TABLE 3.

Implementation Strategy for AI

Problem	Action	Elaboration	CFIR Domain
Generic strategy
Gap research—clinic  Timing: clinical need v data availability  Communication  Support	AI research agenda	Appoint contact person per clinical team: look at the possibilities and needs to create a shared AI research/development agenda Contact person is a link between research and clinic Quarterly meeting clinicians—researchers	Inner setting
Support  Ownership  Communication	AI champions/opinion leaders in clinical teams	Champions promote innovation on the work floor, gain support from other people, define, and overcome obstacles and share information and knowledge about the innovation	Inner setting
Lack of knowledge and understanding	Education program	Define basic knowledge level for employees Define specific requirements per function group Training program and teaching materials will be made available	Characteristics of individuals
Management/leadership	Management development program AI	Development of a module leading digital transformation for management	Inner setting
Complexity, clear goals, sufficient employees, and good feasibility and desirability	Quick scan project for chance of timely implementation	In previous research, a prediction model for timely implementation of innovations was developed.38,39 All AI projects will use the model at the start, during, and at the end of the project. Project leader, project members, and project owners will fill out the model. This provides project leaders with a quick scan of their project and this way they can ensure that the goals and process are clear for all stakeholders, ensure sufficient employees to work on the project, see whether the project is feasible and desirable, and whether project members find it a complex project. With this information, the project leader can better prepare for potential obstacles/hurdles during the project	Process
Policy and legislation  Good = good enough	Develop AI policy	Aim for automation but keep flexibility for continuous improvements Sacrifices must be made, since a small improvement means the AI solution must be rebuilt and validated A roadmap for the introduction and management of AI following relevant current legislation	Outer setting Inner setting
Project-specific strategy
Lack of stakeholder involvement/engagement  Clear goals and process  Support  Ownership  Complexity  Good feasibility and desirability  Communication	Interactive, multidisciplinary kick-off workshop	Participants of the workshop: all relevant stakeholders needed at any given time in a specific AI project (from researcher to ICT) Goal workshop: stimulate implementation by creating collective problem awareness/collective ownership Workshop content  Goal and planning of project  Quick scan project using prediction model timely implementation  Inventory bottlenecks from various stakeholders  Brainstorming solutions for bottlenecks  Result: action list solution bottlenecks, including who picks up which task Evaluation: acceptability, feasibility, appropriateness, costs	Intervention characteristics Inner setting Process
Frequent project meetings  Risk analyses  Evaluation and testing  Lack of effect measurement  Education  Clear goals and process	Project plan	Project plan including  Phasing, milestones/deliverables, risk analysis, effect measurement, training, and communication plan Complete project planning, including  Time commitment, staff deployment, planning in time If there are separate phases, evaluation after each phase Regular project meetings of whole project team	Process Inner setting Characteristics of individuals
Evaluation and testing  Lack of effect measurement	Evaluation	Evaluation before the project: acceptability, appropriateness, feasibility, and the cost of the project Evaluation during the project: acceptability, feasibility, adoption, fidelity, coverage/scope, and cost of the project Evaluation after the project: an evaluation takes place on both content and process:  Does it deliver what it is supposed to? Does it do what it is supposed to do?  Lessons learned? What should be done differently next time?	Process

Abbreviations: AI, artificial intelligence; CFIR, Consolidated Framework for Implementation Research; ICT, Information Communication Technology.