Working Paper: NBER ID: w24095
Authors: Mark V. Pauly
Abstract: This paper describes current pattern of insurance coverage for precision medicines and, especially, companion diagnostics and explores what coverage would improve efficiency. We find that currently coverage is common for tests and treatments with clinical acceptance used at high volumes but is haphazard across both private insurers and Medicare for precision medicines in general. Analysis of the case of homogenous patient preferences finds that discovery and use of the test that converts an ordinary drug into a precision drug can either increase or decrease total spending, and might call for full or no coverage of test and treatments. Heterogeneity in marginal benefits from testing and treatment can call for partial coverage. Finally, varying threshold levels for diagnostic test results can lead to a demand curve to test and treatment that calls for partial cost sharing. Numerical examples and case studies of several test-treatment combinations illustrate these points.
Keywords: Precision Medicine; Insurance Coverage; Cost Sharing; Genetic Testing; Health Economics
JEL Codes: I11; I13; O32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
Cause | Effect |
---|---|
insurance coverage for precision medicine (I13) | healthcare outcomes (I11) |
coverage of tests and treatments (I13) | total spending (H56) |
effectiveness of the treatment (C90) | coverage of the test (C90) |
patient preferences (I11) | effectiveness of the treatment (C90) |
costs associated with ineffective treatments (I12) | effectiveness of the treatment (C90) |
varying thresholds for diagnostic test results (C52) | demand curve for partial cost sharing (D41) |
insurance design (G52) | relationship between test results and treatment decisions (C52) |
heterogeneity in patient responses to treatments (C21) | optimal coverage (C61) |
specific context of the treatment and patient population (I11) | optimal coverage (C61) |