A Framework to Guide Implementation of AI in Health Care: Protocol for a Cocreation Research Project

• Published in: JMIR research protocols Vol. 12; p. e50216
• Main Authors: Nilsen, Per, Svedberg, Petra, Neher, Margit, Nair, Monika, Larsson, Ingrid, Petersson, Lena, Nygren, Jens
• Format: Journal Article
• Language: English
• Published: Toronto JMIR Publications 01.01.2023
• Subjects: Artificial intelligence; Collaboration; Content analysis; Decision making; framework; frameworks; health care; Health care delivery; IDC; implementation; Innovations; process model; process models; Public health; Usability; Sweden; process models; framework; frameworks; implementation; AI; process model; artificial intelligence; health care
• ISSN: 1929-0748; 1929-0748
• DOI: 10.2196/50216

Background Artificial intelligence (AI) has the potential in health care to transform patient care and administrative processes, yet health care 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 health care. Objective The aim of this protocol is to describe the development, testing, and evaluation of a framework, “Artificial Intelligence-Quality Implementation Framework” (AI-QIF), intended to guide decisions and activities related to the implementation of various AI-based applications in health care. Methods The paper outlines the development of an AI implementation framework for broad use in health care 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 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 will proceed in 5 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 health care environments. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Cocreation is a guiding principle for the project and is an important aspect in 4 of the 5 development phases. The cocreation process will enable the use of both on research-based and practice-based knowledge. Results The project is being 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 health care. The program was launched in 2021 and has carried out numerous research activities. The development of AI-QIF as a tool to guide the implementation of AI-based applications in health care will draw on knowledge and experience acquired from these activities. The framework is being developed over 2 years, from January 2023 to December 2024. It is 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 health care based on the premise that implementation processes benefit from being well-prepared and structured. The framework will be coproduced to enhance its relevance, validity, usefulness, and potential value for application in practice. International Registered Report Identifier (IRRID) DERR1-10.2196/50216