Introduction
A critical and challenging issue confronting health technologies decision making lies in the imperative to be more accountable for healthcare spending, particularly within publicly funded health systems. The rapid escalation of health costs necessitates making judicious decisions regarding the coverage of new health technologies and reconsidering currently covered technologies based on updated evidence. Health technology assessment (HTA) has evolved in response to this need (Reference Abelson, Giacomini, Lehoux and Gauvin1).
An advantage of HTA and related economic evaluations is their use of incremental cost-effectiveness ratios (ICERs) for comparing different health technologies and to establish ICER thresholds for acceptance of a health technology into health benefits packages. However, as the ICER considers only limited criteria, it may neglect other important ones that are relevant for setting resource allocation priorities among health technologies. This limitation sometimes leads to lowering the relative priority of health technologies deemed more valuable by some stakeholders (Reference Cleemput, Neyt, Thiry, De Laet and Leys2).
Empirical evidence indicates that, when setting priorities for health technologies, policymakers deem as important such criteria as efficiency, equity (e.g., prioritizing severely ill or impoverished individuals), equality, financial protection, and political considerations (Reference Musgrove3–Reference Youngkong, Kapiriri and Baltussen5). However, policymakers face challenges in simultaneously considering such diverse criteria for decision making due to the uneven availability of supporting evidence, variations in the importance of criteria, potential conflicts among criteria, and difficulties in handling different types of information, leading to the risk of cognitive overload (Reference Baltussen and Niessen6). Multicriteria decision analysis (MCDA) offers an approach to overcoming these limitations (Reference Baltussen and Niessen6). MCDA allows for the identification of a comprehensive set of criteria, evaluating health technologies based on these criteria in a performance matrix, and then qualitatively or quantitatively assessing the matrix to rank the desired health technologies (Reference Baltussen and Niessen6). In the context of prioritizing health technologies, MCDA is an objective, transparent, evidence-based method that aids decision making by considering and evaluating multiple objectives, aspects, and criteria (Reference Belton and Stewart7).
In Iran’s largely integrated and centralized healthcare system, the Ministry of Health and Medical Education (MoHME) is responsible for policymaking, prioritization, planning, and monitoring activities across the public and private sectors (Reference Doshmangir, Bazyar, Majdzadeh and Takian8). The HTA function in Iran was formalized in 2007 with the establishment of a secretariat under the MoHME. Its mission is to systematically evaluate health technologies to support evidence-based decision making, focusing on aspects such as safety, effectiveness, cost-effectiveness, and broader social, ethical, and legal implications. HTA projects are conducted through partnerships with universities and research centers, supported by a scientific committee that prioritizes topics and oversees evaluations. Additionally, capacity building has been a significant focus, with workshops and a master’s degree program in HTA offered at leading medical universities across the country (Reference Arab-Zozani, Sokhanvar, Kakemam, Didehban and Hassanipour9).
The health system’s financing and service provision in Iran follow a hybrid structure, featuring an integrated healthcare network predominantly funded by the government and managed through medical sciences universities under the MoHME’s supervision. Social health insurance is a key element of Iran’s healthcare system, providing health services to about 90 percent of the population (Reference Hassanzadeh10). Consequently, the compilation and revision of insurance benefits packages and priorities for inclusion of various health technologies in health insurance benefits packages are conducted in tandem. As such, it is imperative to employ practical, transparent, and evidence-based tools.
In developing an MCDA-based methodology, the Iranian Health Insurance Benefit Optimization Model (IR-HIBOM), this study demonstrates a practical tool for evaluating and prioritizing health technologies. These efforts aim to optimize the allocation of public resources for compiling and revising benefit packages and supporting responsible policymaking and decision making, accordingly.
Methods
The sequential phases undertaken to develop and demonstrate the potential utility of the IR-HIBOM were as follows.
Extraction and selection of allocation criteria
In the initial phase, the identification and selection of allocation criteria relevant to health technologies prioritization for resource allocation were performed. Various methods are typically employed to identify criteria and other characteristics in preference assessment studies, including one or more of reviewing scientific literature, group discussions, interviews, and expert panels. In this study, a set of criteria from various relevant published studies and other documentation were extracted and summarized through a literature search and evidence review. Notably, given recent published comprehensive reviews of different allocation criteria (Reference Mobinizadeh, Raeissi, Nasiripour, Olyaeemanesh and Tabibi11;Reference Tromp and Baltussen12), a new systematic review was not conducted in this study. However, an additional set of criteria representing social aspect was extracted based on a survey of Iranian adults conducted by members of the study team (Reference Darvishi, Daroudi, Yaseri and Sari13).
Initially, twenty-nine independent criteria were identified. A single expert panel consisting of five study team members with backgrounds in health economics, health policy, health management, HTA, and epidemiology was established for the review and selection of criteria. Given the large number of candidate criteria, the panel convened across three sessions. The criteria were first categorized into clinical, economic, and social dimensions. During the expert panel sessions, each dimension was thoroughly discussed, and the related criteria were refined and finalized with precise definitions. The finalized set of criteria encompassed conventional criteria from similar studies and novel criteria introducing social aspects not previously included in similar models. In each expert panel session, a subset of criteria was reviewed and discussed. Some criteria were eliminated due to conceptual overlap or low perceived relevance, ensuring that each criterion covered a distinct dimension, while others were retained for further consideration. The consensus process was implemented through a structured prioritization approach: after open discussion, each panel member independently ranked the criteria based on their own preferences and judgment. These individual rankings were then aggregated to produce a collective prioritization. The final set of thirteen criteria was determined based on this multiround, consensus-driven process.
Preference assessment and ranking of allocation criteria
After the criteria were finalized, the preferences of health system experts in Iran regarding their relative importance for financing health technologies were assessed via an electronic questionnaire. These experts included about 350 qualified individuals, comprising faculty members in healthcare management, health economics, HTA, pharmacoeconomics, physicians, and other selected managers and researchers in the health system. An electronic questionnaire link, accompanied by additional explanations, was distributed via email or social media. To ensure the relevance and credibility of the preference data, expert participants were purposefully selected rather than recruited through open invitations. A complete list of faculty members in these fields was compiled using the national scientometrics database, and email invitations containing the questionnaire link and study details were sent to all of them. In addition, the questionnaire was shared individually via email or secure messaging with a selected group of physicians, pharmacists, and others who held managerial experience within the health system. No open or public invitations were issued through social media platforms; participation was limited to those specifically targeted based on their expertise and institutional roles.
For university faculty members, the questionnaire link, along with detailed explanations and the study objectives, was sent via email. For those faculty members who did not respond to the email, the questionnaire was later shared through social media platforms, specifically WhatsApp and Telegram, to improve the response rate. For others among the qualified individuals, the questionnaire was distributed directly via WhatsApp and Telegram. These platforms were chosen due to their widespread use and accessibility among the intended participants. Ultimately, 171 individuals (48.9 percent) responded to the questionnaire, among whose responses, 163 responses (46.6 percent) were considered valid after eliminating incomplete submissions. All respondents completed the electronic questionnaire independently and remotely. No interviewer facilitation or real-time administration was involved. This self-administered online format allowed respondents to complete the questionnaire at their convenience while maintaining standardization and consistency across responses.
The study determined a minimum necessary time required for participants to thoroughly understand and respond accurately to the questions. As such, eight questionnaires that were completed in a shorter duration than that minimum were deemed invalid and excluded from the final analysis to maintain the integrity of the study results.
Best–worst scaling method (BWS)
The BWS method was employed to assess the preferences of the 163 health system experts regarding criteria for prioritizing the financing of health technologies. Utilizing the BWS, experts were asked to identify the best and worst criteria under various choice tasks, engaging in pairwise comparisons between each of these two indicators (best and worst) and other criteria. Subsequently, a maximum–minimum problem was formulated and solved to ascertain the weight of different criteria (Reference Flynn14). In this context, the best criterion refers to the one deemed more important than other criteria in any one choice task in the prioritization of technologies for health budget allocation, while the worst criterion is considered the least important compared to other criteria in any one choice task.
Three main types of BWS methods vary in the complexity of the cases or options considered. The choice of method depends on the nature of the study. In this study, the BWS object case method was employed, where the researcher seeks relative values associated with each criterion, without decomposing criteria into their more detailed levels and characteristics (Reference Louviere, Flynn and Marley15).
Designing scenarios
To match the number of finalized criteria, the questionnaire consisted of thirteen choice tasks, each of which offered a comparison among a subset of four of the thirteen criteria, with each criterion appearing in four choice tasks. The question text remained consistent across all choice tasks. In each question, respondents were required to identify the most important (i.e., “best”) and least important (i.e., “worst”) criteria.
The optimal and balanced selection of choice tasks was achieved using R version 4.2.0 for Windows software.
Online supplementary file 1 presents the full questionnaire and choice tasks related to this section.
Data analysis
The collected data underwent analysis using the mixed logit method. The mixed logit model accounts for preference heterogeneity, assuming that parameters are randomly distributed across the population. The β or odds ratio (OR) coefficient signifies the relative preference or importance of a factor (criterion) compared to a reference factor (criterion). ORs reflect changes in likelihood of choice of each criterion based on “best–worst” differences, relative to the reference criterion. In this instance, the reference criterion was relative safety. Alongside β or ORs, the model incorporates a standard deviation capturing unexplained variation around the mean (Reference Guo and Shen16). Based on this method, the thirteen criteria were ranked according to the preferences of Iran’s health system experts. Excel 2016 and Stata 17 software were employed for analyses in this section.
Completing the IR-HIBOM elements
Determining the weight of relative importance of criteria
Coefficients obtained from the mixed logit analysis in the previous phase, scaled from 0 to 100, were used to determine the relative weights of the thirteen allocation criteria.
Determining and defining the framework of scoring levels of criteria and decision rules
Defining the scoring levels of the criteria and framework of scoring involved reviewing scoring levels of similar criteria in past models and studies. The study team, in several expert panels, finalized the scoring levels and framework of the model.
The process included:
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1. Drafting scoring levels based on objectives, criterion nature, and available evidence.
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2. Five iterations of review and editing by the expert panels (each comprising five specialists from various fields: health economics, health policy, health management, HTA, and epidemiology).
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3. Finalizing scoring levels and framework.
In the first four sessions, three to four criteria were reviewed per session, and the scoring system and its framework were defined. The fifth session was dedicated to summarizing and finalizing all of the discussed items.
For new criteria (those covering social aspects), scoring levels and definitions were determined by the study team, aiming for precision and quantifiability. Three to five levels were assigned to each criterion based on its nature, and scores were allocated to each level. Due to the differing numbers of defined levels of criteria, scoring across the levels were normalized to the range of 0–1.
Some criteria used a straightforward approach (e.g., low, medium, high), while others included decision rules for flexibility and optimization.
Model appraisal
Case examples of thirteen health technologies previously assessed (in 2021 and 2022) for insurance coverage by the HTA office of the Ministry of Health of Iran were used to appraise the potential utility of IR-HIBOM (17). These case examples were selected to represent a diversity of health care (e.g., medicines, supportive care, screening) and to probe the range of allocation criteria. The health technologies and their target groups and indications included:
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1. HT 1: Medicine in the treatment of metastatic breast cancer
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2. HT 2: Medicine in the treatment of metastatic breast cancer
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3. HT 3: Ablation technology in the treatment of atrial fibrillation
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4. HT 4: Ablation technology in the treatment of atrial fibrillation
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5. HT 5: Rehabilitation in the management of relapsing remitting multiple sclerosis (RRMS)
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6. HT 6: Medicine in the treatment of advanced melanoma
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7. HT 7: Medicine in the treatment of advanced melanoma
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8. HT 8: Medicine in the treatment of primary progressive multiple sclerosis (PPMS)
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9. HT 9: Medicine in the treatment of relapsing remitting multiple sclerosis (RRMS)
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10. HT 10: Technology for sedation in intensive care unit (ICU)
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11. HT 11: Technology for sedation in intensive care unit (ICU)
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12. HT 12: Technology in osteoporosis screening
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13. HT 13: Technology in osteoporosis screening
Due to proprietary considerations, the generic and commercial names of these technologies are not identified. Model evaluation using the MCDA model was conducted through the simple additive weighting (SAW) technique. This method involves forming a decision matrix and calculating the final rank after normalizing and weighting the criteria (Reference Hwang and Yoon18).
The process included:
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• Extracting and summarizing relevant general evidence from existing HTA reports.
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• Determining the score of each technology in each criterion based on evidence and expert opinions of the study team.
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• Completing the performance matrix of the model by incorporating these scores.
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• Assigning a total weighted score to each technology based on the estimated importance weight of each criterion.
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• Ranking all technologies was conducted based on their total weighted score. In this manner, technologies with higher total weighted scores were prioritized for resource allocation, while those with lower scores were assigned lower priorities.
To compare the rankings based on the IR-HIBOM method with those determined by cost-effectiveness criteria, we also ranked selected technologies based solely on their cost-effectiveness index (cost-effectiveness ratio). Given that the HTA reports for these technologies span different years, we calculated each technology’s average cost-effectiveness ratio to gross domestic product per capita ratio (i.e., ACER/GDP ratio) for the respective years of study. ACER values were calculated based on information present in the HTA reports.
Results
Final criteria
From the pool of potential allocation criteria, thirteen encompassing diverse aspects were ultimately selected in the initial phase. Table 1 shows a comprehensive list of the criteria along with their descriptions.
Table 1. Criteria and descriptions
These criteria served as the foundation for subsequent phases in the study, providing a comprehensive framework for the development and evaluation of the IR-HIBOM.
Determination and estimation of criteria importance weights
To ascertain and estimate the relative weights of the thirteen criteria, the preferences of health system experts were assessed using the BWS method. The resulting rankings and valuations of the criteria were derived from this process.
Characteristics of health system experts
The average age of the 163 health system experts was 59.5 years, ranging from 30 to 75 years. Men comprised 57.7 percent of the experts; health economists accounted for 18.4 percent, constituting the single largest share of the experts. Regarding professional work experience, 21.5 percent reported less than 5 years, while 20.3 percent had more than 25 years. Supplementary Table S1 provides a comprehensive breakdown of this group.
BWS findings and estimating the criteria’s importance weights
Table 2 presents the findings from the mixed logit analysis, including the frequency of each criterion being selected as the most and least important. ORs were computed for each criterion. The ORs were estimated in comparison to the first criterion in the dataset (i.e., relative safety), and the reported significance was based on this referent.
Table 2. BWS analysis results and ranking of study criteria
Note: Odds ratios reflect changes in likelihood of choice based on “best–worst” differences, relative to the reference criterion.
The criteria rankings place cost-effectiveness (OR = 5.58) as the most important criterion and ease-of-use (OR = 0.19) as the least important.
For the BWS results, the importance weight of each criterion was calculated based on the OR coefficients. The estimated importance weights, rescaled within the range of 0–100, are presented in Table 2, where cost-effectiveness holds the highest weight at 30.5 percent and ease-of-use has the lowest weight at 1 percent.
Levels of criteria and framework of scoring
Following the determination of criterion weights, scoring levels were established, considering three to five levels assigned to each criterion. Adjustments were made within the range of 0–1 to normalize the scores. The detailed scoring levels for criteria are presented in Table 3, and additional explanations, including the scoring framework and the decision rules for two criteria, can be found in Table 4. The tables indicate that certain criteria in the developed model necessitate scoring by a specialized committee of the study team due to the diverse nature of health technologies.
Table 3. Scoring levels framework for criteria in the IR-HIBOM
Table 4. Instructions and decision rules for criteria scoring in the IR-HIBOM
Additional details of the scoring framework for each criterion are provided in the online supplementary file 2.
Completing the performance matrix and appraising the model
The thirteen technologies were scored, drawing upon available evidence and applying the scoring levels of the thirteen criteria in the model accordingly.
The resulting performance matrix of the model is presented in Supplementary Table S2. Following the completion of the performance matrix, each technology’s total weighted score was computed within the model. Subsequently, a ranking was established, prioritizing thirteen health technologies with higher total scores for resource allocation. The scoring and rankings are detailed in Table 5.
Table 5. Appraisal of the IR-HIBOM and ranking of selected health technologies
Among the ranked technologies, HT 12 for osteoporosis screening secured the top priority with a score of 82.4. HT 10 for sedation in the ICU had the lowest priority with a score of 42.4.
To provide a comparative perspective on the evaluation results derived from the designed model and those based on cost-effectiveness criteria, Table 5 also displays the scores and rankings of technologies based only on the ACER/GDP per-capita criteria. This comparison reveals significant variation in the rankings between the two approaches. It is important to acknowledge that the evidence regarding the cost-effectiveness of the screening technologies HT 12 and HT 13 was presented based on the cost per true positive/negative cases identified, rendering practical comparability with other technologies in this aspect challenging.
In Iran, the primary authority responsible for the regulation, development, and revision of social health insurance packages is the Secretariat of the Supreme Council of Health Insurance. The results of this study have been presented to the Secretariat for its consideration for potential use in some form for helping to support future revisions of health insurance benefit packages.
Discussion
In this study, a MCDA model, IR-HIBOM, was developed using a systematic approach, combining quantitative and qualitative methods across successive stages. The goal was to demonstrate a methodology for prioritizing health technologies for the allocation of healthcare financial resources and the formulation of health insurance benefit packages. The comprehensive nature of the model’s design involved determining relevant criteria, gauging perspectives of health system experts, assigning values, estimating the criteria’s relative importance weights, and establishing scoring levels and decision rules.
Ultimately, thirteen criteria were incorporated into the final modeling phase, encompassing various dimensions related to the model’s purpose. Various dimensions related to health technology nature, disease severity, economic characteristics, opportunity cost, equity considerations, and the quality of supporting evidence. In comparison to models with similar purposes, IR-HIBOM is relatively comprehensive in its scope of nonredundant criteria and diverse dimensions. Among other similar models, the Netherlands health technology prioritization model considered criteria such as disease burden, potential health benefits, patient numbers, health technology costs, long-term financial consequences, and impact on health system policies (Reference Oortwijn, Banta, Vondeling and Bouter19). Canada’s Evidence and Value: Impact on Decision Making (EVIDEM) HTA prioritization model uses criteria similar to those in the present model, that is, improving effectiveness, safety, alternative health technologies, budget impacts, completeness and consistency of available evidence, population size, disease severity, and cost-effectiveness (Reference Goetghebeur, Wagner, Khoury, Rindress, Grégoire and Deal20). Lithuania’s prioritization model considered such criteria as health benefits, quality and quantity of evidence, evaluation timing, expected benefits for policymakers, and social, legal, and ethical considerations (Reference Jankauskiene and Petronyte21). Notably, IR-HIBOM combines quantitative and qualitative elements. A model developed by Mobinizadeh et al. in Iran in 2016 (Reference Mobinizadeh, Raeissi, Nasiripour, Olyaeemanesh and Tabibi11) and updated in 2021 (Reference Mobinizadeh, Mohamadi, Arman, Nasiripour, Olyaeemanesh and Mohamadi22) for selecting and prioritizing HTA topics shares notable similarities with the present model. Common criteria include health benefits at the population level, the size of the vulnerable population, availability of alternative technologies, budget impacts, financial protection, and the quality of evidence. “The Mobinizadeh et al. model incorporates safety criteria as a veto criterion (a condition or threshold in decision-making models, that can outright reject an alternative regardless of its performance in other criteria) and decision rules pertaining to scoring ‘uncertainty of cost-effectiveness’. The current model aligns closely with the Mobinizadeh et al. model, particularly regarding types of criteria and inclusion of decision rules.”
A distinctive feature of this study is the use of the BWS statistical method for weighting the allocation criteria. This method, involving a larger pool of experts specializing in health management and policymaking, sets it apart from similar studies. The model’s results reveal that the cost-effectiveness criterion holds the greatest weight, while the ease-of-use/acceptance of the health technology criterion is deemed least important. Due to variations in criteria and methodological differences in measuring relative weight, direct comparisons with other models, such as those of Mobinizadeh et al. and EVIDEM, present challenges. Notably, the analytic hierarchy process method used in these models differs fundamentally from the approach employed in this study.
Furthermore, this study contributes to the field by establishing and defining scoring levels within each criterion, considering their relative effects and minimizing overlap. The scoring scale varies across criteria, encompassing three, four, or five levels for each criterion, as appropriate, distinguishing it from Mobinizadeh et al. model, which employs a one to five scale, and the EVIDEM model, which adopts a zero to three scale for scoring or valuing each criterion (Reference Goetghebeur, Wagner, Khoury, Rindress, Grégoire and Deal20;Reference Mobinizadeh, Mohamadi, Arman, Nasiripour, Olyaeemanesh and Mohamadi22).
This study incorporates decision rules for certain criteria to enhance the model’s capability and flexibility. These rules are particularly relevant to the cost-effectiveness and budget impact criteria, providing guidance for decision making under specific conditions. By considering factors such as the severity of the disease, access to alternative health technologies, and other prioritized criteria, decision rules ensure that a technology’s relative priority is not determined solely by traditional parameters used in determining cost-effectiveness or budget impact. Decision rules can vary in their application across different criteria, and there is potential for their consideration on a broader scale within the entire model. Models from England (23) the Netherlands (Reference Nederland24), and Mobinizadeh et al. (Reference Mobinizadeh, Mohamadi, Arman, Nasiripour, Olyaeemanesh and Mohamadi22) also incorporate decision rules, particularly addressing the uncertainty of cost-effectiveness in their model. This approach aligns with a collaborative international effort to use decision rules in MCDA to support HTA (Reference Baltussen, Marsh and Thokala25).
For the evaluation of the final model, a diverse set of thirteen technologies was carefully selected to probe its utility, including of the allocation criteria and their levels, scoring framework, and decision rules. This approach facilitates a thorough assessment of the model’s effectiveness. Additionally, the results of prioritization utilizing the IR-HIBOM were compared to those from traditional approach based on cost-effectiveness, which revealed significant differences in rankings. Considering additional criteria explicitly through a prioritization model as demonstrated here can make a significant impact on prioritizing health technologies for resource allocation.
Strengths and limitations
While efforts were made to address limitations prevalent in similar models, inherent challenges persist in MCDA models. Potentially overlapping criteria were carefully distinguished through precise definitions, scoring levels for each criterion, and scoring framework. However, some limitations, such as the inability to assess the quality of published HTA reports, remain.
A potential limitation of the IR-HIBOM model lies in its practical application and acceptability within routine decision-making processes. Due to its comprehensive nature, there may be concerns regarding the willingness of HTA bodies to adopt it as a standard approach. As noted earlier, the results of this study were presented to the Secretariat of the Supreme Council of Health Insurance, the primary authority responsible for regulating, developing, and revising social health insurance packages in Iran. Consideration and potential use of some form of this model by the Secretariat could support more objective, transparent, and routine revisions of health insurance benefit packages. However, further research is required to assess whether the benefits of using such a comprehensive model outweigh the potential challenges of implementing it in practical, real-world settings.
Despite these challenges, the study demonstrates certain strengths. Among these are the design of a MCDA model based on specialized concepts in health economics, along with the rationale for and integration of statistical and mathematical techniques. The use of innovative methods, such as the BWS method, enabled the participation of a larger, more diverse set of experts in the weighting process. Moreover, the inclusion of specialized expert panels in different stages of the research enhances the model’s credibility and applicability for HTA.
Conclusion
The constraints of public budgets and the imperative to prioritize resource allocation for health technologies call for robust, transparent methodologies. Recognizing the need to account for a diverse, nonredundant set of criteria to inform resource allocation, employing a suitable framework to incorporate them and generate informed outputs becomes invaluable for decision making. Incorporating the preferences of different stakeholders can significantly influence related public policies. This study demonstrates the applicability of IR-HIBOM for health resource allocation within Iran, particularly for addressing the practical challenges of compiling and revising health benefit packages for a national health system.
These models should be viewed as decision-support tools. They can help to guide, though not replace, deliberative processes, serving as dynamic frameworks that can be updated over time to adapt to evolving constraints and opportunities within the health system. Ultimately, the application of this model will depend on the evolving decisions and policies of the relevant authorities, recognizing the need for further evaluation of its practical implementation in real-world settings.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/S0266462325100263.
Availability of data and materials
The datasets generated and/or analyzed during this study are not publicly available but are available from the corresponding author on reasonable request.
Authors contribution
AD, AAS, and RD designed the study and conducted estimations and analyses. AD and RD acquired, interpreted the data, designed model, and revised the manuscript. MY and RD were in charge of statistical analysis. AD and MM conducted performance matrix and model appraisal. All authors have read and approved the manuscript.
Funding statement
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Competing interest
The authors have no conflicts of interest associated with the material presented in this article.
Ethics approval and consent to participate
This study was approved by the Ethical Committee of Tehran University of Medical Sciences (ethics committee no: IR.TUMS.SPH.REC.1398.246). All the study protocols and methods were carried out in accordance with relevant guidelines and regulations. Informed consent was documented electronically with a signature on the consent form’s designated field.
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