Evidence map›Paper›PMID 31556828›Full record

ArticleJournal of managed care & specialty pharmacy2019

A Strategy to Support Efficient Development and Use of Innovations in Personalized Medicine and Precision Medicine.

Louis P Garrison, Adrian Towse

Abstract read
In one paragraph

Article in Journal of managed care & specialty pharmacy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Toward Sex-Specific Biomaterials Innovation: A Perspective.ACS biomaterials science & engineering · 2025
    Review
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  7. Exploring interdisciplinary perspectives on the implementation ofJournal of Alzheimer's disease : JAD · 2025
    Article
  8. Review
  9. Review
  10. Artificial Intelligence Opportunities to Guide Precision Dosing Strategies.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2024
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. HTA methodology and value frameworks for evaluation and policy making for cell and gene therapies.The European journal of health economics : HEPAC : health economics in prevention and care · 2020
    Article
  16. Article
  17. Insights From a Temporal Assessment of Increases in US Private Payer Coverage of Tumor Sequencing From 2015 to 2019.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2020
    Article
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Louis P GarrisonThe Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, Seattle.
Adrian TowseThe Office of Health Economics, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This analysis addresses an important policy question: "What are potentially transformative strategies and methods to define and measure value at all levels of decision making that are aligned with personalized medicine/precision medicine (PM/PrM)?" To approach this question, we build on our previous work with PM and recent work as members of ISPOR's Special Task Force on Value Assessment Frameworks. We focused on 3 key challenges: (1) the need for a broader concept of value for PM/PrM; (2) the development of appropriate concepts and methods for measuring and splitting the innovation reward for PM/PrM; and (3) the application of this expanded concept to align value assessment in the hierarchy of related decision contexts, from formulary inclusion and value-based pricing to shared patient-provider decision making. Our working assumption was that the goal of policy affecting PM/PrM technologies should be to promote "dynamic efficiency"-the optimal development, adoption, and use of these technologies. For this purpose, we laid out 6 basic cross-cutting policy principles. First, rewards for innovation should be value-based and flexible over time and across indications as new evidence emerges. Second, the concept of value should be based on the microeconomic concept of willingness to pay and should include, at its core, the value of health gain-in terms of the length and quality of life- but also going beyond this to include other uncertainty-related elements. Third, innovation rewards should be modifiable given new real-world evidence and knowledge. Fourth, the scientific information embedded in PM and PrM technologies is a global public good that should be financed globally through differential pricing. Fifth, reward systems need to recognize that the diagnostic technologies that make PM and PrM possible are technically "economic complements." Sixth, splitting the rewards among complementary inputs is arbitrary in a static sense but matters dynamically. For PM/PrM value assessment, we argue for including the "value of knowing"; the need for indication-specific pricing; the importance of updating the economic evaluation and pricing over the product life cycle; addressing the new challenge posed by next-generation sequencing and high-cost combination therapies; and aligning value metrics across different decision contexts based on health benefit and patient-centered utility. DISCLOSURES: No previous outside funding supported this policy analysis. The authors have nothing to disclose. The authors are grateful to have received first place in the PhRMA Foundation-Personalized Medicine Coalition 2018 Value Assessment Challenge Award for this work.

Indexed as

Organizational InnovationPrecision MedicineDecision Making, OrganizationalHumansMedication Therapy ManagementPharmaceutical ServicesPolicy

Identifiers

PMID31556828
PMCPMC10398297

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.