHODS

We identified major themes in narrative and systematic reviews related to D&I science, healthcare equity, and their intersections. Based on our expertise, and supported by synthesis of published studies, we propose recommendations for how D&I science is relevant for advancing healthcare equity. We used iterative discussions internally and at the Summit to refine preliminary findings and recommendations.

PRINCIPAL FINDINGS

We identified four guiding principles and three D&I science domains with strong promise for accelerating progress toward healthcare equity. We present eight recommendations and more than 60 opportunities for action by practitioners, healthcare leaders, policy makers, and researchers.

CONCLUSIONS

Promising areas for D&I science to impact healthcare equity include the following: attention to equity in the development and delivery of evidence-based interventions; the science of adaptation; de-implementation of low-value care; monitoring equity markers; organizational policies for healthcare equity; improving the economic evaluation of implementation; policy and dissemination research; and capacity building.

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.

Commentary: Achieving Health Equity - The Role of Learning Health Systems

Achieving health equity, for decades a domain of high-performing health systems, has been elevated to a priority and recognized as a central objective of health system transformation and quality improvement efforts. By prioritizing health equity; developing, implementing and evaluating models of care that optimize individual and population health; developing strong partnerships with patients and communities; conducting research to generate evidence on the effectiveness of interventions across diverse populations; implementing strategies to integrate clinical care, public health and social care; and participating in multisector collaborations to address social needs, learning health systems can play a pivotal role in eliminating health inequities.

Development and use of a novel tool for assessing and improving researcher embeddedness in learning health systems and applied system improvements

This paper outlines the development, deployment and use, and testing of a tool for measuring and improving healthcare researcher embeddedness - i.e., being connected to and engaged with key leverage points and stakeholders in a health system. Despite the widely acknowledged importance of embeddedness for learning health systems and late-stage translational research, we were not aware of useful tools for addressing and improving embeddedness in scholar training programs. We developed the MN-LHS Embeddedness Tool covering connections to committees, working groups, leadership, and other points of contact across four domains: patients and caregivers; local practice (e.g., operations and workflows); local institutional research (e.g., research committees and agenda- or initiative-setting groups); and national (strategic connections within professional groups, conferences, etc.). We used qualitative patterns and narrative findings from 11 learning health system training program scholars to explore variation in scholar trajectories and the embeddedness tool's usefulness in scholar professional development. Tool characteristics showed moderate evidence of construct validity; secondarily, we found significant differences in embeddedness, as a score, from baseline through program completion. The tool has demonstrated simple, practical utility in making embeddedness an explicit (rather than hidden) part of applied and learning health system researcher training, alongside emerging evidence for validity.