Implementers Perspectives on the Routine Use of Artificial Intelligence in Health Services: A Qualitative Study Using the Consolidated Framework for Implementation Research (CFIR)

Background: Interest is growing in the use of Artificial Intelligence (AI) technologies in health care. Health AI innovations have been explored in a range of clinical contexts, yet their implementation into routine practice remains challenging. The aim of this study was to understand the factors that influenced the implementation of AI innovations into routine practice in Australian Healthcare organisations, from the perspective of implementers. Methods: The study used a qualitative methodology. AI implementers were identified via an environmental scan of publicly available information, combined with passive snowballing. In-depth research interviews were undertaken between November 2021 and June 2022. Interviews were audio recorded and transcribed into text for data analysis. Transcripts were inductively coded by the researchers, followed by deductive categorisation of the data using the Consolidated Framework for Implementation Research (CFIR). Results: The study identified 11 different AI innovations being introduced in Australian healthcare organisations, and a total of 12 implementers working on the implementation of these innovations were recruited to participate in the study. Factors influencing the implementation of AI innovations into routine practice were identified across all five domains of the CFIR framework, but the innovation and implementation process domains were emphasised the most in the data. Implementers faced many barriers integrating their innovations into practice including challenges with stakeholder engagement, data access and other technical hurdles, resourcing constrains and lengthy timeframes for implementation. Discussion: The number of Health AI solutions being implemented in routine practice in Australian healthcare organisations is small relative to the uptake of innovation seen in research and industry. This gap is likely a reflection of the length and complexity of the implementation process for Health AI solutions, and barriers that need to be overcome as part of this process.