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)
A Framework to Guide Implementation of Artificial Intelligence in Healthcare: Protocol for a Co-creation Research Project
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 and tools to guide such processes when implementing AI-based applications in healthcare.
Objective:
The aim of this protocol is to describe 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 broad use in healthcare 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 will proceed in five phases, with primarily qualitative methods being used. The process starts with phase I, in which an AI-adapted version of QIF is created (AI-QIF). 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 usability testing of the prototype in healthcare environments. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Co-creation is a guiding principle for the project and is an important aspect in four of the five development phases. The co-creation process will enable the utilization of both on research-based and practice-based knowledge.
Results:
The project will be conducted within the frame of a larger research program with the overall objective of developing theoretically and empirically informed frameworks to support AI implementation in routine healthcare. The program was launched in 2021 and has carried out numerous research activities. The development of AI-QIF as a tool to guide implementation of AI-based applications in healthcare will draw on knowledge and experience acquired from these activities. The framework will be developed over two years, January 2023 to December 2024. It will be under continuous development and refinement.
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: -
Citation
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Copyright
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