Accepted for/Published in: JMIR Formative Research
Date Submitted: Mar 16, 2023
Date Accepted: May 31, 2023
Barriers and enablers for implementation of an AI-based decision support tool to reduce risk of readmission of heart failure patients: Stakeholder interviews
ABSTRACT
Background:
AI applications in healthcare are expected to provide value for health care organizations, professionals as well as patients. However, implementation of such systems should be carefully planned and organized in order to ensure quality, safety and acceptance.
Objective:
This study aimed to understand the context and stakeholder perspectives related to future implementation of a decision support system for readmission prediction of heart failure patients.
Methods:
Interviews were held with 12 stakeholders from the regional and municipal healthcare organizations to gather their views on what effects implementation of such decision support system could have. Data was analysed based on the categories of barriers and enablers defined in the NASSS framework.
Results:
Stakeholders had in general a positive attitude and curiosity towards AI-based decision support systems, and lifted several barriers and enablers based on experiences of previous implementations of IT-systems. Aspects brought up related to all categories in the NASSS framework.
Conclusions:
Several ideas were put forward on how the proposed AI-system would potentially affect and provide value for patients, professionals and the organization, and implementation aspects were important parts of that. A successful system need not only technological and clinical precision, but also a carefully planned implementation process. Of importance for further planning was the placement of the application in the care process. Clinical Trial: Not applicable
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