Matthew B. Carson

Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk‐natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross‐pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.Results: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer‐reviewed publications. In 2022, our cross‐discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.Conclusions: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution‐level learning health system.

Scientific discovery depends on access to data and the knowledge this data makes possible. Research data sharing is increasingly recognized as a priority for organizations to support the successful conduct of research. The National Institutes of Health states, "data sharing enables researchers to rigorously test the validity of research findings, strengthen analyses through combined datasets, reuse hard-to-generate data, and explore new frontiers of discovery" [1]. Conversely, in the absence of data sharing, there are increased risks related to the robustness, rigor, and replicability of results, and the potential of valuable data is diminished. For these reasons and more, institutional data sharing capacity is a critical topic for organizations to scrutinize, discuss, and advance. Advocacy and support for data sharing are often discussed with an emphasis on understanding and supporting the practices of individual investigators or scientific communities [2,3]. However, a researcher's ability to successfully engage in and benefit from sound data sharing depends on their organizational setting and, specifically, the organization's data sharing capacity. For example, sharing data is easier and more equitable when organizational processes and procedures are established and documented, and research workforce members can access centralized training and infrastructure resources. The effect of how an organization approaches and supports data sharing extends beyond the success of its investigators. Institutions that share data can participate in innovative largescale initiatives and pursue new funding opportunities. Universities that contribute to creating

Introduction: Enterprise data warehouses (EDWs) serve as foundational infrastructure in a modern learning health system, housing clinical and other system‐wide data and making it available for research, strategic, and quality improvement purposes. Building on a longstanding partnership between Northwestern University's Galter Health Sciences Library and the Northwestern Medicine Enterprise Data Warehouse (NMEDW), an end‐to‐end clinical research data management (cRDM) program was created to enhance clinical data workforce capacity and further expand related library‐based services for the campus.Methods: The training program covers topics such as clinical database architecture, clinical coding standards, and translation of research questions into queries for proper data extraction. Here we describe this program, including partners and motivations, technical and social components, integration of FAIR principles into clinical data research workflows, and the long‐term implications for this work to serve as a blueprint of best practice workflows for clinical research to support library and EDW partnerships at other institutions.Results: This training program has enhanced the partnership between our institution's health sciences library and clinical data warehouse to provide support services for researchers, resulting in more efficient training workflows. Through instruction on best practices for preserving and sharing outputs, researchers are given the tools to improve the reproducibility and reusability of their work, which has positive effects for the researchers as well as for the university. All training resources have been made publicly available so that those who support this critical need at other institutions can build on our efforts.Conclusions: Library‐based partnerships to support training and consultation offer an important vehicle for clinical data science capacity building in learning health systems. The cRDM program launched by Galter Library and the NMEDW is an example of this type of partnership and builds on a strong foundation of past collaboration, expanding the scope of clinical data support services and training on campus.

The National Institutes of Health (NIH) Policy for Data Management and Sharing (DMS Policy) recognizes the NIH’s role as a key steward of United States biomedical research and information and seeks to enhance that stewardship through systematic recommendations for the preservation and sharing of research data generated by funded projects. The policy is effective as of January 2023. The recommendations include a requirement for the submission of a Data Management and Sharing Plan (DMSP) with funding applications, and while no strict template was provided, the NIH has released supplemental draft guidance on elements to consider when developing a plan. This article provides 10 key recommendations for creating a DMSP that is both maximally compliant and effective.

Enabling and supporting discoverability of research outputs and datasets are key functions of university and academic health center institutional repositories. Yet adoption rates among potential repository users are hampered by a number of factors, prominent among which are difficulties with basic usability. In their efforts to implement a local instance of InvenioRDM, a turnkey next generation repository, team members at Northwestern University’s Galter Health Sciences Library & Learning Center supplemented agile development principles and methods and a user experience design-centered approach with observations of users’ microinteractions (interactions with each part of the software’s interface that requires human intervention). Microinteractions were observed through user testing sessions conducted in Fall 2019. The result has been a more user-informed development effort incorporating the experiences and viewpoints of a multidisciplinary team of researchers spanning multiple departments of a highly ranked research university.

The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) to electronic health record (EHR) narratives for pharmacovigilance. We review methods of varying complexity and problem focus, summarize the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. The ability to accurately capture both semantic and syntactic structures in clinical narratives becomes increasingly critical to enable efficient and accurate ADE detection. Significant progress has been made in algorithm development and resource construction since 2000. Since 2012, statistical analysis and machine learning methods have gained traction in automation of ADE mining from EHR narratives. Current state-of-the-art methods for NLP-based ADE detection from EHRs show promise regarding their integration into production pharmacovigilance systems. In addition, integrating multifaceted, heterogeneous data sources has shown promise in improving ADE detection and has become increasingly adopted. On the other hand, challenges and opportunities remain across the frontier of NLP application to EHR-based pharmacovigilance, including proper characterization of ADE context, differentiation between off- and on-label drug-use ADEs, recognition of the importance of polypharmacy-induced ADEs, better integration of heterogeneous data sources, creation of shared corpora, and organization of shared-task challenges to advance the state-of-the-art.

Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes. Identifying communities in networks combining node attributes has become increasingly popular in recent years. Most existing methods operate on networks with attributes of binary, categorical, or numerical type only. In this study, we introduce kNN-enhance, a simple and flexible community detection approach that uses node attribute enhancement. This approach adds the k Nearest Neighbor (kNN) graph of node attributes to alleviate the sparsity and the noise effect of an original network, thereby strengthening the community structure in the network. We use two testing algorithms, kNN-nearest and kNN-Kmeans, to partition the newly generated, attribute-enhanced graph. Our analyses of synthetic and real world networks have shown that the proposed algorithms achieve better performance compared to existing state-of-the-art algorithms. Further, the algorithms are able to deal with networks containing different combinations of binary, categorical, or numerical attributes and could be easily extended to the analysis of massive networks.