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Updated: May 19, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
A systematic review of coverage decision-making on health technologies-evidence from the real world
1Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Health Economics and Health Care Management, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; University of Hamburg, Hamburg Center for Health Economics, Esplanade 36, 20354 Hamburg, Germany. katharina.fischer@wiso.uni-hamburg.de
Objective:
Quantitative analysis of real-world coverage decision-making offers insights into the revealed preferences of appraisal committees. Aim of this review was to structure empirical evidence of coverage decisions made in practice based on the components 'methods and evidence', 'criteria and standards', 'decision outcome' and 'processes'.
Methods:
Several electronic databases, key journals and decision committees' websites were searched for publications between 1993 and June 2011. Inclusion criteria were the analysis of past decisions and application of quantitative methods. Each study was categorized by the scope of decision-making and the components covered by the variables used in quantitative analysis.
Results:
Thirty-two studies were identified. Many focused on pharmaceuticals, the UK NICE or the Australian PBAC. The components were covered comprehensively, but heterogeneously. Seventy-two variables were identified of which the following were more prevalent: specifications of the decision outcome; the indications considered for appraisal, identification of incremental cost-effectiveness ratios, appropriateness of evaluation methods, type of economic or clinical evidence used for assessment, and the decision date.
Conclusions:
Research was dominated by analysis of decision outcomes and appraisal criteria. Although common approaches were identified, the complexity of coverage decision-making - reflected by the heterogeneity of identified variables - will continue to challenge empirical research.
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