Building a learning health care community in rural and remote areas: a systematic review.

BACKGROUND: A Learning Health Care Community (LHCC) is a framework to enhance health care through mutual accountability between the health care system and the community. LHCC components include infrastructure for health-related data capture, care improvement targets, a supportive policy environment, and community engagement. The LHCC involves health care providers, researchers, decision-makers, and community members who work to identify health care needs and address them with evidence-based solutions. The objective of this study was to summarize the barriers and enablers to building an LHCC in rural areas. METHODS: A systematic review was conducted by searching electronic databases. Eligibility criteria was determined by the research team. Published literature on LHCCs in rural areas was systematically collected and organized. Screening was completed independently by two authors. Detailed information about rural health care, activities, and barriers and enablers to building an LHCC in rural areas was extracted. Qualitative analysis was used to identify core themes. RESULTS: Among 8169 identified articles, 25 were eligible. LHCCs aimed to increase collaboration and co-learning between community members and health care providers, integrate community feedback in health care services, and to share information. Main barriers included obtaining adequate funding and participant recruitment. Enablers included meaningful engagement of stakeholders and stakeholder collaboration. CONCLUSIONS: The LHCC is built on a foundation of meaningful use of health data and empowers health care practitioners and community members in informed decision-making. By reducing the gap between knowledge generation and its application to practice, the LHCC has the potential to transform health care delivery in rural areas.

Learning health systems on the front lines to strengthen care against future pandemics and climate change: a rapid review.

BACKGROUND: An essential component of future-proofing health systems against future pandemics and climate change is strengthening the front lines of care: principally, emergency departments and primary care settings. To achieve this, these settings can adopt learning health system (LHS) principles, integrating data, evidence, and experience to continuously improve care delivery. This rapid review aimed to understand the ways in which LHS principles have been applied to primary care and emergency departments, the extent to which LHS approaches have been adopted in these key settings, and the factors that affect their adoption. METHODS: Three academic databases (Embase, Scopus, and PubMed) were searched for full text articles reporting on LHSs in primary care and/or emergency departments published in the last five years. Articles were included if they had a primary focus on LHSs in primary care settings (general practice, allied health, multidisciplinary primary care, and community-based care) and/or emergency care settings. Data from included articles were catalogued and synthesised according to the modified Institute of Medicine's five-component framework for LHSs (science and informatics, patient-clinician partnerships, incentives, continuous learning culture, and structure and governance). RESULTS: Thirty-seven articles were included, 32 of which reported LHSs in primary care settings and seven of which reported LHSs in emergency departments. Science and informatics was the most commonly reported LHS component, followed closely by continuous learning culture and structure and governance. Most articles (n = 30) reported on LHSs that had been adopted, and many of the included articles (n = 17) were descriptive reports of LHS approaches. CONCLUSIONS: Developing LHSs at the front lines of care is essential for future-proofing against current and new threats to health system sustainability, such as pandemic- and climate change-induced events. Limited research has examined the application of LHS concepts to emergency care settings. Implementation science should be utilised to better understand the factors influencing adoption of LHS approaches on the front lines of care, so that all five LHS components can be progressed in these settings.

Learning networks in pediatric heart failure and transplantation.

BACKGROUND: Learning networks have emerged in medicine as a novel organizational structure that contains elements of quality improvement, education, and research with the goal of effecting rapid improvements in clinical care. In this article, the concept of a learning network is defined and highlighted in the field of pediatric heart failure and transplantation. METHODS: Learning networks are defined, with particular attention paid to the recent creation of the Advanced Cardiac Therapies Improving Outcomes Network (ACTION) for children with heart failure and those being supported with ventricular assist devices (VAD). RESULTS: The mission, goals, and organizational structure of ACTION are described, and recent initiatives promoted by ACTION are highlighted, such as stroke reduction initiatives, practice harmonization protocols, and use of ACTION data to support the recent US Food and Drug Administration approval of newer VAD for pediatric patients. CONCLUSIONS: The learning network, exemplified by ACTION, is distinguished from traditional clinical research collaboratives by contributions in research, quality improvement, patient-reported outcomes, and education, and serves as an effective vehicle to drive clinical improvement in the care of children with advanced heart failure.

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.

The Role of a Resilient Information Infrastructure in COVID-19 Vaccine Uptake in Ontario.

The COVID-19 pandemic has highlighted the need for a robust and nimble public health data infrastructure. ICES - a government-sponsored, independent, non-profit research institute in Ontario, Canada - functions as a key component of a resilient information infrastructure and an enabler of data co-production, contributing to Ontario's response to the COVID-19 pandemic as part of a learning health system. Linked data on the cumulative incidence of infection and vaccination at the neighbourhood level revealed disparate uptake between areas with low versus high risk of COVID-19. These data were leveraged by the government, service providers, media and the public to inform a more efficient and equitable vaccination strategy.

E-SCOPE: A Strategic Approach to Identify and Accelerate Implementation of Evidence-based Best Practices.

BACKGROUND: More than 50,000 randomized controlled trials and 8000 systematic reviews are anticipated to be published annually in the coming years. This huge volume of published findings makes it challenging for health care delivery systems to review new evidence, prioritize health care practices that warrant implementation, and implement best practices. OBJECTIVE: The objective of this study was to describe the Kaiser Permanente Southern California E-SCOPE (Evidence Scanning for Clinical, Operational, and Practice Efficiencies) program, a systematic method to accelerate the implementation of evidence-based practices in clinical care settings. METHODS: E-SCOPE uses a strategic evidence search algorithm to conduct proactive literature searches to identify high-quality studies of interventions that yield improved health outcomes, quality and/or efficiency of care delivery, or cost savings. Each quarterly search yields 500-1000 abstracts; about 5%-10% of studies are selected each quarter for consideration for implementation. These studies are presented to clinical and operational leaders and other stakeholders to make the final determination regarding the implementation of the practice; E-SCOPE staff work closely with stakeholders to develop an implementation plan, identify practice owners, and ensure sustainability. RESULTS: The time from study publication to implementation using the E-SCOPE process ranges from 4 to 36 months, with an average of ∼16 months. Four examples of E-SCOPE implementation efforts, including new deployment, scale-up/spread, deimplementation, and operational efforts, are described. CONCLUSION: A single, centralized program for the proactive identification of the most up-to-date, evidence-based best practices and facilitated implementation can efficiently and effectively promote continuous learning and implementation in a learning health care system.

"Implementation Is so Difficult": Survey of National Learning Health System Decision-makers Identifies Need For Implementation Information in Evidence Reviews.

BACKGROUND: Evidence use within learning health care systems can improve patient health outcomes. Embedded in the Veterans Health Administration (VHA) since 2007, the Veterans Affairs Evidence Synthesis Program (ESP) provides tailored evidence synthesis services to support VHA's learning health care system goals. As part of the ESP's ongoing quality improvement efforts, we have been surveying our users since 2016. METHODS: We reviewed data from a survey of end-users received between September 5, 2016, and June 10, 2019. The survey assessed: (1) nature of decision-making needs; (2) actions resulting from the report's findings; (3) implementation timeframe; and (4) overall perception of report content. RESULTS: Feedback was received from 66 of the 138 operational partners requesting ESP products during the fiscal year 2015 through 2018. Requests commonly informed clinical guidance (58%), identified future research needs (58%), and determined VHA-specific implementation strategy (47%). A total of 91% of responders used reports, typically within 3 months after completion (82%). Use was typically for VHA publications and/or presentations to inform VHA policy or guidance (26%), to inform intervention/strategy adoption decisions (23%) and for medical device and therapy procurement decisions (21%). Over half (53%) of respondents indicated that it would be useful for ESP reports to include more guidance on implementing findings. CONCLUSION: Our survey of learning health system decision-makers' actual patterns and timing of evidence use provides valuable new information that can further support development of other health system and evidence producer partnerships and identifies key needs for better supporting health systems' uptake of evidence.

Opportunities and challenges in Taiwan for implementing the learning health system.

Due to the low ratio of medical decisions made upon solid scientific evidence (4%) and the low efficiency of deploying knowledge in practice (17 years), the concept of a learning health system (LHS) was initiated to speed up knowledge generation and adoption and systematically approach continuous improvement in clinical practice. This concept can be illustrated by a so-called learning health cycle. This cycle, the first version as well as its variants, provides a framework for discussion on a common basis and has been well-accepted by the medical communities. Though the idea attracted major attention widely, very little has been done in way of actual adoption in real practices in the past 10 years. Nevertheless, as one of the pioneers in Taiwan, we have been involved in the effort to implement the LHS locally since 2016. In this article, we systematically summarize the evolution of the learning health cycle, review cases of its applications and briefly introduce the work we have done for promoting LHSs in Taiwan. Based on the experience we have gained, we try to identify the challenges and opportunities in Taiwan. While full-scale electronic medical records powered by the National Health Insurance system give Taiwan a special advantage in achieving a nationwide LHS, the medical community is not yet ready for a dramatic change. The lack of infrastructure for this use and motivation to take action right away makes the implementation of a LHS in Taiwan challenging.

A framework for value-creating learning health systems.

BACKGROUND: Interest in value-based healthcare, generally defined as providing better care at lower cost, has grown worldwide, and learning health systems (LHSs) have been proposed as a key strategy for improving value in healthcare. LHSs are emerging around the world and aim to leverage advancements in science, technology and practice to improve health system performance at lower cost. However, there remains much uncertainty around the implementation of LHSs and the distinctive features of these systems. This paper presents a conceptual framework that has been developed in Canada to support the implementation of value-creating LHSs. METHODS: The framework was developed by an interdisciplinary team at the Institut national d'excellence en santé et en services sociaux (INESSS). It was informed by a scoping review of the scientific and grey literature on LHSs, regular team discussions over a 14-month period, and consultations with Canadian and international experts. RESULTS: The framework describes four elements that characterise LHSs, namely (1) core values, (2) pillars and accelerators, (3) processes and (4) outcomes. LHSs embody certain core values, including an emphasis on participatory leadership, inclusiveness, scientific rigour and person-centredness. In addition, values such as equity and solidarity should also guide LHSs and are particularly relevant in countries like Canada. LHS pillars are the infrastructure and resources supporting the LHS, whereas accelerators are those specific structures that enable more rapid learning and improvement. For LHSs to create value, such infrastructures must not only exist within the ecosystem but also be connected and aligned with the LHSs' strategic goals. These pillars support the execution, routinisation and acceleration of learning cycles, which are the fundamental processes of LHSs. The main outcome sought by executing learning cycles is the creation of value, which we define as the striking of a more optimal balance of impacts on patient and provider experience, population health and health system costs. CONCLUSIONS: Our framework illustrates how the distinctive structures, processes and outcomes of LHSs tie together with the aim of optimising health system performance and delivering greater value in health systems.

Funding Learning Health System Research: Challenges and Strategies.

PURPOSE: A growing number of health systems are establishing learning health system (LHS) programs, where research focuses on rapidly improving the health system's internal operations and performance. The authors examine funding challenges facing such initiatives and identify strategies for managing tensions between reliance on external research funding and directly contributing to improvement and learning within the researchers' own system. METHOD: Qualitative case studies of LHS research programs in 5 health systems were performed via 38 semistructured interviews (October 2019-April 2021) with 35 diverse respondents. Inductive and deductive rapid qualitative analysis supported interview, system-level, and cross-system summaries and analysis. RESULTS: External funding awards to LHS researchers facilitated some internal improvement and learning, scientific advancements, and the reputation of researchers and their systems, but reliance on external funding also challenged researchers' responsiveness to concerns of system leaders, managers, practitioners, and system needs. Gaps between external funding requirements and internally focused projects arose in objectives, practical applicability, audiences, timetables, routines, skill sets, and researchers' careers. To contribute more directly to system improvement, LHS researchers needed to collaborate with clinicians and other nonresearchers and pivot between long research studies and shorter, dynamic improvement, evaluation, and data analysis projects. With support from system executives, LHS program leaders employed several strategies to enhance researchers' internal contributions. They aligned funded-research topics with long-term system needs, obtained internal funding for implementing and sustaining practice change, and diversified funding sources. CONCLUSIONS: To foster LHS research contributions to internal system learning and improvement, LHS program leaders need to manage tensions between concentrating on externally funded research and fulfilling their mission of providing research-based services to their own system. Health system executives can support LHS programs by setting clear goals for them; appropriately staffing, budgeting, and incentivizing LHS researchers; and developing supportive, system-wide teamwork, skill development programs, and data infrastructures.

Implementation of Quality Improvement System in a National Health System.

There is a continued need to improve the quality of health care services and the health outcomes associated with that care. Quality improvement model and the Learning Health System framework have emerged as potential methods to achieve the goals of better care and enhanced outcomes. Few practical tools and approaches have been developed to assist with the initiation of quality improvement and development of a Learning Health System. The Department of Veterans Affairs developed and deployed a national system to assist and organize improvement projects and serve as a database for learning. During its first 5 years of use, 7397 quality improvement projects were completed using the electronic tool. National implementation of a structured tool for quality improvement is possible.

Learning health systems from an academic perspective: establishing a collaboratory within a school of medicine and health sciences.

Learning Health Systems (LHSs) seek continuous improvement through the translation and integration of internally and externally generated knowledge across stakeholders within and external to the organization, yet current approaches are primarily described from the healthcare delivery perspective, leaving teaching and research responsibilities underexposed. Academic medical centers offer a unique perspective on LHSs because their mission includes teaching, research, and healthcare. This introduces an opportunity to enact, educate, and study processes and outcomes of LHSs within a single system. Little information is available to describe these processes and outcomes, resulting in a knowledge gap regarding the role of education and research in the quality improvement cycles and learning of LHSs. To close this knowledge gap, The George Washington University School of Medicine and Health Sciences initiated the Health Research and Education Collaboratory (GW Collaboratory) in 2017. The GW Collaboratory was established to study mechanisms supporting continuous quality improvement and learning in health systems within an academic medical center. We envision the GW Collaboratory as interconnected knowledge nodes facilitating collaboration among clinicians, patients, researchers, and educators to study the knowledge generation, dissemination, application, and evaluation required for continuous quality improvement and learning. We employ a project-based approach to foster communities of learning focused on exploring specific health problems of interest. We propose the GW Collaboratory as one model by which academic medical centers can contribute to the science of LHS.

The essential role of population health during and beyond COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has fundamentally changed how health care systems deliver services and revealed the tenuousness of care delivery based on face-to-face office visits and fee-for-service reimbursement models. Robust population health management, fostered by value-based contract participation, integrates analytics and agile clinical programs and is adaptable to optimize outcomes and reduce risk during population-level crises. In this article, we describe how mature population health programs in a learning health system have been rapidly leveraged to address the challenges of the pandemic. Population-level data and care management have facilitated identification of demographic-based disparities and community outreach. Telemedicine and integrated behavioral health have ensured critical primary care and specialty access, and mobile health and postacute interventions have shifted site of care and optimized hospital utilization. Beyond the pandemic, population health can lead as a cornerstone of a resilient health system, better prepared to improve public health and mitigate risk in a value-based paradigm.

A practical model for research with learning health systems: Building and implementing effective complex case management.

For researchers to contribute meaningfully to the creation of learning health systems, practical tools are required to operationalize existing conceptual frameworks. We describe a model currently in use by the University of Wisconsin Health Innovation Program (HIP). The HIP model consolidates and enhances existing learning health system frameworks by defining specific steps needed to create sustainable change based on research conducted within the health system. As an example of the model's application, we describe its use to improve patient identification for the University of Wisconsin health system's case management program. Our case study shows the importance of culture, infrastructure, and strong leadership support in realizing a learning health systems research project and creating sustainable change within the health system. By articulating the foundational elements and steps to conduct research with learning health systems, our model supports researchers in achieving the challenge of moving learning health systems from concept to action.

A qualitative study of design stakeholders' views of developing and implementing a registry-based learning health system.

BACKGROUND: New opportunities to record, collate, and analyze routine patient data have prompted optimism about the potential of learning health systems. However, real-life examples of such systems remain rare and few have been exposed to study. We aimed to examine the views of design stakeholders on designing and implementing a US-based registry-enabled care and learning system for cystic fibrosis (RCLS-CF). METHODS: We conducted a two-phase qualitative study with stakeholders involved in designing, implementing, and using the RCLS-CF. First, we conducted semi-structured interviews with 19 program personnels involved in design and delivery of the program. We then undertook 11 follow-up interviews. Analysis of interviews was based on the constant comparative method, supported by NVivo software. RESULTS: The organizing principle for the RCLS-CF was a shift to more partnership-based relationships between patients and clinicians, founded in values of co-production, and facilitated by technology-enabled data sharing. Participants proposed that, for the system to be successful, the data it collects must be both clinically useful and meaningful to patients and clinicians. They suggested that the prerequisites included a technological infrastructure capable of supporting data entry and joint decision-making in an accessible way, and a set of social conditions, including willingness from patients and clinicians alike to work together in new ways that build on the expertise of both parties. Follow-up interviews highlighted some of the obstacles, including technical challenges and practical constraints on refiguring relationships between clinicians and patients. CONCLUSIONS: The values and vision underlying the RCLS-CF were shared and clearly and consistently articulated by design stakeholders. The challenges to realization were often not at the level of principle, but were both practical and social in character. Lessons from this study may be useful to other systems looking to harness the power of "big data" registries, including patient-reported data, for care, research, and quality improvement.

Novel tools for a learning health system: a combined difference-in-difference/regression discontinuity approach to evaluate effectiveness of a readmission reduction initiative.

Current methods used to evaluate the effects of healthcare improvement efforts have limitations. Designs with strong causal inference-such as individual patient or cluster randomisation-can be inappropriate and infeasible to use in single-centre settings. Simpler designs-such as prepost studies-are unable to infer causal relationships between improvement interventions and outcomes of interest, often leading to spurious conclusions regarding programme success. Other designs, such as regression discontinuity or difference-in-difference (DD) approaches alone, require multiple assumptions that are often unable to be met in real world improvement settings. We present a case study of a novel design in improvement and implementation research-a hybrid regression discontinuity/DD design-that leverages risk-targeted improvement interventions within a hospital readmission reduction programme. We demonstrate how the hybrid regression discontinuity-DD approach addresses many of the limitations of either method alone, and represents a useful method to evaluate the effects of multiple, simultaneous heath system improvement activities-a necessary capacity of a learning health system. Finally, we discuss some of the limitations of the hybrid regression discontinuity-DD approach, including the need to assign patients to interventions based upon a continuous measure, the need for large sample sizes, and potential susceptibility of risk-based intervention assignment to gaming.