Evidence map›Paper›PMID 39228212›Full record

ArticleInternational journal of technology assessment in health care2024

Development of an multicriteria decision analysis framework for rare disease reimbursement prioritization in Malaysia.

Ku N Ku Abd Rahim, Nurkhodrulnada Muhammad Lattepi, Roza Sarimin, Sze Shir Foo, Syaqirah Akmal, Sit Wai Lee, Izzuna Mudla Mohamed Ghazali

Abstract read
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Article in International journal of technology assessment in health care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Ku N Ku Abd RahimMalaysian Health Technology Assessment Section, Medical Development Division, Ministry of Health Malaysia, Putrajaya, Malaysia.ORCID https://orcid.org/0000-0003-1603-3758
Nurkhodrulnada Muhammad LattepiMalaysian Health Technology Assessment Section, Medical Development Division, Ministry of Health Malaysia, Putrajaya, Malaysia.
Roza SariminMalaysian Health Technology Assessment Section, Medical Development Division, Ministry of Health Malaysia, Putrajaya, Malaysia.
Sze Shir FooMalaysian Health Technology Assessment Section, Medical Development Division, Ministry of Health Malaysia, Putrajaya, Malaysia.
Syaqirah AkmalMalaysian Health Technology Assessment Section, Medical Development Division, Ministry of Health Malaysia, Putrajaya, Malaysia.
Sit Wai LeeMalaysian Health Technology Assessment Section, Medical Development Division, Ministry of Health Malaysia, Putrajaya, Malaysia.
Izzuna Mudla Mohamed GhazaliMalaysian Health Technology Assessment Section, Medical Development Division, Ministry of Health Malaysia, Putrajaya, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesRare diseases (RD)-related policies have received significant attention due to the pressing medical requirements associated with these medical conditions and the substantial impact and treatments they may have on healthcare budgets. Nevertheless, policymakers frequently encounter difficulties in managing issues concerning resource allocation and prioritization within this population. Realizing the need to address such problems, this study was conducted to develop a framework based on the multicriteria decision analysis to improve RD reimbursement prioritization in Malaysia.

methodsPrimarily, a scoping review was performed to identify the methods and criteria used for the reimbursement of RD treatment, followed by strategic stakeholder engagement and a deliberative process on determining the best approach for the framework, including criteria identification, elicitation of weights, and a pilot assessment using the framework.

resultsThe findings reflected the priorities and perspectives of the stakeholders, which identified eight key criteria and their associated weights, namely effectiveness (19.6 percent), disease severity (15.6 percent), safety (14.2 percent), access to treatment (12.6 percent), economic consideration (12.2 percent), type of therapeutic treatment (11.5 percent), availability of alternatives (8.3 percent), and population group (6 percent).

conclusionsIn summary, the developed framework was well-accepted by the Rare Disease Committee, which will be applied as part of the committee deliberation for transparent and equitable decision making on fund allocation and reimbursement of orphan and RD treatment in Malaysia.

Indexed as

Decision Support TechniquesHealth PrioritiesRare DiseasesTechnology Assessment, BiomedicalHealth Care RationingHumansMalaysiaReimbursement MechanismsResource Allocationdecision analysisdiseaseorphan diseaserare diseaserare multicriteria decision analysis

Identifiers

PMID39228212
PMCPMC11569914

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LicenceCC BY-NC-SA
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.