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Value Health. 2017 Jan;20(1):47-53. doi: 10.1016/j.jval.2016.08.736.

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

Author information

1
Department 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.
2
Department of Clinical Pharmacy, Center for Translational and Policy Research on Peronalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA.
3
Department 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.
4
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.

Abstract

BACKGROUND:

The 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.

OBJECTIVES:

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

METHODS:

We 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.

RESULTS:

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

CONCLUSIONS:

We 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.

KEYWORDS:

cost-benefit analysis methods; digital medicine; individualized medicine; personalized medicine

PMID:
28212968
PMCID:
PMC5319740
DOI:
10.1016/j.jval.2016.08.736
[Indexed for MEDLINE]
Free PMC Article

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