INTRODUCTION Artificial intelligence (AI) has great potential to optimise patient care and reduce the bur- den on healthcare. Despite numerous AI solutions being developed for hospitals, the clinical adoption rate remains low, largely due to challenges in evaluating their practical usefulness before implementation. This research addresses the gap by developing a decision-making strategy that hospitals can use to assess whether specific AI applications should be implemented. The strategy aims to ensure that AI solutions are effec- tively integrated, address genuine problems, and meet stakeholder needs, thereby facilitating better decision- making and more successful AI adoption in clinical settings.
METHODS The methodology involved three key phases: development, testing, and evaluation of the decision- making strategy. The strategy was initially developed through an analysis of existing guidelines, specifically the "Stappenplan Healthy AI (HAI)" document, and corresponding literature. Stakeholder evaluations, in- cluding input from hospital AI teams, medical officers, and clinical specialists, were used to refine the strategy. A practical case study was conducted in the Radiology department of the Noordwest Ziekenhuisgroep (NWZ) hospital to test the strategy’s applicability, followed by stakeholder feedback through a structured question- naire to evaluate its effectiveness and usability.
RESULTS The results showed that the iterative development process, involving multiple rounds of stake- holder feedback, substantially improved the decision-making strategy’s comprehensiveness and relevance. Stakeholders highlighted that the strategy effectively captured critical aspects of AI integration, such as tech- nical requirements, stakeholder needs, and workflow implications. Testing in the Radiology department re- vealed challenges in identifying responsible individuals for data collection, which initially delayed the pro- cess, but also underscored the need for well-defined roles. The feedback from stakeholders was largely pos- itive, indicating that the strategy was clear and practical for evaluating AI solutions, though some improve- ments were suggested for addressing technical integration and detailing follow-up actions. Stakeholders ap- preciated the structured format, which facilitated effective communication and collaboration among differ- ent departments. Overall, the decision-making strategy succeeded in creating a robust framework for evalu- ating AI applications, helping ensure that such technologies are implemented thoughtfully and effectively.
CONCLUSION The aim of this study was to develop a decision-making strategy for hospitals to determine whether AI applications should be implemented, as well as to test and evaluate the strategy. The iterative process proved effective in creating a practical and efficient tool that helps identify potential bottlenecks and clarifies resource needs for implementation. The involvement of ICT stakeholders was crucial, highlighting the importance of technical evaluation as a key factor in decision-making. Overall, the strategy provides a focused and manageable framework that allows hospitals to evaluate AI applications effectively, supporting informed decisions to improve healthcare efficiency and patient care quality.