Browsing by Subject "Learning health systems"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Adolescent Assent and Reconsent for Biobanking: Recent Developments and Emerging Ethical Issues
    (Frontiers Media, 2021-07-09) Kasperbauer, T. J.; Halverson, Colin; Medicine, School of Medicine
    Research biobanks that enroll minors face important practical, ethical, and regulatory challenges in reconsenting participants when they reach the age of 18. Federal regulations governing research in the United States provide minimal guidance and allow for a range of practices, including waiving the requirement to obtain reconsent. Some commentators have argued that institutional review boards should indeed grant such waivers, given the low risks of biobank-based research and the impracticality of contacting all participants when they turn 18. There is also significant ethical debate about the age at which adolescents can make authentic, autonomous decisions regarding their research participation. This paper reviews these issues in detail, describes the current state of the ethical discussion, and outlines evidence-based policies for enrolling minors into research biobanks.
  • Thumbnail Image
    Item
    Baseline Quality Improvement Capacity of 33 Endocrinology Centers Participating in the T1D Exchange Quality Improvement Collaborative
    (American Diabetes Association, 2022) Marks, Brynn E.; Mungmode, Ann; Neyman, Anna; Levin, Laura; Rioles, Nicole; Eng, Donna; Lee, Joyce M.; Basina, Marina; Hawah-Jones, Nana; Mann, Elizabeth; O’Malley, Grenye; Wilkes, Meredith; Steenkamp, Devin; Aleppo, Grazia; Accacha, Siham; Ebekozien, Osagie; Pediatrics, School of Medicine
    This article describes the evolution of the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI) and provides insight into the development and growth of a successful type 1 diabetes quality improvement (QI) program. Since its inception 8 years ago, the collaborative has expanded to include centers across the United States with varying levels of QI experience, while simultaneously achieving many tangible improvements in type 1 diabetes care. These successes underscore the importance of learning health systems, data-sharing, benchmarking, and peer collaboration as drivers for continuous QI. Future efforts will include recruiting additional small- to medium-sized centers focused on adult care and underserved communities to further the goal of improving care and outcomes for all people living with type 1 diabetes.
  • Thumbnail Image
    Item
    Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system
    (Wiley, 2024-04-16) Rajamani, Geetanjali; Melton, Genevieve B.; Pestka, Deborah L.; Peters, Maya; Ninkovic, Iva; Lindemann, Elizabeth; Beebe, Timothy J.; Shippee, Nathan; Benson, Bradley; Jacob, Abraham; Tignanelli, Christopher; Ingraham, Nicholas E.; Koopmeiners, Joseph S.; Usher, Michael G.; Medicine, School of Medicine
    Background: Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability. Objectives: We established a pragmatic clinical trial unit ("RapidEval") to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery. Methods: During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership. Implementation results: Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2). Conclusions: Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team.
  • Thumbnail Image
    Item
    Privacy‐preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience
    (Wiley, 2024-01-11) Tachinardi, Umberto; Grannis, Shaun J.; Michael, Sam G.; Misquitta, Leonie; Dahlin, Jayme; Sheikh, Usman; Kho, Abel; Phua, Jasmin; Rogovin, Sara S.; Amor, Benjamin; Choudhury, Maya; Sparks, Philip; Mannaa, Amin; Ljazouli, Saad; Saltz, Joel; Prior, Fred; Baghal, Ahmen; Gersing, Kenneth; Embi, Peter J.; Medicine, School of Medicine
    Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy-preserving record linkage (PPRL) methods to ensure that patient-level EHR data remains secure and private when governance-approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear-text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross-linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large-scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.