Evidence map›Paper›PMID 28212968›Full record

ArticleValue in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research2017

"What Goes Around Comes Around": Lessons Learned from Economic Evaluations of Personalized Medicine Applied to Digital Medicine.

Kathryn A Phillips, Michael P Douglas, Julia R Trosman, Deborah A Marshall

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
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

4 authors.

Kathryn A PhillipsDepartment of Clinical Pharmacy, Center for Translational and Policy Research on Peronalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA; Philip R. Lee Institute for Health Policy, University of California San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. Electronic address: Kathryn.Phillips@ucsf.edu.
Michael P DouglasDepartment of Clinical Pharmacy, Center for Translational and Policy Research on Peronalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA.
Julia R TrosmanDepartment of Clinical Pharmacy, Center for Translational and Policy Research on Peronalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA; Center for Business Models in Healthcare, Chicago, IL, USA; Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Deborah A MarshallDepartment of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.

Funding

Risk-Benefit Trade-Offs for Whole Genome SequencingR01HG007063 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PHILLIPS, KATHRYN A · 2013 to 2016
$2.5M
NHGRI NIH HHS R01 HG007063
6 · The paper itself

Abstract

backgroundThe growth of "big data" and the emphasis on patient-centered health care have led to the increasing use of two key technologies: personalized medicine and digital medicine. For these technologies to move into mainstream health care and be reimbursed by insurers, it will be essential to have evidence that their benefits provide reasonable value relative to their costs. These technologies, however, have complex characteristics that present challenges to the assessment of their economic value. Previous studies have identified the challenges for personalized medicine and thus this work informs the more nascent topic of digital medicine.

objectivesTo examine the methodological challenges and future opportunities for assessing the economic value of digital medicine, using personalized medicine as a comparison.

methodsWe focused specifically on digital biomarker technologies and multigene tests. We identified similarities in these technologies that can present challenges to economic evaluation: multiple results, results with different types of utilities, secondary findings, downstream impact (including on family members), and interactive effects.

resultsUsing a structured review, we found that there are few economic evaluations of digital biomarker technologies, with limited results.

conclusionsWe conclude that more evidence on the effectiveness of digital medicine will be needed but that the experiences with personalized medicine can inform what data will be needed and how such analyses can be conducted. Our study points out the critical need for typologies and terminology for digital medicine technologies that would enable them to be classified in ways that will facilitate research on their effectiveness and value.

Indexed as

BiomarkersCost-Benefit AnalysisEvidence-Based PracticeHumansInformation SystemsPrecision MedicineRemote Sensing TechnologyTechnology Assessment, BiomedicalTelemedicineBiomarkerscost-benefit analysis methodsdigital medicineindividualized medicinepersonalized medicine

Identifiers

PMID28212968
PMCPMC5319740

What OpenQuestion holds

Textmetadata
LicenceTDM
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.