Policymaker, please consider your needs carefully: does outcomes research in relapsed or refractory multiple myeloma reduce policymaker uncertainty regarding value for money of bortezomib?

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.

Barriers and Opportunities for Implementation of Outcome-Based Spread Payments for High-Cost, One-Shot Curative Therapies

Background: The challenging market access of high-cost one-time curative therapies has inspired the development of alternative reimbursement structures, such as outcome-based spread payments, to mitigate their unaffordability and answer remaining uncertainties. This study aimed to provide a broad overview of barriers and possible opportunities for the practical implementation of outcome-based spread payments for the reimbursement of one-shot therapies in European healthcare systems. Methods: A systematic literature review was performed investigating published literature and publicly available documents to identify barriers and implementation opportunities for both spreading payments and for implementing outcome-based agreements. Data was analyzed via qualitative content analysis by extracting data with a reporting template. Results: A total of 1,503 publications were screened and 174 were included. Main identified barriers for the implementation of spread payments are reaching an agreement on financial terms while considering 12-months budget cycles and the possible violation of corresponding international accounting rules. Furthermore, outcome correction of payments is currently hindered by the need for additional data collection, the lack of clear governance structures and the resulting administrative burden and cost. The use of spread payments adjusted by population- or individual-level data collected within automated registries and overseen by a governance committee and external advisory board may alleviate several barriers and may support the reimbursement of highly innovative therapies. Conclusion: High-cost advanced therapy medicinal products pose a substantial affordability challenge on healthcare systems worldwide. Outcome-based spread payments may mitigate the initial budget impact and alleviate existing uncertainties; however, their effective implementation still faces several barriers and will be facilitated by realizing the required organizational changes.

Uncertainty and Coverage With Evidence Development: Does Practice Meet Theory?

OBJECTIVES

In theory, a successful coverage with evidence development (CED) scheme is one that addresses the most important uncertainties in a given assessment. We investigated the following: (1) which uncertainties were present during the initial assessment of 3 Dutch CED cases, (2) how these uncertainties were integrated in the initial assessments, (3) whether CED research plans included the identified uncertainties, and (4) issues with managing uncertainty in CED research and ways forward from these issues.

METHODS

Three CED initial assessment dossiers were analyzed and 16 stakeholders were interviewed. Uncertainties were identified in interviews and dossiers and were categorized in different causes: unavailability, indirectness, and imprecision of evidence. Identified uncertainties could be mentioned, described, and explored. Issues and ways forward to address uncertainty in CED schemes were discussed during the interviews.

RESULTS

Forty-two uncertainties were identified. Thirteen (31%) were caused by unavailability, 17 (40%) by indirectness, and 12 (29%) by imprecision. Thirty-four uncertainties (81%) were only mentioned, 19 (45%) were described, and the impact of 3 (7%) uncertainties on the results was explored in the assessment dossiers. Seventeen uncertainties (40%) were included in the CED research plans. According to stakeholders, research did not address the identified uncertainty, but CED research should be designed to focus on these.

CONCLUSIONS

In practice, uncertainties were neither systematically nor completely identified in the analyzed CED schemes. A framework would help to systematically identify uncertainty, and this process should involve all stakeholders. Value of information analysis, and the uncertainties that are not included in this analysis should inform CED research design.

Mission-oriented translational cancer research - health economics

Health economics is an integrated aspect of all phases of mission-oriented translational cancer research and should be considered an intrinsic component of any study aimed at improving outcomes for patients and intervention costs. Information about value and value for money of new options for prevention and treatment is needed for decisions about their adoption and use by healthcare systems.

The Challenge of Conditional Reimbursement: Stopping Reimbursement Can Be More Difficult Than Not Starting in the First Place!

BACKGROUND

Conditional reimbursement of new health technologies is increasingly considered as a useful policy instrument. It allows gathering more robust evidence regarding effectiveness and cost-effectiveness of new technologies without delaying market access. Nevertheless, the literature suggests that ending reimbursement and provision of a technology when it proves not to be effective or cost-effective in practice may be difficult.

OBJECTIVES

To investigate how policymakers and the general public in the Netherlands value removing a previously reimbursed treatment from the basic benefits package relative to not including a new treatment.

METHODS

To investigate this issue, we used discrete-choice experiments. Mixed multinomial logit models were used to analyze the data. Compensating variation values and changes in probability of acceptance were calculated for withdrawal of reimbursement.

RESULTS

The results show that, ceteris paribus, both the general public (n = 1169) and policymakers (n = 90) prefer a treatment that is presently reimbursed over one that is presently not yet reimbursed.

CONCLUSIONS

Apparently, ending reimbursement is more difficult than not starting reimbursement in the first place, both for policymakers and for the public. Loss aversion is one of the possible explanations for this result. Policymakers in health care need to be aware of this effect before engaging in conditional reimbursement schemes.

A practical guide for using registry data to inform decisions about the cost effectiveness of new cancer drugs: lessons learned from the PHAROS registry

Decision makers increasingly request evidence on the real-world cost effectiveness of a new treatment. There is, however, a lack of practical guidance on how to conduct an economic evaluation based on registry data and how this evidence can be used in actual decision making. This paper explains the required steps on how to perform a sound economic evaluation using examples from an economic evaluation conducted with real-world data from the Dutch Population based HAematological Registry for Observational Studies. There are three main issues related to using registry data: confounding by indication, missing data, and insufficient numbers of (comparable) patients. If encountered, it is crucial to accurately deal with these issues to maximize the internal validity and generalizability of the outcomes and their value to decision makers. Multivariate regression modeling, propensity score matching, and data synthesis are well-established methods to deal with confounding. Multiple imputation methods should be used in cases where data are missing at random. Furthermore, it is important to base the incremental cost-effectiveness ratio of a new treatment compared with its alternative on comparable groups of (matched) patients, even if matching results in a small analytical population. Unmatched real-world data provide insights into the costs and effects of a treatment in a real-world setting. Decision makers should realize that real-world evidence provides extremely valuable and relevant policy information, but needs to be assessed differently compared with evidence derived from a randomized clinical trial.