ArticleJMIR research protocols2023
A Framework to Guide Implementation of AI in Health Care: Protocol for a Cocreation Research Project.
Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies.Journal of medical Internet research · 2024Pooled it
- Self-Administered Interventions Based on Natural Language Processing Models for Reducing Depressive and Anxiety Symptoms: Systematic Review and Meta-Analysis.JMIR mental health · 2024Pooled it
- When is AI "just another innovation"? A comparative conceptual analysis of artificial intelligence and evidence-based practice implementation.Implementation science communications · 2026Article
- Framework for artificial intelligence implementation research in healthcare: synthesizing current evidence on barriers and facilitators.NPJ digital medicine · 2026Article
- From Pilot Trap to Institutional Capacity: A Governance Framework for Sustainable Clinical AI Implementation in Health Systems.Journal of medical Internet research · 2026Article
- Integrating systems thinking and implementation science in biomedical informatics research.JAMIA open · 2026Review
- AI-Enhanced Continuing Professional Development as an Evolving Sociotechnical System: Multimethod Theoretical Framework Development Study.JMIR medical education · 2026Article
- Collaborative and Cooperative Hospital "In-House" Medical Device Development and Implementation in the AI Age: The European Responsible AI Development (EURAID) Framework Compatible With European Values.Journal of medical Internet research · 2026Article
- Clinical Artificial Intelligence Implementation in Routine Care: Real-World Operational Outcomes from a Provincial Health System in China.Risk management and healthcare policy · 2026Article
- Article
- Critical activities for successful implementation and adoption of AI in healthcare: towards a process framework for healthcare organizations.Frontiers in digital health · 2025Article
- The development and use of data warehousing in clinical settings: a scoping review.Frontiers in digital health · 2025Review
- Applications of Multimodal Artificial Intelligence in Non-Hodgkin Lymphoma B Cells.Biomedicines · 2024Review
- A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design.PloS one · 2024Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial 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.
objectiveThe 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.
methodsThe 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.
resultsThe 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.
conclusionsThe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.