BACKGROUND
Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a non-user-oriented development approach, resulting in reduced user acceptance.
OBJECTIVE
This study has two research objectives. Firstly, problems and corresponding solutions that hinder or support the development and implementation of AI-based CDSS are identified. Secondly, this study aims to increase user acceptance by creating a user-oriented requirement profile, using the example of sepsis.
METHODS
The study is based on a mixed methods approach combining (i) a scoping review, (ii) focus groups with physicians and professional caregivers and (iii) semi-structured interviews with relevant stakeholders. The research modules mentioned provide the basis for the development of a (iv) survey, including a discrete choice experiment (DCE) with physicians. The survey is followed by the development of a requirement profile for AI-based CDSS and the derivation of policy recommendations for action, which are evaluated in a (v) expert roundtable discussion.
RESULTS
This study provides an overview of the barriers and corresponding solutions related to the development and implementation of AI-based CDSS. Using sepsis as an example, a user-oriented requirement profile for AI-based CDSS is developed.
CONCLUSIONS
The results of the study represent the first attempt to create a comprehensive user-oriented requirement profile for the development of sepsis-specific AI-based CDSS. In addition, general recommendations are derived, in order to reduce barriers in the development and implementation of AI-based CDSS.