Prioritization of health technologies for insurance coverage is usually based on explicit and implicit criteria. This study presents the development of the multi-criteria decision analysis (MCDA) model, the Iranian Health Insurance Benefit Optimization Model (IR-HIBOM), to inform the design of basic health insurance benefit packages.

An initial set of twenty-nine potential allocation criteria was identified through a review of available evidence and other relevant literature. Review of this set by three specialized panels yielded a final set of thirteen criteria. A cross-sectional survey using the best–worst scaling method was then fielded to 163 health system experts to evaluate their preferences regarding the relative importance of the allocation criteria. The mixed logit method was employed to determine the weight of the relative importance of each criterion. Subsequently, a multilevel criteria scoring framework was defined based on a review of similar models and input from a panel of five expert members of the study team. Finally, model’s appraisal was conducted.

Thirteen criteria, including relative safety, efficacy, disease severity, access to alternative health technologies, budget impacts, cost-effectiveness, quality of evidence, population size, age, job absenteeism, economic status, daily care needs, and ease-of-use/acceptance were selected. Cost-effectiveness and ease-of-use criteria had the highest and lowest relative importance weights, with 30.5 percent and 1 percent, respectively. Furthermore, scores were determined for the several levels of each criterion, and decision rules were defined for the cost-effectiveness and budget impact criteria. The final model’s appraisal, based on weighted scores of thirteen selected technologies, indicated that it was valid and applicable.

The IR-HIBOM demonstrated its potential utility in the health resource allocation.

The adoption of genomic technologies in the context of hospital-based health technology assessment presents multiple practical and organizational challenges.

This study aimed to assist the Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa) decision makers in analyzing which acute myeloid leukemia (AML) genomic panel contracting strategies had the highest value-for-money.

A tailored, three-step approach was developed, which included: mapping clinical pathways of AML patients, building a multicriteria value model using the MACBETH approach to evaluate each genomic testing contracting strategy, and estimating the cost of each strategy through Monte Carlo simulation modeling. The value-for-money of three contracting strategies – “Standard of care (S1),” “FoundationOne Heme test (S2),” and “New diagnostic test infrastructure (S3)” – was then analyzed through strategy landscape and value-for-money graphs.

Implementing a larger gene panel (S2) and investing in a new diagnostic test infrastructure (S3) were shown to generate extra value, but also to entail extra costs in comparison with the standard of care, with the extra value being explained by making available additional genetic information that enables more personalized treatment and patient monitoring (S2 and S3), access to a broader range of clinical trials (S2), and more complete databases to potentiate research (S3).

The proposed multimethodology provided IPO Lisboa decision makers with comprehensive and insightful information regarding each strategy’s value-for-money, enabling an informed discussion on whether to move from the current Strategy S1 to other competing strategies.