PUB - Publikationen an der Universität Bielefeld

Background: Antimicrobial resistances pose significant challenges in healthcare systems. Clinical decision support systems (CDSS) represent a potential strategy for promoting a more targeted and guideline-based use of antibiotics. The integration of artificial intelligence (AI) into these systems has the potential to support physicians in selecting the most effective drug therapy. Objective: This study aims to analyze the feasibility of an AI-based CDSS pilot version for antibiotic therapy in sepsis patients and identify facilitating and inhibiting conditions for its implementation in intensive care medicine. Methods: The evaluation was conducted in two steps, employing a qualitative methodology. Initially, expert interviews were conducted, in which intensive care physicians were asked to assess the AI-based recommendations for antibiotic therapy in terms of plausibility, layout and design. Subsequently, focus group interviews were conducted to examine the technology acceptance of the AI-based CDSS. The interviews were anonymized and evaluated using content analysis. Results: With regard to the feasibility of the intervention: The majority of physicians have a positive attitude towards AI-based CDSS. The assessment of AI-based recommendations largely depends on plausibility and professional experience. A central element for the acceptance of the AI-based CDSS was the time factor. A lack of digitization in the clinics was identified as a major inhibiting factor. According to the physicians, the heterogeneous use of antibiotics in practice to date also has a negative impact on the predictive ability of the AI-based CDSS. Conclusions: Early integration of users is beneficial for both the identification of relevant context factors and the further development of an effective CDSS. Overall, the potential of AI-based CDSS is offset by inhibiting contextual conditions that impede its acceptance and implementation. The advancement of AI-based CDSS and the mitigation of these inhibiting conditions are crucial for the realization of its full potential.