Estimating Preferences for Complex Health Technologies: Lessons Learned and Implications for Personalized Medicine

Area of Science:

• Health economics
• Genomic medicine
• Decision science

Background:

• Whole-genome sequencing (WGS) offers extensive genetic information but presents challenges in value assessment.
• WGS results include actionable, clinically valid but not yet actionable, and unknown significance findings.
• Assessing WGS value is complicated by incidental findings unrelated to the initial health concerns.

Purpose of the Study:

• To examine study design challenges in estimating the value of whole-genome sequencing (WGS).
• To propose solutions for key challenges in valuing complex genomic tests.
• To inform the economic evaluation of other advanced health technologies.

Main Methods:

• Utilized stated-preference methods, including contingent valuation and discrete-choice experiments.
• Conceptualized WGS decision-making as sequential choices.
• Elicited preferences for risk reduction and consequences of health interventions.

Main Results:

• Identified three key challenges: layers of uncertainty, endogenous downstream consequences, and mixed utility of test information.
• Developed strategies to address these design challenges in valuing WGS.
• Demonstrated a framework for evaluating complex health technologies with inherent uncertainties.

Conclusions:

• Stated-preference methods can be adapted to value complex genomic tests like WGS.
• Addressing uncertainty and downstream effects is crucial for accurate value estimation.
• The study provides insights for valuing other multi-faceted health technologies.