Accepted for/Published in: JMIR Research Protocols
Date Submitted: Jun 23, 2023
Open Peer Review Period: Jun 23, 2023 - Aug 18, 2023
Date Accepted: Sep 8, 2023
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited.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.
AI-QIF: A Protocol for Co-creating a Framework to Guide Implementation of Artificial Intelligence in Healthcare
ABSTRACT
Background:
Artificial intelligence (AI) has the potential in healthcare to transform patient care and administrative processes, yet healthcare has been slow to adopt AI due to many types of barriers. Implementation science has shown the importance of structured implementation processes to overcome implementation barriers. However, there is a lack of knowledge to guide such processes when implementing AI-based applications in healthcare.
Objective:
The aim of this paper is to provide a protocol for the development, testing and evaluation of a framework, AI-QIF (Artificial Intelligence-Quality Implementation Framework), intended to guide decisions and activities related to the implementation of various AI-based applications in healthcare.
Methods:
The article outlines the development of an AI implementation framework for healthcare in five phases based on the Quality Implementation Framework (QIF). QIF is a process model developed in implementation science. The model guides the user to consider implementation-related issues in a step-by-step design and to plan and perform activities that support implementation. This framework was chosen for its adaptability, usability, broad scope and detailed guidance concerning important activities and considerations for successful implementation. The development process starts with phase I, in which an AI-adapted version of QIF is created. Phase II will produce a digital mockup of the AI-QIF. Phase III will involve the development of a prototype of the AI-QIF with an intuitive user interface. Phase IV is dedicated to testing the prototype of the AI-QIF in a healthcare environment. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Co-creation is a guiding principle for the project, and collaboration between researchers and various stakeholders will take place to varying degrees in the five phases. The process will draw on research-based and practice-based knowledge.
Results:
The AI-QIF will draw on numerous knowledge sources. The framework will be under continuous development and refinement. Insights gained during this process will be used as the foundation for parallel investments in regional capacity to increase the practical resources, competencies and organizational structures required to facilitate implementation of AI-based applications. This work will be carried out in collaboration with representatives from academia, strategic partners from the business sector, as well as political and operational leaders and teams from the regional and municipal healthcare systems.
Conclusions:
The development of the AI implementation framework, AI-QIF, described in this study protocol aims to facilitate the implementation of AI-based applications in healthcare based on the premise that implementation processes benefit from being well prepared and structured. The framework will be co-produced to enhance its relevance, validity, usefulness and potential value for application in practice. Clinical Trial: Not relevant.
Citation
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Copyright
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