Health Economics Tools and Precision Medicine: Opportunities and Challenges

Abstract

Precision medicine – individualizing care for patients and addressing variations in treatment response – is likely to be important in improving the nation’s health in a cost-effective manner. Despite this promise, widespread use of precision medicine, specifically genomic markers, in clinical care has been limited in practice to date. Lack of evidence, clear evidence thresholds, and reimbursement have been cited as major barriers. Health economics frameworks and tools can elucidate the effects of legal, regulatory, and reimbursement policies on the use of precision medicine while guiding research investments to enhance the appropriate use of precision medicine. Despite the capacity of economics to enhance the clinical and human impact of precision medicine, application of health economics to precision medicine has been limited – in part because precision medicine is a relatively new field – but also because precision medicine is complex, both in terms of its applications and implications throughout medicine and the healthcare system. The goals of this review are several-fold: (1) provide an overview of precision medicine and key policy challenges for the field; (2) explain the potential utility of economics methods in addressing these challenges; (3) describe recent research activities; and (4) summarize opportunities for cross-disciplinary research.

Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps

“…While most applications of HEOR and CEA to precision medicine have focused on pharmacogenomics 37 to assess the value of precision testing to diagnose patients and align them with the appropriate targeted oncologic agent(s), 7 there is still a paucity of published research evaluating which precision patient subgroups can benefit from a particular cost-effective treatment regimen. 6 Some of the reasons are methodological and relate to our core understandings of bias and confounding.…”

“…A prime example of this is the Personalized Risk Information in Cost-Effectiveness Studies (PRICES) project, funded by the National Institutes of Health. 7 Using risk models, the project aimed to understand how each patient's individualized risk of experiencing adverse health outcomes can help clinicians better tailor care and resources. 43 Additionally, ML analyses seem to be gaining traction as the toolbox of interest to reanalyze randomized clinical trials, where the risk of treatment selection bias may be somewhat limited.…”

“…6,7 This does not mean, however, that development work is not already being conducted to identify opportunities and foundational frameworks for the application of health economics tools to precision medicine, as suggested by Veenstra et al in Table 1. 7 As the economic evaluation of observational data and generation of real-world evidence will remain an important topic in precision medicine, 7 expert opinion suggests that contemporary advanced predictive algorithms such as machine learning (ML) should be explored as advanced tools for decision-making and cost-effectiveness analysis, 6 consistent with those particular domains outlined in Table 1. For example, when paired with clinical opinion from a health care professional, ML results can produce valuable insights into accelerating clinical workflow and optimizing therapeutic interventions and resource allocation.…”

“…Yet, practical and methodological challenges exist in using medical big data for economic evaluations of precision medicine. 6 , 7 Challenges relate to the presence of bias and confounding in observational studies, handling of missing data and clinical miscoding, absence of available health state utility values (used to calculate quality-adjusted life years [QALYs]) for population subgroups of interest, 6 as well as lack of clear evidence on willingness-to-pay thresholds and reimbursement. 7 While these challenges are also present in traditional applications of health technology assessment, which perhaps may explain why patient heterogeneity is rarely looked into in cost-effectiveness assessments, 8 making causal inferences in the context of multiple identified P-HEOR cohorts could compound the problem.…”

“… 6 Literature reviews have also brought up the point that the application of HEOR to precision medicine is still in its infancy. 6 , 7 This does not mean, however, that development work is not already being conducted to identify opportunities and foundational frameworks for the application of health economics tools to precision medicine, as suggested by Veenstra et al in Table 1 . 7 …”