Table 1.

A Method for Implementing Artificial Intelligence in Healthcare

Part I: Activities preceding AI model development
Task or concept	Key activities	Relevance for AI
Identify and articulate the problem	• Define the problem and why it is important to patients, providers, or health systems • Ensure a clear linkage with organizational priorities and constructs	• Clarifies the potential use case for AI capabilities from the beginning • Ensures organizational resourcing and support throughout development and implementation
Form the team	• Identify sponsors, project manager, process owners, end users, and relevant subject matter experts • Determine accountability vs. responsibility for project success	• For the most effective design and implementation, the team should represent the technical and operational perspectives from the beginning – including sponsorship and project team composition • Accountability (i.e., “yes-no” authority and veto power) should reside within operations
Analyze current system and determine key features for success	• Observe and map out the current processes, identify issues, and analyze and prioritize root causes • Articulate key features for success from the list of prioritized root causes	• Assists in determining when and how an AI technology might be helpful in solving the problem • Brings into focus the possible use cases for AI for various end users • Broadens the possibilities for workflow design and increases the likelihood of a successful AI deployment
Assess the utility of AI	• Determine if AI could address any of the key features for success derived from the last phase • Consider the following costs: ○ Cost to build the model ○ Potential for model bias ○ Ongoing model maintenance needs	• Identifies when AI is needed vs. situations where a non-AI solution is superior • Encourages stewardship of technical resources • Mitigates downstream surprises related to cost and feasibility
Part II: Activities concurrent with AI model development
Task or concept	Key activities	Relevance for AI
Ideate on key features for success and develop clinical integration workflows	• Conduct future state process mapping sessions leveraging design thinking methods such as applying lenses and extremes to stimulate creativity • Develop prototypes, and conduct body storming sessions using real scenarios in a simulation	• Ensures clinical integration workflow design and AI delivery design that balance feasibility, acceptability, efficiency, and effectiveness • Design simulations allow for rapid testing of clinical integration workflows and AI model acceptability
Plan, do, study, adjust (PDSA)	• Organize and run iterative live clinical tests of the full intervention (model and workflows) • After sufficient iteration, learning and success, the team can plan more widespread implementation and evaluation	• Allows the technical and operational teams to observe, assess and adjust the model and workflow in usual clinical practice where variation is rife • Use of methods and frameworks, such as the unified theory of acceptance and use of technology (UTAUT), enables an iterative data-driven to design of the intervention (model and workflow)