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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Rapid learning: a breakthrough agenda.

Lynn M Etheredge1

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This summary is machine-generated.

A rapid-learning health system uses big data and electronic health records to quickly find the best patient treatments. Continued investment and collaboration are crucial for advancing this evidence-based medicine approach.

Keywords:
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Area of Science:

  • Health Informatics
  • Health Services Research
  • Big Data Analytics

Background:

  • The concept of a rapid-learning health system was introduced in 2007 to expedite evidence-based medicine.
  • Early development (2007-2013) involved workshops, policy initiatives, and public investment in data infrastructure.
  • The system leverages electronic health records and big data for high-speed information retrieval.

Purpose of the Study:

  • To outline the foundational development of rapid-learning health systems.
  • To identify current challenges and future directions for implementation.
  • To emphasize the need for advanced comparative effectiveness research and decision support tools.

Main Methods:

  • Utilizing electronic health records and big data analytics.
  • Developing standardized data handling for genetic information.
  • Integrating health information technology for improved information exchange.

Main Results:

  • Significant foundational work has been completed since 2007.
  • Key challenges remain in scaling clinical research and modernizing trials.
  • Advancements are needed in analyzing genetic factors and improving clinical decision support.

Conclusions:

  • Implementing a large-scale rapid-learning health system requires overcoming significant hurdles.
  • Future progress depends on continued investment in comparative effectiveness research and health IT.
  • Commitment, leadership, and collaborative efforts are essential for realizing the full potential of these systems.