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<p>Delays in translating new medical evidence into clinical practice hinder patient access to the best available treatments. Our data reveals an average delay of nine years from the initiation of human research to its adoption in clinical guidelines, with 1.7–3.0 years lost between trial publication and guideline updates. A substantial part of these delays stems from slow, manual processes in updating clinical guidelines, which rely on time-intensive evidence synthesis workflows. The Next Generation Evidence (NGE) system addresses this challenge by harnessing state-of-the-art biomedical Natural Language Processing (NLP) methods. This novel system integrates diverse evidence sources, such as clinical trial reports and digital guidelines, enabling automated, data-driven analyses of the time it takes for research findings to inform clinical practice. Moreover, the NGE system provides precision-focused literature search filters tailored specifically for guideline maintenance. In benchmarking against two German oncology guidelines, these filters demonstrate exceptional precision in identifying pivotal publications for guideline updates.</p>

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High-precision information retrieval for rapid clinical guideline updates

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月27日

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  • Florian Borchert,
  • Paul Wullenweber,
  • Annika Oeser,
  • Nina Kreuzberger,
  • Torsten Karge,
  • Thomas Langer,
  • Nicole Skoetz,
  • Lothar H. Wieler,
  • Matthieu-P. Schapranow,
  • Bert Arnrich

摘要

Delays in translating new medical evidence into clinical practice hinder patient access to the best available treatments. Our data reveals an average delay of nine years from the initiation of human research to its adoption in clinical guidelines, with 1.7–3.0 years lost between trial publication and guideline updates. A substantial part of these delays stems from slow, manual processes in updating clinical guidelines, which rely on time-intensive evidence synthesis workflows. The Next Generation Evidence (NGE) system addresses this challenge by harnessing state-of-the-art biomedical Natural Language Processing (NLP) methods. This novel system integrates diverse evidence sources, such as clinical trial reports and digital guidelines, enabling automated, data-driven analyses of the time it takes for research findings to inform clinical practice. Moreover, the NGE system provides precision-focused literature search filters tailored specifically for guideline maintenance. In benchmarking against two German oncology guidelines, these filters demonstrate exceptional precision in identifying pivotal publications for guideline updates.