Developing a Life-Cycle–Based HTA Framework for AI and Digital Diagnostic Technologies in Rare Pediatric Melanoma: The MELCAYA Approach
Author(s)
Livio Battaglia, Dr.1, Gabriella Di Santo, Dr.1, Marco Marchetti, Dr.1, Alessandra Lo Scalzo1, Pietro Refolo, Dr.2, Dario Sacchini, PhD, MD2, Costanza Raimondi, Dr.2, Joan Fibla-Reixachs, Dr.3, Laura Sampietro-Colom, PhD, MD3.
1National Agency for Regional Health Services, Rome, Italy, 2Università Cattolica del Sacro Cuore, Rome, Italy, 3Hospital Clinic de Barcelona, SABADELL, Spain.
1National Agency for Regional Health Services, Rome, Italy, 2Università Cattolica del Sacro Cuore, Rome, Italy, 3Hospital Clinic de Barcelona, SABADELL, Spain.
OBJECTIVES: This study aimed to design and validate a tailored Health Technology Assessment (HTA) framework for evaluating Digital Health Technologies (DHTs) developed within the MELCAYA EU project for the secondary prevention and diagnosis of melanoma in Children, Adolescents, and Young Adults (CAYA). The framework supports DHTs assessment, including AI tools and connected diagnostic devices, considering different stages of technology maturity/life-cycle stages.
METHODS: A mixed-methods approach was used, combining a scoping literature review, background analysis of MELCAYA technologies, and semi-structured interviews with HTA experts and clinicians. A supplementary documentary search broadened the scope to include existing HTA frameworks for DHTs. Retrieved evidence informed the draft MELCAYA framework, which was proof-tested with MELCAYA developers (WP5, WP6) using structured checklists and interviews at two key development stages: pilot studies (Innovation Maturity Level (IML) 4-7) and market readiness (IML 8-10). A transferability tool was then developed to support broader applicability.
RESULTS: The literature review identified only three HTA reports addressing rare diseases and digital diagnostics, revealing significant gaps. These focused on genomic technologies and highlighted challenges in generating clinical and economic evidence, addressing ELSI aspects, and applying regulatory standards in rare contexts. Expert insights emphasized the need to adapt HTA to the constraints of rare diseases, noting barriers such as limited trial designs, small population sizes, and ethical tensions. Proof-testing revealed feasibility challenges at early stages (e.g., immature safety and economic data), while later stages allowed more robust assessment—though organizational and legal complexities persisted.
CONCLUSIONS: The final framework incorporates clinical and non-clinical domains, distinguishes between AI and connected devices, and adapts to innovation maturity levels. The transferability tool, structured as a dual-track matrix, enables systematic domain mapping across IML stages and supports broader application in rare oncology DHTs.
METHODS: A mixed-methods approach was used, combining a scoping literature review, background analysis of MELCAYA technologies, and semi-structured interviews with HTA experts and clinicians. A supplementary documentary search broadened the scope to include existing HTA frameworks for DHTs. Retrieved evidence informed the draft MELCAYA framework, which was proof-tested with MELCAYA developers (WP5, WP6) using structured checklists and interviews at two key development stages: pilot studies (Innovation Maturity Level (IML) 4-7) and market readiness (IML 8-10). A transferability tool was then developed to support broader applicability.
RESULTS: The literature review identified only three HTA reports addressing rare diseases and digital diagnostics, revealing significant gaps. These focused on genomic technologies and highlighted challenges in generating clinical and economic evidence, addressing ELSI aspects, and applying regulatory standards in rare contexts. Expert insights emphasized the need to adapt HTA to the constraints of rare diseases, noting barriers such as limited trial designs, small population sizes, and ethical tensions. Proof-testing revealed feasibility challenges at early stages (e.g., immature safety and economic data), while later stages allowed more robust assessment—though organizational and legal complexities persisted.
CONCLUSIONS: The final framework incorporates clinical and non-clinical domains, distinguishes between AI and connected devices, and adapts to innovation maturity levels. The transferability tool, structured as a dual-track matrix, enables systematic domain mapping across IML stages and supports broader application in rare oncology DHTs.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
HTA106
Topic
Health Policy & Regulatory, Health Technology Assessment, Medical Technologies
Topic Subcategory
Value Frameworks & Dossier Format
Disease
Oncology, Pediatrics, Rare & Orphan Diseases