Persistent knowledge is essential for propagating the learning health system (LHS) cycle. Integral to the cycle are iterative transformations of data into knowledge. However, human efforts to undertake these transformations are increasingly challenged when dealing with larger data scales and complexities. Data sets within repositories and archives are often underutilized unless specifically requested for research programs. Specialized software algorithms (agents) can use existing knowledge for learning tasks, explore their environment, discover and create goals, and interact with humans. This paper examines the potential role of software intelligence for autonomous transformations of data and knowledge. Agents can perform various goal-directed tasks. Multi-agent systems can be utilized for data collection, description, preparation, modeling, and knowledge-mining tasks. Knowledge representation, ontologies, semantic web standards, knowledge bases, and graphs can lead to a higher level of directed learning. Agents can develop reasoning abilities and self-generate goals by leveraging semantic relationships between various datasets. A conceptual framework for an intelligent biomedical platform (IBP) is proposed. The IBP comprises four layers: infrastructure (IS), user interface (UI), coordination system (CS), and data and knowledge (DK). It also integrates a network of multi-agent systems for clinical decision-making and knowledge-mining tasks. Intelligence in the platform results from the interaction of the IS, UI, CS, and DK agents. These agents can implement multiple inferential steps using the data and knowledge within accessible repositories. Large language models can be integrated with various knowledge resources and domain-specific databases, thereby improving the accuracy of results. An IBP supported by a multi-agent system can enhance the autonomous transformation of data and knowledge. Including software intelligence within current repositories and archives enhances data reuse and the generation of new knowledge. With the addition of software reasoning capabilities in biomedical platforms, the LHS cycle can be efficiently propagated to aid in newer biomedical discoveries.

ABSTRACTIntroductionThis article shares insights from the Learning Health Systems Symposium, “Charting the Future of Saskatchewan Healthcare: Generating Value within our Health Systems.”MethodsPatient Partners and over 100 professionals in the fields of research, healthcare, and policy joined the Saskatchewan Centre for Patient‐Oriented Research (SCPOR) in Regina, Saskatchewan Canada, for a day filled with knowledge about learning health systems (LHSs). Attendees provided key insights on how to build provincial capacity in LHSs and thereby improve the health of people in Saskatchewan.ResultsKey insights from the symposium included strengthening meaningful partnerships with patients and the community; establishing a shared vision for LHSs; harmonizing conflicting priorities; removing silos; recognizing the natural tensions between academia and the healthcare system; and building on and aligning infrastructure to support LHSs development.ConclusionThe LHSs symposium provided a way for health system leaders, Patient Partners, researchers, clinicians, and students to develop a common understanding of LHSs and envision a future for LHSs in Saskatchewan, Canada. Key next steps were to strengthen patient and community engagement, work collaboratively to establish a shared vision of LHSs, better understand existing assets to support LHSs, and identify opportunities to build future LHSs infrastructure.

ABSTRACTObjectiveEmbedded research partnerships can advance the implementation of evidence‐based policies and practices, including those aligned with person‐centered care. Care delivery model transformations, such as the VA's person‐centered Whole Health System (WHS), can benefit from an ongoing cycle of program implementation and their evaluation to inform future evolution. This paper describes how embedded researchers partnered with policy makers leading VA's WHS transformation to support its development, implementation, and scaling to illustrate lessons learned for embedded research.Data Sources and Study SettingFifty‐eight embedded research projects were conducted from FY2013 to FY2024.Study DesignWe recorded each project's scope, methodology, results, products, and impact. Through a group reflection process, we identified common cross‐project lessons that fostered this successful embedded research partnership. Finally, we mapped projects to phases of Kilbourne's Knowledge to Action Framework (Pre‐Implementation, Implementation, and Sustainment) to demonstrate how embedded researchers defined evaluation questions, evaluated WHS transformation, and assessed outcomes to inform the implementation and sustainment of VA's WHS transformation.Data Collection/Extraction MethodsProjects used multiple qualitative, survey, and large database methods.Principal FindingsAcross 58 projects, 380 discrete products were used by our operational partner to refine the WHS model, improve implementation support, scale effective practices, inform new policy, and sustain transformation. Three practices cut across these projects to contribute to our successful embedded research partnership: agility, collaboration, and continuous learning and improvement. Additionally, the purpose, questions, and methods of embedded research projects varied as operational partner activities moved across pre‐implementation, implementation, and sustainment phases, iteratively impacting WHS transformation.ConclusionsEmbedded research can transform a healthcare system through the timely translation of data into practice, enabling evidence‐based policy and practice decisions. Our embedded research‐operations partnership, characterized by trust, respect, and strong communication, ensured that evaluations generated an evidence base to inform both implementation and impact.

ABSTRACTIntroductionLearning health systems leverage clinical data and knowledge to advance healthcare quality. Effective training in informatics concepts and tools is essential for medical trainees to become health system experts and contributors to positive organizational change. The objective of this study is to summarize characteristics of existing clinical informatics training programs and map these to recommended Learning Health System informatics competencies. We aim to answer the following research questions: (1) How are academic medical institutions implementing informatics education initiatives for medical trainees? (2) Are these initiatives implementing recommended informatics competencies? and (3) How effective are these initiatives according to established health professions education evaluation frameworks?MethodsWe searched for literature in the databases Embase, Ovid Medline, and Web of Science. Three researchers independently screened study titles and abstracts. Inclusion criteria were (a) studies with medical students and/or postgraduate medical professionals in the study sample, (b) in an academic medical setting, and (c) describing a clinical informatics curriculum initiative.ResultsWe included a total of 27 studies for analysis. Most curricula (n = 16) had the objective of basic informatics knowledge acquisition. Instruction delivery for most (n = 17) included a combination of didactic and practical components. The most common evaluation tool was student self‐reported confidence and self‐efficacy. All but three of the studies integrated the recommended informatics competency of demonstrating knowledge of clinical information systems such as Electronic Health Records. Most studies (n = 12) reported outcomes related to the Kirkpatrick Level II of Learning.ConclusionGaps remain in the context of leveraging education to pursue Learning Health System endeavors of clinical research, quality improvement, and achieving organizational‐level results. Further research on recent education initiatives targeting undergraduate and graduate medical trainees is needed to elevate the rate of clinical informatics education implementation, while simultaneously advocating for standardization in the design and evaluation of these initiatives.

ABSTRACTIntroductionThe Swiss Learning Health System (SLHS), funded from 2017 to 2025, facilitated the movement of research to practice and policy, responding to national calls to enhance health services research and build research scientist capacity. This study evaluates the SLHS to understand how Learning Health System (LHS) science can provide a foundation for Knowledge Translation (KT) platforms at a national level, identifying successes, challenges, and lessons learned from capacity building and institutionalizing KT in support of evidence‐informed decision‐making (EIDM) in health policy and practice.MethodsWe employed a mixed‐methods approach from September to December 2023, using the SLHS program aims and the Knowledge‐to‐Action framework to inform the study's conceptualization. Data collection involved two workshops with over 40 SLHS members, a survey of 39 members, and in‐depth interviews with 10 key informants, analyzed using descriptive and thematic methods.ResultsCapacity‐building efforts prompting a cultural shift by training research scientists to adopt a science‐policy‐practice mindset were the most common successes captured. The LHS and KT approaches aided in dismantling silos and encouraged community building through participatory methods. An important lesson learned is the value of co‐creation involving key partners, especially patients, in the research process to strengthen relevance, issue prioritization, and evidence use. However, challenges persisted in adequately tailoring and transferring knowledge, highlighting the need for more consistent engagement with community partners to enhance the impact and relevance of KT efforts.ConclusionThis study demonstrated that the SLHS is a valued initiative for capacity building, while highlighting the need to strengthen co‐creation and refine strategies for adapting research evidence for EIDM in health policy and practice.

ABSTRACTBackgroundAccess improvement is a fundamental component of value‐based healthcare as it inherently promotes quality by eliminating chokepoints, redundancies, and inefficiencies that could hinder the provisioning of timely care. Yet as healthcare organizations struggle with cost containment, the question of how to most effectively enhance access remains largely unsolved. Given the economic, regulatory, and social forces across the healthcare marketplace, the critical importance of access in optimizing efficiency is increasingly being recognized. The purpose of this review is to thus present a practical framework that offers healthcare organizations an actionable, thematic‐based foundation for approaching access improvement.Methods and MaterialsAn interpretive synthesis of health access as it relates to the timely, satisfactory, and sustainable receipt of services was presented while addressing operational constraints, inclusivity and underlying determinants of health. The criticality of this concept, spanning the entire healthcare continuum and encompassing all aspects of care delivery, from making an initial appointment to completing treatment and being followed thereafter, was evaluated. Empirical lessons were highlighted for discussion.ResultsGiven the sense of urgency that exists around this issue, the potential pitfalls of “hurrying” to initiate access improvement are frequently overlooked by health systems. Considerations related to workforce shortages, resource limitations, logistical coordination, workflow processes, space capacity, provider availability, and organizational culture and hierarchy, among others, need to be methodically addressed so as not to introduce new inefficiencies into a healthcare environment that is already viewed by many as overly bureaucratic. Based on the core themes that emerged, a conceptual framework for access improvement centered on strategy, alignment, execution, adaptation, and reflection was developed.ConclusionThe design, planning, and operationalization of access improvement initiatives in healthcare require meticulous organizational preparation and a deliberate leadership approach incorporating principles of change management. Success will be dependent on achieving the appropriate balance between speed, purpose, and precision.

ObjectiveDoctoral and postdoctoral training in health services and policy research (HSPR) needs to evolve to address changing career trajectories, emerging health system challenges, and the advancement of learning health systems. This changing landscape necessitates examination of the core competencies that underpin training. This study presents a refreshed set of core competencies for HSPR to guide the modernization of training and considerations for implementation.MethodsQualitative methods and an iterative development process with extensive community engagement throughout were used. Data were obtained from multiple sources, including literature reviews, surveys, key informant interviews, focus groups, Task Force meetings, a consensus workshop, and a validation process (Fall 2022 to May 2024). The study setting is Canada.ResultsThe refreshed core competencies include nine essential domains that maintain an emphasis on rigorous scholarly preparation and prioritize leadership and other professional skills deemed essential to contribute to evidence‐informed system improvement and learning health systems. Additionally, the framework features two new transversal domains: Equity, Diversity, Inclusion, Accessibility, and Anti‐Oppression; and Indigenous Cultural Safety and Humility. These domains are considered fundamental principles to be embedded into all aspects of HSPR competencies and training, fostering more inclusive and equitable leaders and health systems.ConclusionsAs health systems continuously evolve, so too should the questions researchers address, the methods they use, and the skills needed to maximize contributions to evidence‐informed health system improvement and learning health systems. The refreshed core competencies for HSPR maintain important continuity with the inaugural competency framework while also including several important additions. The framework allows for flexibility in its implementation and us; it can be used to guide the enhancement of existing training programs, the development of new ones, and the growth and development of a HSPR workforce with the skills to lead and contribute within learning health systems.

BackgroundClinical quality registries (CQR) systematically monitor and provide feedback on the appropriateness and effectiveness of health care within specific clinical domains, collecting data about medical care processes and outcomes, and providing benchmarked performance reports to health care providers for the purpose of driving improvements in safety and quality.AimsThe successful development of a learning infrastructure requires the alignment of people, technologies, policies, and processes, brought together by shared needs and a culture of continuous quality improvement. The creation and description of such infrastructure is likely to successfully support the delivery of services critical to continuous quality improvement initiatives.Materials and MethodsWe aimed to describe the construction and impacts of healthcare improvement infrastructure within CQRs.ResultsSocio‐technical infrastructure comprises participants that contribute to the design, evaluation, reporting and dissemination of quality improvement activities: employing effective and timely data acquisition, describing healthcare processes and outcomes; supported by directed policy process and supportive organisations. Review of CQR function identifies positive impacts on healthcare utilisation, improved clinical outcomes and significant improvement in survival supported by cost effective investment.DiscussionQuality improvement frameworks and strategies have been developed to drive CQRs towards international best practice in learning health system structure for data collection and reporting; delivering efficiency and interoperability in data collection and exchange; promoting standardised approaches to CQR design.ConclusionClinical quality registries have demonstrated significant national impacts in quality improvement in high burden disease domains. These registries rely on the construction and resourcing of socio‐technical infrastructures that support quality improvement.

Improving health equity requires engagement with partners outside of the clinical health system to address the socio-political-economic and structural drivers that shape health equity. Health systems need to focus on learning with communities to impact these conditions. The learning health system will need to evolve to a learning community to advance health equity. We describe the experience of researchers, educators, clinicians, and community members ("four pillars") working together to develop the Dartmouth Health Center for Advancing Rural Health Equity. Structures and processes were co-designed to create an infrastructure that supports partnerships dedicated to improving rural health equity through community-based projects, education and training, and advocacy efforts. Co-designing an infrastructure to support enduring partnerships between research, education, clinical service, and community can support learning and improvement activities focused on improving health equity.

IntroductionA learning health system (LHS) strives to improve clinical practice and outcomes through applied research and quality improvement (QI). However, distinguishing between research and QI has been a persistent challenge. While research involving human subjects is highly regulated, QI remains largely unregulated, lacking in comparable oversight. With confusion and uncertainty surrounding this distinction and little practical guidance for QI activities, Denver Health's LHS developed a model of practice for reviewing QI projects.MethodsIn 2018, Denver Health, an integrated academic safety‐net delivery system, established the Quality Improvement Review Committee (QuIRC) as a practical approach for distinguishing between QI and human subjects research. We describe the institutional structure and processes, from identifying the problem to establishing the committee and charter, to obtaining institution support, and finally implementation and improvement, ensuring transparency and protections of disseminated QI work.ResultsOver 7 years, the QuIRC has reviewed 379 submissions, with 78% approved as QI (non‐human subjects research), 8% referred to the Colorado Multiple Institutional Review Board, and 13% requiring clarification or being withdrawn. A standardized review process, clear charter, broad organizational representation, executive sponsorship, and IRB collaboration enhanced transparency and engagement. The QuIRC has facilitated QI dissemination, supporting the LHS framework and increasing recognition of the impact of QI on clinical care and patient outcomes.ConclusionsThe QuIRC framework has improved clarity and oversight of QI activities at Denver Health. These practical approaches can be adapted by other health care systems, contributing to broader efforts to establish national guidance for QI oversight.