Determining the impact of modeling additional sources of uncertainty in value-of-information analysis

Financial Estimation of the Uncertainty in Medicine Using Present Value of Medical Fees and a Mortality Risk Prediction Model: a Retrospective Cohort Study

This study aimed to develop a method to enable the financial estimation of each patient’s uncertainty without focusing on healthcare technology. We define financial uncertainty (FU) as the difference between an actual amount of claim (AC) and the discounted present value of the AC (DAC). DAC can be calculated based on a discounted present value calculated using a cash flow, a period of investment, and a discount rate. The present study considered these three items as AC, the length of hospital stay, and the predicted mortality rate. The mortality prediction model was built using typical data items in standard level electronic medical records such as sex, age, and disease information. The performance of the prediction model was moderate because an area under curve was approximately 85%. The empirical analysis primarily compares the FU of the top 20 diseases with the actual AC using a retrospective cohort in the University of Miyazaki Hospital. The observational period is 5 years, from April 1, 2013, to March 31, 2018. The analysis demonstrates that the proportion of FU to actual AC is higher than 20% in low-weight children, patients with leukemia, brain tumor, myeloid leukemia, or non-Hodgkin’s lymphoma. For these diseases, patients cannot avoid long hospitalization; therefore, the medical fee payment system should be designed based on uncertainty. Our method is both practical and generalizable because it uses a small number of data items that are required in standard electronic medical records. This method contributes to the decision-making processes of health policymakers.

Exploring Uncertainty in Economic Evaluations of Drugs and Medical Devices: Lessons from the First Review of Manufacturers' Submissions to the French National Authority for Health

OBJECTIVES

The objective of this paper was to evaluate how uncertainty has been accounted for in the cost-effectiveness analyses (CEAs) submitted by manufacturers to the French National Authority for Health (HAS) and to identify recurring concerns in these submissions.

METHODS

We used a cross-sectional design to evaluate manufacturers' submissions from the beginning of the evaluation process in October 2013 to the end of May 2015 (n = 28). The sources of uncertainty attached to these CEAs were categorized and assessed. Relevant data were extracted independently by two assessors.

RESULTS

Adherence to the HAS reference case was generally considered to be acceptable. Methodological uncertainty and parameter uncertainty were the sources of uncertainty that were most frequently explored by manufacturers. The quality of reporting of deterministic sensitivity analysis and probabilistic sensitivity analysis varied substantially across submissions, with a frequent lack of justification of the plausible range of parameter point estimates in 12 submissions (43 %). Structural uncertainty was explored much less frequently. Concerns related to omission of either important clinical events or relevant health states or extrapolation of the effects of the technology beyond the time horizon of the clinical trials were identified in 16 submissions (57 %).

CONCLUSIONS

This study presented a characterization of the treatment of uncertainty for the first 28 manufacturers' submissions to the HAS. This work identified important concerns regarding the exploration of sources of uncertainty. The findings may help manufacturers to improve the quality of their submissions and may provide useful insights for extending guidelines on uncertainty analysis in CEAs submitted to the HAS.

Use of Value of Information in Healthcare Decision Making: Exploring Multiple Perspectives

BACKGROUND

Value of information (VOI) is a tool that can be used to inform decisions concerning additional research in healthcare. VOI estimates the value of obtaining additional information and indicates the optimal design for additional research. Although it is recognized as good practice in handling uncertainty, it is still hardly used in decision making in the Netherlands.

OBJECTIVE

This paper aims to examine the potential value of VOI, barriers and facilitators and the way forward with the use of VOI in the decision-making process for reimbursement of pharmaceuticals in the Netherlands.

METHODS

Three focus group interviews were conducted with researchers, policy makers, and representatives of pharmaceutical companies.

RESULTS

The results revealed that although all stakeholders recognize the relevance of VOI, it is hardly used and many barriers to the performance and use of VOI were identified. One of these barriers is that not all uncertainties are easily incorporated in VOI, and the results may be biased if structural uncertainties are ignored. Furthermore, not all research designs indicated by VOI may be feasible in practice.

CONCLUSIONS

To fully embed VOI into current decision-making processes, a threshold incremental cost-effectiveness ratio and guidelines that clarify when and how VOI should be performed are needed. In addition, it should be clear to all stakeholders how the results of VOI are used in decision making.