Training the next generation of learning health system scientists.

Introduction: The learning health system (LHS) aligns science, informatics, incentives, stakeholders, and culture for continuous improvement and innovation. The Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute designed a K12 initiative to grow the number of LHS scientists. We describe approaches developed by 11 funded centers of excellence (COEs) to promote partnerships between scholars and health system leaders and to provide mentored research training. Methods: Since 2018, the COEs have enlisted faculty, secured institutional resources, partnered with health systems, developed and implemented curricula, recruited scholars, and provided mentored training. Program directors for each COE provided descriptive data on program context, scholar characteristics, stakeholder engagement, scholar experiences with health system partnerships, roles following program completion, and key training challenges. Results: To date, the 11 COEs have partnered with health systems to train 110 scholars. Nine (82%) programs partner with a Veterans Affairs health system and 9 (82%) partner with safety net providers. Clinically trained scholars (n = 87; 79%) include 70 physicians and 17 scholars in other clinical disciplines. Non-clinicians (n = 29; 26%) represent diverse fields, dominated by population health sciences. Stakeholder engagement helps scholars understand health system and patient/family needs and priorities, enabling opportunities to conduct embedded research, improve outcomes, and grow skills in translating research methods and findings into practice. Challenges include supporting scholars through roadblocks that threaten to derail projects during their limited program time, ranging from delays in access to data to COVID-19-related impediments and shifts in organizational priorities. Conclusions: Four years into this novel training program, there is evidence of scholars' accomplishments, both in traditional academic terms and in terms of moving along career trajectories that hold the potential to lead and accelerate transformational health system change. Future LHS training efforts should focus on sustainability, including organizational support for scholar activities.

Everybody's talking about equity, but is anyone really listening?: The case for better data-driven learning in health systems

Data collection, analysis, and data driven action cycles have been viewed as vital components of healthcare for decades. Throughout the COVID-19 pandemic, case incidence and mortality data have consistently been used by various levels of governments and health institutions to inform pandemic strategies and service distribution. However, these responses are often inequitable, underscoring pre-existing healthcare disparities faced by marginalized populations. This has prompted governments to finally face these disparities and find ways to quickly deliver more equitable pandemic support. These rapid data informed supports proved that learning health systems (LHS) could be quickly mobilized and effectively used to develop healthcare actions that delivered healthcare interventions that matched diverse populations' needs in equitable and affordable ways. Within LHS, data are viewed as a starting point researchers can use to inform practice and subsequent research. Despite this innovative approach, the quality and depth of data collection and robust analyses varies throughout healthcare, with data lacking across the quadruple aims. Often, large data gaps pertaining to community socio-demographics, patient perceptions of healthcare quality and the social determinants of health exist. This prevents a robust understanding of the healthcare landscape, leaving marginalized populations uncounted and at the sidelines of improvement efforts. These gaps are often viewed by researchers as indication that more data is needed rather than an opportunity to critically analyze and iteratively learn from multiple sources of pre-existing data. This continued cycle of data collection and analysis leaves one to wonder if healthcare has a data problem or a learning problem. In this commentary, we discuss ways healthcare data are often used and how LHS disrupts this cycle, turning data into learning opportunities that inform healthcare practice and future research in real time. We conclude by proposing several ways to make learning from data just as important as the data itself. © The Authors.

Interventions for Maintenance of Essential Health Service Delivery during the COVID-19 Response in Uganda, between March 2020 and April 2021.

INTRODUCTION: The COVID-19 pandemic overwhelmed health systems globally and affected the delivery of health services. We conducted a study in Uganda to describe the interventions adopted to maintain the delivery of other health services. METHODS: We reviewed documents and interviewed 21 key informants. Thematic analysis was conducted to identify themes using the World Health Organization health system building blocks as a guiding framework. RESULTS: Governance strategies included the establishment of coordination committees and the development and dissemination of guidelines. Infrastructure and commodity strategies included the review of drug supply plans and allowing emergency orders. Workforce strategies included the provision of infection prevention and control equipment, recruitment and provision of incentives. Service delivery modifications included the designation of facilities for COVID-19 management, patient self-management, dispensing drugs for longer periods and the leveraging community patient networks to distribute medicines. However, multi-month drug dispensing led to drug stock-outs while community drug distribution was associated with stigma. CONCLUSIONS: Health service maintenance during emergencies requires coordination to harness existing health system investments. The essential services continuity committee coordinated efforts to maintain services and should remain a critical element of emergency response. Self-management and leveraging patient networks should address stigma to support service continuity in similar settings and strengthen service delivery beyond the pandemic.

Evaluating clinician-led quality improvement initiatives: A system-wide embedded research partnership at Stanford Medicine.

Introduction: Many healthcare delivery systems have developed clinician-led quality improvement (QI) initiatives but fewer have also developed in-house evaluation units. Engagement between the two entities creates unique opportunities. Stanford Medicine funded a collaboration between their Improvement Capability Development Program (ICDP), which coordinates and incentivizes clinician-led QI efforts, and the Evaluation Sciences Unit (ESU), a multidisciplinary group of embedded researchers with expertise in implementation and evaluation sciences. Aim: To describe the ICDP-ESU partnership and report key learnings from the first 2 y of operation September 2019 to August 2021. Methods: Department-level physician and operational QI leaders were offered an ESU consultation to workshop design, methods, and overall scope of their annual QI projects. A steering committee of high-level stakeholders from operational, clinical, and research perspectives subsequently selected three projects for in-depth partnered evaluation with the ESU based on evaluability, importance to the health system, and broader relevance. Selected project teams met regularly with the ESU to develop mixed methods evaluations informed by relevant implementation science frameworks, while aligning the evaluation approach with the clinical teams' QI goals. Results: Sixty and 62 ICDP projects were initiated during the 2 cycles, respectively, across 18 departments, of which ESU consulted with 15 (83%). Within each annual cycle, evaluators made actionable, summative findings rapidly available to partners to inform ongoing improvement. Other reported benefits of the partnership included rapid adaptation to COVID-19 needs, expanded clinician evaluation skills, external knowledge dissemination through scholarship, and health system-wide knowledge exchange. Ongoing considerations for improving the collaboration included the need for multi-year support to enable nimble response to dynamic health system needs and timely data access. Conclusion: Presence of embedded evaluation partners in the enterprise-wide QI program supported identification of analogous endeavors (eg, telemedicine adoption) and cross-cutting lessons across QI efforts, clinician capacity building, and knowledge dissemination through scholarship.

Collaborative writing applications in support of knowledge translation and management during pandemics: A scoping review.

OBJECTIVES: This study aimed to: (1) Map existing evidence about the use of collaborative writing applications (CWAs) during pandemics; (2) Describe CWAs' positive and negative effects on knowledge translation (KT) and knowledge management (KM) during pandemics; and (3) Inventory the barriers and facilitators that affect CWAs' use to support KT and KM during pandemics. MATERIALS AND METHODS: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews, we conducted a scoping review of the literature reporting the use of CWAs during pandemics published between 2001 and 2021. Two reviewers undertook the screening, study selection and qualitative thematic analysis. RESULTS: We identified a total of 46 studies. CWAs were used for the following two purposes: KT and KM (23 of 46) anddisease surveillance and infodemiology (20 of 46). Three studies addressed both purposes. Influenza was the focus of most studies (15 of 46), followed by COVID-19 (10 of 46).We identified and classified 24 barriers and 66 facilitators into four categories (factors related to the CWAs, users' knowledge and attitude towards CWAs, human environment, and organizational environment). We also found 74 positive and 7 negative effects that were classified into processes and outcomes. CONCLUSION: CWAs offer the potential to accelerate KT and KM during pandemics. Their scalability and adaptability to different contexts makes them well suited to support the urgent KT and KM needed in the context of rapidly changing knowledge during pandemics. While their speed and cost as disease surveillance systems compare favorably with existing surveillance systems, the primary challenge is to ensure the accuracy of information shared.

Learning health systems: A review of key topic areas and bibliometric trends.

INTRODUCTION: The emergent field of learning health systems (LHSs) has been rapidly evolving as the concept continues to be embraced by researchers, managers, and clinicians. This paper reports on a scoping review and bibliometric analysis of the LHS literature to identify key topic areas and examine the influence and spread of recent research. METHODS: We conducted a scoping review of LHS literature published between January 2016 and May 2020. The authors extracted publication data (eg, journal, country, authors, citation count, keywords) and reviewed full-texts to identify: type of study (empirical, non-empirical, or review), degree of focus (general or specific), and the reference used when defining LHSs. RESULTS: A total of 272 publications were included in this review. Almost two thirds (65.1%) of the included articles were non-empirical and over two-thirds (68.4%) were from authors in the United States. More than half of the publications focused on specific areas, for example: oncology, cardiovascular care, and genomic medicine. Other key topic areas included: ethics, research, quality improvement, and electronic health records. We identified that definitions of the LHS concept are converging; however, many papers focused on data platforms and analytical processes rather than organisational and behavioural factors to support change and learning activities. CONCLUSIONS: The literature on LHSs remains largely theoretical with definitions of LHSs focusing on technical processes to reuse data collected during the clinical process and embedding analysed data back into the system. A shift in the literature to empirical LHS studies with consideration of organisational and human factors is warranted.

Cancer prevention, risk reduction, and control: opportunities for the next decade of health care delivery research.

In this commentary, we discuss opportunities to optimize cancer care delivery in the next decade building from evidence and advancements in the conceptualization and implementation of multi-level translational behavioral interventions. We summarize critical issues and discoveries describing new directions for translational behavioral research in the coming decade based on the promise of the accelerated application of this evidence within learning health systems. To illustrate these advances, we discuss cancer prevention, risk reduction (particularly precision prevention and early detection), and cancer treatment and survivorship (particularly risk- and need-stratified comprehensive care) and propose opportunities to equitably improve outcomes while addressing clinician shortages and cross-system coordination. We also discuss the impacts of COVID-19 and potential advances of scientific knowledge in the context of existing evidence, the need for adaptation, and potential areas of innovation to meet the needs of converging crises (e.g., fragmented care, workforce shortages, ongoing pandemic) in cancer health care delivery. Finally, we discuss new areas for exploration by applying key lessons gleaned from implementation efforts guided by advances in behavioral health.

Learning health systems: Driving real-world impact in mental health and substance use disorder research.

The Veterans Health Administration (VHA), under the U.S. Department of Veterans Affairs (VA), is one of the largest single providers of health care in the U.S. VA supports an embedded research program that addresses VA clinical priorities in close partnership with operations leaders, which is a hallmark of a Learning Health System (LHS). Using the LHS framework, we describe current VA research initiatives in mental health and substance use disorders that rigorously evaluate national programs and policies designed to reduce the risk of suicide and opioid use disorder (data to knowledge); test implementation strategies to improve the spread of effective programs for Veterans at risk of suicide or opioid use disorder (knowledge to performance); and identify novel research directions in suicide prevention and opioid/pain treatments emanating from implementation and quality improvement research (performance to data). Lessons learned are encapsulated into best practices for building and sustaining an LHS within health systems, including the need for early engagement with clinical leaders; pragmatic research questions that focus on continuous improvement; multi-level, ongoing input from regional and local stakeholders, and business case analyses to inform ongoing investment in sustainable infrastructure to maintain the research-health system partnership. Essential ingredients for supporting VA as an LHS include data and information sharing capacity, protected time for researchers and leaders, and governance structures to enhance health system ownership of research findings. For researchers, incentives to work with health systems operations (e.g., retainer funding) are vital for LHS research to be recognized and valued by academic promotion committees.

Why We Are Losing the War Against COVID-19 on the Data Front and How to Reverse the Situation.

With over 117 million COVID-19-positive cases declared and the death count approaching 3 million, we would expect that the highly digitalized health systems of high-income countries would have collected, processed, and analyzed large quantities of clinical data from patients with COVID-19. Those data should have served to answer important clinical questions such as: what are the risk factors for becoming infected? What are good clinical variables to predict prognosis? What kinds of patients are more likely to survive mechanical ventilation? Are there clinical subphenotypes of the disease? All these, and many more, are crucial questions to improve our clinical strategies against the epidemic and save as many lives as possible. One might assume that in the era of big data and machine learning, there would be an army of scientists crunching petabytes of clinical data to answer these questions. However, nothing could be further from the truth. Our health systems have proven to be completely unprepared to generate, in a timely manner, a flow of clinical data that could feed these analyses. Despite gigabytes of data being generated every day, the vast quantity is locked in secure hospital data servers and is not being made available for analysis. Routinely collected clinical data are, by and large, regarded as a tool to inform decisions about individual patients, and not as a key resource to answer clinical questions through statistical analysis. The initiatives to extract COVID-19 clinical data are often promoted by private groups of individuals and not by health systems, and are uncoordinated and inefficient. The consequence is that we have more clinical data on COVID-19 than on any other epidemic in history, but we have failed to analyze this information quickly enough to make a difference. In this viewpoint, we expose this situation and suggest concrete ideas that health systems could implement to dynamically analyze their routine clinical data, becoming learning health systems and reversing the current situation.

The influence of the learning health system to address the COVID-19 pandemic: An examination of early literature.

INTRODUCTION: The COVID-19 pandemic has demanded immediate response from healthcare systems around the world. The learning health system (LHS) was created with rapid uptake of the newest evidence in mind, making it essential in the face of a pandemic. The goal of this review is to gain knowledge on the initial impact of the LHS on addressing the COVID-19 pandemic. METHODS: PubMed, Scopus and the Duke University library search tool were used to identify current literature regarding the intersection of the LHS and the COIVD-19 pandemic. Articles were reviewed for their purpose, findings and relation to each component of the LHS. RESULTS: Twelve articles were included in the review. All stages of the LHS were addressed from this sample. Most articles addressed some component of interoperability. Articles that interpreted data unique to COVID-19 and demonstrated specific tools and interventions were least common. CONCLUSIONS: Gaps in interoperability are well known and unlikely to be solved in the coming months. Collaboration between health systems, researchers, governments and professional societies is needed to support a robust LHS which grants the ability to rapidly adapt to global emergencies.