In-depth interviews with ten key leaders at Seattle Children's, deeply involved in the development of their enterprise analytics program, were carried out. Leadership roles under review during interviews included Chief Data & Analytics Officer, Director of Research Informatics, Principal Systems Architect, Manager of Bioinformatics and High Throughput Analytics, Director of Neurocritical Care, Strategic Program Manager & Neuron Product Development Lead, Director of Dev Ops, Director of Clinical Analytics, Data Science Manager, and Advance Analytics Product Engineer. Conversations, forming the unstructured interviews, sought to glean leadership perspectives on their experience developing enterprise analytics at Seattle Children's.
With an entrepreneurial spirit and agile development methodologies, much like those found in innovative startups, Seattle Children's has built an advanced, enterprise-wide analytics system that's an integral part of their everyday operations. An iterative approach to analytics efforts involved selecting high-value projects, which were executed by Multidisciplinary Delivery Teams embedded within service lines. Service line leadership, in close collaboration with Delivery Team leads, steered the team to success by prioritizing projects, setting budgets, and maintaining governance over their analytical work. read more This organizational setup at Seattle Children's has spurred the creation of an extensive set of analytical products, which have enhanced both operational processes and patient clinical care.
Seattle Children's experience with a near real-time analytics ecosystem underscores how a leading healthcare system can cultivate a robust, scalable solution, delivering substantial value from the expanding volume of health data.
Seattle Children's has effectively illustrated how a prominent healthcare system can construct a powerful, expandable, real-time analytics infrastructure, one that extracts considerable value from the burgeoning volume of health data currently available.

Clinical trials yield evidence vital for informed decision-making, but also directly advance the well-being of the individuals who take part. Nevertheless, clinical trials frequently encounter setbacks, including difficulty in recruiting participants, and substantial financial burdens. The disconnected nature of clinical trials is a significant factor in hindering trial conduct. It prevents the rapid sharing of data, the development of insights, the implementation of tailored interventions, and the identification of knowledge gaps. Other areas of healthcare have explored the utilization of a learning health system (LHS) as a model for sustained improvement and learning. Clinical trial performance could be markedly improved through the implementation of an LHS approach, fostering continual enhancements in trial procedures and operational efficiency. read more A comprehensive trial data-sharing initiative, alongside an ongoing analysis of trial recruitment and other success metrics, and targeted trial enhancement activities, are likely important elements of a Trials Learning Health System, showcasing a continuous learning process and facilitating ongoing trial improvement. The development and utilization of a Trials LHS transforms clinical trials into a manageable system, providing benefits for patients, advancing the field of medicine, and decreasing the costs associated with trials for stakeholders.

Academic medical centers' clinical departments are committed to providing clinical care, facilitating education and training, nurturing faculty growth, and encouraging scholarly activities. read more These departments have faced a constant increase in the need to bolster the quality, safety, and value of their care delivery. Academic departments, however, frequently find themselves lacking the necessary number of clinical faculty experts in improvement science to spearhead initiatives, educate students, and create original research. The structure, actions, and early repercussions of a scholarly improvement program within an academic department of medicine are documented in this article.
A Quality Program, spearheaded by the University of Vermont Medical Center's Department of Medicine, is dedicated to three key objectives: advancing care delivery, providing educational resources and training, and promoting scholarly pursuits in improvement science. Designed as a resource hub for students, trainees, and faculty, the program furnishes educational and training opportunities, analytical support, consultation in design and methodology, and project management assistance. To improve healthcare, it aims to integrate education, research, and care delivery, with the purpose of applying and learning from evidence.
For the first three years of full-scale implementation, the Quality Program supported approximately 123 projects per year, including initiatives for improving clinical quality in the future, examining past clinical programs and practices, and curriculum design and evaluation. A total of 127 scholarly products, encompassing peer-reviewed publications, abstracts, posters, and presentations at local, regional, and national conferences, have emerged from the projects.
The Quality Program provides a practical model to promote improvement science scholarship, care delivery training, and advancements in care delivery, all of which support the objectives of a learning health system at the academic clinical department level. Such departmental resources, dedicated to the task, have the potential to improve care delivery and promote academic achievement for improvement science faculty and trainees.
With a focus on care delivery improvement, training, and scholarship in improvement science, the Quality Program can serve as a model for fostering a learning health system within an academic clinical department. Dedicated resources within such departments are poised to improve the provision of care while bolstering the academic success of faculty and trainees, with a specific emphasis on improvement science.

Learning health systems (LHSs) are defined in part by their commitment to providing evidence-based practice. The Agency for Healthcare Research and Quality (AHRQ) utilizes systematic reviews to create evidence reports, which summarize the available evidence on subjects of interest. Even with the AHRQ Evidence-based Practice Center (EPC) program's production of high-quality evidence reviews, their practical use and usability in the field are not guaranteed or encouraged.
With the aim of improving the significance of these reports for local health systems (LHSs) and facilitating the dissemination of evidence, AHRQ conferred a contract upon the American Institutes for Research (AIR) and its Kaiser Permanente ACTION (KPNW ACTION) collaborative partner to craft and deploy web-based solutions capable of bridging the implementation and dissemination gap in evidence-practice reports within local healthcare systems. We implemented a co-production approach across the three stages of activity planning, co-design, and implementation, to complete this work within the timeframe of 2018 to 2021. We outline the methods, summarize the findings, and analyze the implications for future activities.
LHSs benefit from web-based tools that provide clinically relevant summaries with clear visual representations of AHRQ EPC systematic evidence reports. These tools can improve awareness and accessibility of EPC reports, enhance LHS evidence review infrastructure, and facilitate the development of system-specific protocols and care pathways, leading to better practice at the point of care and training and education initiatives.
The co-design of these tools, coupled with facilitated implementation, fostered an approach to enhancing the accessibility of EPC reports, thereby enabling broader application of systematic review findings to support evidence-based practices within LHSs.
The joint creation and facilitated deployment of these tools brought about a way to make EPC reports more readily available and to more widely apply systematic review outcomes to backing evidence-based techniques in local healthcare systems.

A modern learning health system leverages enterprise data warehouses (EDWs) as its foundational infrastructure, housing clinical and other system-wide data to support research, strategic planning, and quality improvement. To further the existing partnership between Northwestern University's Galter Health Sciences Library and the Northwestern Medicine Enterprise Data Warehouse (NMEDW), a comprehensive clinical research data management (cRDM) program was implemented to strengthen the clinical data workforce and expand library support services for the university community.
A comprehensive training program includes coverage of clinical database architecture, clinical coding standards, and the translation of research questions into appropriate queries for accurate data extraction. A description of this program, encompassing collaborative partners and motivations, technical and social aspects, integrating FAIR principles into clinical research workflows using patient data, and the long-term impact on establishing best practice workflows for clinical research, benefiting library and EDW partnerships at other institutions.
This training program has not only bolstered the collaboration between our institution's health sciences library and clinical data warehouse, but also improved support services for researchers, resulting in more efficient training workflows. By providing instruction on optimal methods for preserving and distributing research outputs, researchers gain the ability to enhance the reproducibility and usability of their work, benefiting both the researchers and the university. Open access to all training resources now allows those supporting this crucial need at other institutions to expand upon our collective work.
Clinical data science capacity building within learning health systems is significantly enhanced by library-based partnerships that provide training and consultation. Galter Library and the NMEDW's cRDM program exemplifies this partnership model, building upon a legacy of successful collaborations to augment clinical data support and training initiatives on campus.