In this paper, we characterise the risk-sharing contracts that health authorities can design when they face a regulatory decision on drug pricing and reimbursement in a context of uncertainty. We focus on two types of contracts. On the one hand, the health authority can reimburse the firm for each treated patient regardless of health outcomes (non risk-sharing). Alternatively, the health authority can pay for the drug only when the patient is cured (risk-sharing contract). The optimal contract depends on the trade-off between the monitoring costs, the marginal production cost and the utility derived from treatment. A non-risk-sharing agreement will be preferred by the health authority, if patients who should not be treated impose a relatively low cost to the health system. When this cost is high, the health authority would prefer a risk-sharing agreement for relatively low monitoring costs.
In this article, we model the behavior of a pharmaceutical firm that has marketing authorization for a new therapy believed to be a candidate for personalized use in a subset of patients, but that lacks information as to why a response is seen only in some patients. We characterize the optimal outcome-based reimbursement policy a health authority should follow to encourage the pharmaceutical firm to undertake research and development activities to generate the information needed to effectively stratify patients. Consistent with the literature, we find that for a pharmaceutical firm that does not undertake research and development activities, when the treatment fails, the total price of the drug must be returned to the healthcare system (full penalization). By contrast, if the firm undertakes research and development activities that make the implementation of personalized medicine possible, treatment failure should not be fully penalized. Surprisingly, in some cases, particularly for high-efficacy drugs and small target populations, the optimal policy may not require any penalty for treatment failure. To illustrate the main results of the analysis, we provide a numerical simulation and a graphical analysis.
Although promising, the use of personalized medicine is still under scrutiny as there are important issues demanding a response. Personalized medicine may have an impact in the drug development processes, and contribute to the efficiency and effectiveness of health care delivery. Nevertheless, more accurate statistical and economic information related to tests results and treatment costs as well as additional medical information on the efficacy of the treatments are needed to adopt decisions that incorporate economic rationality.
Background Antibiotics have been overprescribed to treat infectious diseases and have generated antimicrobial resistances that reduce their effectiveness. Following the rationale behind the new paradigm of personalized medicine, point-of-care diagnostic testing (POCT) has been proposed to improve the quality of antibiotic prescription with the aim of reducing antimicrobial resistances. Methods In order to understand whether this recommendation is valid, we create a theoretical economic model to determine under which conditions the expected benefits of using POCT to guide antibiotic prescription are greater than for empiric prescription, where we define the expected benefits as the difference between the economic value of health and the costs of the treatment. We consider the interaction of a group of physicians who express differing levels of uncertainty when prescribing with a firm selling a diagnostic device, and analyse the firm’s pricing policy and the physicians’ prescribing decisions. We allow the physicians to internalize the external costs of antimicrobial resistances. Results We find that the use of POCT reduces the number of antibiotic prescriptions. The reduction in antibiotic prescriptions is higher when physicians internalise the costs of antimicrobial resistances. Physicians with relatively high levels of uncertainty use POCT as they are uncertain about the right treatment for a large proportion of patients. Physicians with low levels of uncertainty prefer to prescribe empirically. The segmentation in the population of physicians regarding the uptake of POCT depends on the distribution of levels of uncertainty across physicians. For each test, the firm charges the marginal production costs of the inputs needed to administer the test, and makes its profit from the sales of the testing devices. Conclusions From a theoretical perspective, our findings corroborate the fact that POCT improve the quality of antibiotic prescription and reduce the number of prescriptions. Nevertheless, their use is not always recommended as empiric therapy may be preferred when uncertainty is low.
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