The Future of Precision Medicine : Potential Impacts for Health Technology Assessment

Objective Precision medicine allows health care interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information or treatment response. We analyse what developments are expected in precision medicine over the next decade and consider the implications for health technology assessment (HTA) agencies. Methods We perform a pragmatic review of the literature on the health economic challenges of precision medicine, and conduct interviews with representatives from HTA agencies, research councils and researchers from a variety of fields, including digital health, health informatics, health economics and primary care research. Results Three types of precision medicine are highlighted as likely to emerge in clinical practice and impact upon HTA agencies: complex algorithms, digital health applications and ‘omics’-based tests. Defining the scope of an evaluation, identifying and synthesizing the evidence and developing decision analytic models will more difficult when assessing more complex and uncertain treatment pathways. Stratification of patients will result in smaller subgroups, higher standard errors and greater decision uncertainty. Equity concerns may present in instances where biomarkers correlate with characteristics such as ethnicity, whilst fast-paced innovation may reduce the shelf-life of guidance and necessitate more frequent reviewing. Discussion Innovation in precision medicine promises substantial benefits to patients, but will also change the way in which some health services are delivered and evaluated. As biomarker discovery accelerates and AI-based technologies emerge, the technical expertise and processes of HTA agencies will need to adapt if the objective of value for money is to be maintained

Economic evaluations of personalized medicine: existing challenges and current developments

Fatiha H Shabaruddin,1 Nigel D Fleeman,2 Katherine Payne3 1Department of Pharmacy, University of Malaya, Kuala Lumpur, Malaysia; 2Liverpool Reviews and Implementation Group (LRiG), University of Liverpool, Liverpool, UK; 3Institute of Population Health, The University of Manchester, Manchester, UK Abstract: Personalized medicine, with the aim of safely, effectively, and cost-effectively targeting treatment to a prespecified patient population, has always been a long-time goal within health care. It is often argued that personalizing treatment will inevitably improve clinical outcomes for patients and help achieve more effective use of health care resources. Demand is increasing for demonstrable evidence of clinical and cost-effectiveness to support the use of personalized medicine in health care. This paper begins with an overview of the existing challenges in conducting economic evaluations of genetics- and genomics-targeted technologies, as an example of personalized medicine. Our paper illustrates the complexity of the challenges faced by these technologies by highlighting the variations in the issues faced by diagnostic tests for somatic variations, generally referring to genetic variation in a tumor, and germline variations, generally referring to inherited genetic variation in enzymes involved in drug metabolic pathways. These tests are typically aimed at stratifying patient populations into subgroups on the basis of clinical effectiveness (response) or safety (avoidance of adverse events). The paper summarizes the data requirements for economic evaluations of genetics and genomics-based technologies while outlining that the main challenges relating to data requirements revolve around the availability and quality of existing data. We conclude by discussing current developments aimed to address the challenges of assessing the cost-effectiveness of genetics and genomics-based technologies, which revolve around two central issues that are interlinked: the need to adapt available evaluation methods and identifying who is responsible for generating evidence for these technologies. Keywords: pharmacogenetics, pharmacogenomics, cost-effectiveness, economic evaluation, somatic variations, germline variation