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Updated: Jan 2, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
An evidence-based framework for identifying technologies of no or low-added value (NLVT)
María Eugenia Esandi1,2, Iñaki Gutiérrez-Ibarluzea3,4, Nora Ibargoyen-Roteta3
1Departamento de Economía, Universidad Nacional del Sur, Bahia Blanca, Argentina.
Objective:
To synthetize the state of the art of methods for identifying candidate technologies for disinvestment and propose an evidence-based framework for executing this task.
Methods:
An interpretative review was conducted. A systematic literature search was performed to identify secondary or tertiary research related to disinvestment initiatives and/or any type of research that specifically described one or more methods for identifying potential candidates technologies, services, or practices for disinvestment. An iterative and critical analysis of the methods described alongside the disinvestment initiatives was performed.
Results:
Seventeen systematic reviews on disinvestment or related terms (health technology reassessment or medical reversal) were retrieved and methods of 45 disinvestment initiatives were compared. On the basis of this evidence, we proposed a new framework for identifying these technologies based on the wide definition of evidence provided by Lomas et al. The framework comprises seven basic approaches, eleven triggers and thirteen methods for applying these triggers, which were grouped in embedded and ad hoc methods.
Conclusions:
Although identification methods have been described in the literature and tested in different contexts, the proliferation of terms and concepts used to describe this process creates considerable confusion. The proposed framework is a rigorous and flexible tool that could guide the implementation of strategies for identifying potential candidates for disinvestment.
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