Accepted for/Published in: JMIR Research Protocols
Date Submitted: May 29, 2024
Date Accepted: Oct 31, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
User-Oriented Requirements for AI-Based Clinical Decision Support Systems in Sepsis: Study Protocol for a Mixed Methods Study
ABSTRACT
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.
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Copyright
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