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This study compares top-down and bottom-up strategies for implementing commercial artificial intelligence (AI) algorithms in Norwegian health regions. Both approaches yield valuable insights for AI adoption in healthcare.

Area of Science:

  • Health Services Research
  • Health Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Healthcare organizations are increasingly exploring the integration of commercial artificial intelligence (AI) algorithms.
  • The implementation of AI in healthcare presents unique challenges and requires strategic planning.
  • Understanding different implementation pathways is crucial for successful technology adoption.

Purpose of the Study:

  • To examine and compare two distinct strategies for implementing commercial AI algorithms in Norwegian health regions.
  • To identify the key characteristics of top-down, research-driven, and bottom-up, innovation-focused implementation approaches.
  • To assess the knowledge generated by each strategy for broader applicability in healthcare settings.

Main Methods:

Keywords:
Commercial AIbottom-upimplementation strategiestop-down

Related Experiment Videos

  • Comparative case study analysis of two Norwegian health regions.
  • Qualitative examination of AI algorithm implementation strategies.
  • Documentation of regional approaches, decision-making processes, and outcomes.

Main Results:

  • One health region adopted a top-down, research-driven strategy for AI implementation.
  • The second health region utilized a bottom-up, innovation-focused strategy for AI implementation.
  • Both strategies generated distinct but valuable knowledge regarding AI implementation in healthcare.

Conclusions:

  • There is no single optimal approach for implementing commercial AI algorithms in healthcare.
  • Both top-down and bottom-up strategies can effectively facilitate AI adoption.
  • The knowledge gained from diverse implementation strategies is transferable and beneficial for other healthcare settings.