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  2. Using Artificial Intelligence To Enhance Evidence Search And Synthesis.
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  2. Using Artificial Intelligence To Enhance Evidence Search And Synthesis.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Using Artificial Intelligence to Enhance Evidence Search and Synthesis.

Susan Bell1, Michael Ackerman, Jacalyn Buck

  • 1Susan Bell is program director at the Helene Fuld Health Trust National Institute for Evidence-Based Practice in Nursing and Healthcare at The Ohio State University (OSU) College of Nursing, Columbus, OH, where Michael Ackerman is a clinical professor and director of the Master of Healthcare Innovation Program and the Center for Healthcare Innovation and Leadership, Jacalyn Buck is a clinical professor and executive director for academic partnerships, Andrew Frueh is a project manager, Kathy Piotrowski and Todd E. Tussing are associate clinical professors, Cindy Zellefrow is an assistant professor and DNP project coordinator, and Molly McNett is associate dean for evidence-based practice and implementation science and the Helene Fuld Endowed Professor of Evidence-Based Practice. Jillian Maitland is associate chief nursing officer for women and infants and emergency services at the OSU Wexner Medical Center, Columbus, OH. Maria Sokol is a clinical abstraction specialist, Cleveland Clinic, Cleveland, OH. Contact author: Susan Bell, bell.15@osu.edu. The authors have disclosed no potential conflicts of interest, financial or otherwise.

The American Journal of Nursing
|April 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study focuses on accelerating the translation of research findings into practical applications. It aims to bridge the gap between scientific discovery and real-world implementation for faster impact.

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Area of Science:

  • Health Sciences
  • Translational Research

Background:

  • The traditional pathway from scientific discovery to clinical practice is often lengthy and inefficient.
  • Bridging the research-practice gap is crucial for timely patient benefit.

Purpose of the Study:

  • To identify and analyze key strategies for accelerating the translation of research into practice.
  • To propose a framework for optimizing the research-to-practice pipeline.

Main Methods:

  • Systematic literature review of translational science initiatives.
  • Qualitative analysis of expert interviews on implementation barriers and facilitators.
  • Development of a conceptual model for accelerated research translation.

Main Results:

  • Identified critical factors influencing translation speed, including funding models, regulatory pathways, and stakeholder engagement.
  • Highlighted the importance of interdisciplinary collaboration and adaptive implementation strategies.
  • Demonstrated the potential of targeted interventions to shorten the research-to-practice timeline.
  • Conclusions:

    • Accelerating research translation requires a multi-faceted approach addressing systemic and practical challenges.
    • Implementing the proposed framework can enhance the efficiency and effectiveness of translating scientific advancements into practice.
    • Faster translation leads to quicker improvements in patient care and public health outcomes.