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A Foundation for Evidence-Driven Practice A Rapid Learning System for Cancer Care: Workshop Summary (2010) / Chapter Skim
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8 Summary
Pages 75-78

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 75...
... He began his summary by noting that traditional linear learning has to be replaced by the rapid network learning that typifies the current computerdriven world. "We live in a network world in which we can gain information in a much more rapid fashion, and we can vet that information in a much more rapid fashion too.
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From page 76...
... Creating natural feedback loops so the results captured are evalu ated and fed back into the system for learning and improvement purposes Because the modern network approach to learning is nonlinear, as opposed to the more traditional linear approach, "one of our biggest challenges is to force ourselves to look specifically at those intersecting dynamics, at each of those points in the feedback process and ensure that the elements necessary for the success of the activities at each of those points are given consideration," Dr. McGinnis said.
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From page 77...
... Dr. McGinnis agreed with the need for public policy changes and noted that nongovernmental groups could also "take ownership of the learning obligation." "You have in the cancer community the patients, the providers, and the motivations to serve as the leading edge of progress and to engage decision makers in an explicit exploration of the issues, opportunities, and responsibilities when it comes to improving access to data, which is a fundamental issue for a learning healthcare system.
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Key Terms

  • learning healthcare
  • rapid fashion
  • traditional linear
  • improving access
  • driven world

  • premarket domain
  • look specifically
  • rapid network
  • traditionally limited
  • results captured

  • explicit exploration
  • global clinical
  • data trust
  • linear learning
  • encounter ought

  • network learning
  • network approach
  • described six
  • engage decision
  • appropriate level

  • creating natural
  • capture data
  • starkly constrained
  • continuous learning
  • network world

  • clinical encounter
  • reliable observational
  • fundamental issue
  • justifiable concerns
  • cancer community


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