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Critically Appraising the Wellness Movement: Cost Without Value, A Narrative Review.

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Direct-to-consumer wellness testing offers little evidence-based value, costing billions and harming patients. A new three-tier precision health framework using AI and targeted testing can improve care and reduce costs.

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Area of Science:

  • Health informatics
  • Genomics
  • Medical diagnostics

Background:

  • The direct-to-consumer wellness testing market has expanded rapidly, encompassing genomics, lab panels, wearables, and imaging.
  • This market growth outpaces scientific evidence, leading to significant downstream costs and potential patient harm, including false positives and anxiety.

Purpose of the Study:

  • To propose a three-tier precision health framework to optimize diagnostic testing.
  • To integrate data from omics, sensors, imaging, and lifestyle medicine.
  • To leverage AI for population assessment and guide high-value testing based on pre-test probability.

Main Methods:

  • Synthesizing data across multiple health-related fields (omics, sensors, imaging, lifestyle medicine).
  • Developing a three-tier framework: Tier 1 (high-sensitivity screens), Tier 2 (targeted confirmation), and Tier 3 (precision interventions).
  • Utilizing Artificial Intelligence (AI) for population assessment to determine pre-test probability.

Main Results:

  • The proposed framework aims to reduce low-value diagnostics and associated downstream costs (estimated over $100 billion annually in the US).
  • It seeks to mitigate patient exposure to false-positive results, anxiety, and bias.
  • The model prioritizes high-value testing guided by pre-test probability.

Conclusions:

  • Adopting this precision health model can significantly decrease unnecessary medical examinations.
  • Resources can be redirected towards evidence-based preventive measures.
  • This approach promises to enhance healthcare efficiency and patient outcomes.