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Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor

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  • Yaron Ilan

    (Department of Medicine, The Hebrew University of Jerusalem-Hadassah Medical Center, Jerusalem 12000, Israel)

Abstract

Background and Aims: Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market differentiators. Two of the problems associated with the partial or complete loss of response to chronic medications are a lack of adherence and compensatory responses to chronic drug administration, which leads to tolerance and loss of effectiveness. Approach and Results: First-generation artificial intelligence (AI) systems do not address these needs and suffer from a low adoption rate by patients and clinicians. Second-generation AI systems are focused on a single subject and on improving patients’ clinical outcomes. The digital pill, which combines a personalized second-generation AI system with a branded or generic drug, improves the patient response to drugs by increasing adherence and overcoming the loss of response to chronic medications. By improving the effectiveness of drugs, the digital pill reduces healthcare costs and increases end-user adoption. The digital pill also provides a market differentiator for branded and generic drug companies. Conclusions: Implementing the use of a digital pill is expected to reduce healthcare costs, providing advantages for all the players in the healthcare system including patients, clinicians, healthcare authorities, insurance companies, and drug manufacturers. The described business model for the digital pill is based on distributing the savings across all stakeholders, thereby enabling improved global health.

Suggested Citation

  • Yaron Ilan, 2021. "Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:811-:d:482802
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    1. Simiao Chen & Michael Kuhn & Klaus Prettner & David E Bloom, 2018. "The macroeconomic burden of noncommunicable diseases in the United States: Estimates and projections," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.
    2. Tina Toni & Bruce Tidor, 2013. "Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-17, March.
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    1. Ram Gelman & Marc Berg & Yaron Ilan, 2022. "A Subject-Tailored Variability-Based Platform for Overcoming the Plateau Effect in Sports Training: A Narrative Review," IJERPH, MDPI, vol. 19(3), pages 1-16, February.

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