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Health technology assessment (HTA) can be used to make healthcare systems more equitable and efficient. Advances in precision oncology are challenging conventional thinking about HTA. Precision oncology advances are rapid, involve small patient groups, and are frequently evaluated without a randomized comparison group. In light of these challenges, mechanisms to manage precision oncology uncertainties are critical. We propose a life-cycle HTA framework and outline supporting criteria to manage uncertainties based on real world data collected from learning healthcare systems. If appropriately designed, we argue that life-cycle HTA is the driver of real world evidence generation and furthers our understanding of comparative effectiveness and value. We conclude that life-cycle HTA deliberation processes must be embedded into healthcare systems for an agile response to the constantly changing landscape of precision oncology innovation. We encourage further research outlining the core requirements, infrastructure, and checklists needed to achieve the goal of learning healthcare supporting life-cycle HTA. [ABSTRACT FROM AUTHOR]

Patients and physicians want rapid access to novel cancer therapies, particularly for advanced cancers with poor prognoses. The US FDA, the European Medicines Agency (EMA), and Health Canada (HC) all have pathways to accelerate and facilitate patient access to novel therapies. In Canada, after receiving HC approval, cancer drugs are evaluated by the Canadian Agency for Drugs and Technologies in Health (CADTH), specifically the pan-Canadian Oncology Drug Review (pCODR). The concern is that recommending drugs based on limited evidence can lead to prescribing treatment that may harm patients or that may not provide any meaningful benefit. [Extracted from the article]

This paper has two objectives, first to review the relevant literature concerning the social importance of severity of pre-treatment condition, and second to present the results of a new analysis of the relationship between social value, individual assessment of health improvement and the severity of illness. The present study differs methodologically from others reported in the literature. The underlying hypothesis is that members of the public have an aversion to patients being in a severe health state irrespective of the reason for their being there, and that this aversion will affect the social valuation of a health program after taking account of the magnitude of the health improvement. This effect will be observable in a program which (compared to another) takes a person out of a severe health state-the usual case discussed in the literature-or in a program which (compared to another) leaves a person in a severe health state. The present study tests this second implication of the hypothesis. We present data consistent with the view that after taking account of health improvement, health programs are preferred which do not leave people in severe health states. Alternative explanations are considered and particularly the possibility that data reflect a social preference for individuals achieving their health potential. Both explanations imply the need to reconsider the rules for prioritising programs. In this analysis, Person Trade-Off (PTO) scores are used to measure social preferences ('value' or 'social utility') and Time Trade-Off (TTO) scores are used to measure individual assessments of health improvement and initial severity. Econometric results suggest that severity is highly significant and may more than double the index of social value of a health service. [ABSTRACT FROM AUTHOR]

This paper re-examines criticisms of cross-sectional methods used to test for supplier-induced demand (SID) and re-evaluates the empirical evidence using data from Australian medical services. Cross-sectional studies of SID have been criticised on two grounds. First, and most important, the inclusion of the doctor supply in the demand equation leads to an identification problem. This criticism is shown to be invalid, as the doctor supply variable is stochastic and depends upon a variety of other variables including the desirability of the location. Second, cross-sectional studies of SID fail diagnostic tests and produce artefactual findings due to model misspecification. Contrary to this, the re-evaluation of cross-sectional Australian data indicate that demand equations that do not include the doctor supply are misspecified. Empirical evidence from the re-evaluation of Australian medical services data supports the notion of SID. Demand and supply equations are well specified and have very good explanatory power. The demand equation is identified and the desirability of a location is an important predictor of the doctor supply. Results show an average price elasticity of demand of 0.22 and an average elasticity of demand with respect to the doctor supply of 0.46, with the impact of SID becoming stronger as the doctor supply rises. The conclusion we draw from this paper is that two of the main criticisms of the empirical evidence supporting the SID hypothesis have been inappropriately levelled at the methods used. More importantly, SID provides a satisfactory, and robust, explanation of the empirical data on the demand for medical services in Australia. [ABSTRACT FROM AUTHOR]