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The Future of Precision Medicine: Potential Impacts for Health Technology Assessment

Author

Listed:
  • James Love-Koh

    (University of York
    University of York)

  • Alison Peel

    (University of York)

  • Juan Carlos Rejon-Parrilla

    (National Institute for Health and Care Excellence)

  • Kate Ennis

    (University of York
    University of Liverpool)

  • Rosemary Lovett

    (National Institute for Health and Care Excellence)

  • Andrea Manca

    (University of York
    Luxembourg Institute of Health)

  • Anastasia Chalkidou

    (Kings Technology Evaluation Centre)

  • Hannah Wood

    (University of York)

  • Matthew Taylor

    (University of York)

Abstract

Objective Precision medicine allows healthcare interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. We analysed what developments are expected in precision medicine over the next decade and considered the implications for health technology assessment (HTA) agencies. Methods We performed a pragmatic literature search to account for the large size and wide scope of the precision medicine literature. We refined and enriched these results with a series of expert interviews up to 1 h in length, including representatives from HTA agencies, research councils and researchers designed to cover a wide spectrum of precision medicine applications and research. Results We identified 31 relevant papers and interviewed 13 experts. We found that three types of precision medicine are expected to emerge in clinical practice: complex algorithms, digital health applications and ‘omics’-based tests. These are expected to impact upon each stage of the HTA process, from scoping and modelling through to decision-making and review. The complex and uncertain treatment pathways associated with patient stratification and fast-paced technological innovation are central to these effects. Discussion Innovation in precision medicine promises substantial benefits but will change the way in which some health services are delivered and evaluated. The shelf life of guidance may decrease, structural uncertainty may increase and new equity considerations will emerge. As biomarker discovery accelerates and artificial intelligence-based technologies emerge, refinements to the methods and processes of evidence assessments will help to adapt and maintain the objective of investing in healthcare that is value for money.

Suggested Citation

  • James Love-Koh & Alison Peel & Juan Carlos Rejon-Parrilla & Kate Ennis & Rosemary Lovett & Andrea Manca & Anastasia Chalkidou & Hannah Wood & Matthew Taylor, 2018. "The Future of Precision Medicine: Potential Impacts for Health Technology Assessment," PharmacoEconomics, Springer, vol. 36(12), pages 1439-1451, December.
    • Handle: RePEc:spr:pharme:v:36:y:2018:i:12:d:10.1007_s40273-018-0686-6
    DOI: 10.1007/s40273-018-0686-6
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    Citations

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    Cited by:

    1. Denicolai, Stefano & Previtali, Pietro, 2020. "Precision Medicine: Implications for value chains and business models in life sciences," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    2. Ioana Andreea Bogoslov & Sorina Corman & Anca Elena Lungu, 2024. "Perspectives on Artificial Intelligence Adoption for European Union Elderly in the Context of Digital Skills Development," Sustainability, MDPI, vol. 16(11), pages 1-34, May.
    3. Junyi Wu & Shari Shang, 2020. "Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    4. Doyeon Lee & Keunhwan Kim, 2022. "Public R&D Projects-Based Investment and Collaboration Framework for an Overarching South Korean National Strategy of Personalized Medicine," IJERPH, MDPI, vol. 19(3), pages 1-25, January.
    5. Li, Munan & Wang, Wenshu & Zhou, Keyu, 2021. "Exploring the technology emergence related to artificial intelligence: A perspective of coupling analyses," Technological Forecasting and Social Change, Elsevier, vol. 172(C).

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