Medical device (MD) procurement is a strategic area of non-clinical decision-making in healthcare, as purchasing choices affect not only cost containment but also quality of care, patient safety, and long-term health system sustainability. In competitive tenders, however, evaluation criteria are often highly heterogeneous and device specific. This limits the possibility of systematically comparing procurement decisions across tenders and of understanding the evaluative logics that guide final decisions. Within this context, artificial intelligence (AI) may support procurement not by replacing human judgement, but by structuring fragmented information, identifying recurrent value dimensions, and enabling a more reflexive comparison between AI-generated outputs and observed human decisions.
This study investigates how AI can support decision-making in MD procurement. It focuses on tender documents managed by a regional central purchasing body in Italy and develops a research design structured into four phases: tender document analysis and data extraction; recoding specific-criteria into higher-level meta-criteria; construction of AI-based value configurations; and comparison of these configurations with observed procurement decisions. The paper reports the preliminary results of the first two phases.
The paper reports the preliminary results of the first two phases. As regards the first phase, approximately 500 tenders were identified. After excluding non-MD tenders, price-only procedures, incomplete cases, and non-interpretable documents, the final dataset consisted of 172 tender documents. Preliminary analysis identified three recurring document formats and corresponding extraction schemas.
As regards the second phase, the first results show that device-specific evaluation criteria can be recoded into broader meta-criteria, such as versatility of use, patient safety, operator safety, interoperability, and degree of clinical certainty. These meta-criteria provide the analytical basis for comparing tenders involving different types of MDs and for integrating AI appropriately into procurement decision-making. By preserving traceability to the original criteria while enabling broader comparability, they support AI-based procurement governance and may contribute to accountability and organizational learning by supporting a more explicit justification of procurement decisions over time.