DIME PROJECT

Implementing AI in Healthcare

SECTION 3 | IMPLEMENT AI

Deploy with confidence

Turn plans into action: deliver a stable, trusted clinical deployment.

The planning is done. Validation is complete. Now, it’s go-time. Successful deployment is a coordinated, cross-functional process that defines whether your tool will land cleanly, safely, and credibly. This phase is where clinicians begin to experience the AI tool in their workflows, where IT activates production environments, and where monitoring systems are initiated.

The stakes are high and so is the opportunity. Your ability to execute here, with minimal disruption or confusion, directly affects adoption, trust, and downstream scalability. Take a strategic approach to integrating AI into live clinical or operational workflows with full technical, operational, and end-user readiness to ensure safety, trust, and early impact.

During deploying, you will…

Choose a rollout method
Finalize technical integration
Activate stakeholder communication
Train and onboard end-users
Prepare the clinical or operational environment
Activate monitoring infrastructure
Finalize safeguards and contingency plans

Choose a rollout method

When deploying an AI tool, a critical decision is whether to implement a “phased” rollout or a “big bang” approach. Each strategy has distinct advantages and risks that should be carefully considered based on the complexity of the tool, the organizational culture, and the tolerance for disruption.

PHASED ROLLOUT

A phased rollout involves deploying the AI tool to a smaller group or subset of the organization first, learning from that initial experience, and then gradually expanding the deployment to the entire system. This method is best applied when the tool complexity is high, workflows vary, or teams want learnings and insights before scaling.

Advantages

Reduced risk: Limits the impact of unforeseen issues to a smaller group, allowing for adjustments before widespread deployment.
Early learning and iteration: Provides opportunities to gather feedback, identify bugs, and refine the tool and processes in a controlled environment.
Improved user acceptance: Allows users to gradually adapt to the new tool, potentially increasing adoption and reducing resistance.
Optimized training: Training can be tailored and refined based on initial user experiences, making subsequent training more effective.
Resource management: Distributes the workload for IT, training, and support teams over a longer period.

Risks

Extended timeline: The overall deployment process can take longer.
Integration challenges: Maintaining consistency and integration across different phases can be complex.
Potential for inconsistency: Different user groups might experience slightly different versions or levels of functionality, leading to confusion.
Sustained effort: Requires ongoing management and support throughout the entire phased deployment.

BIG BANG ROLLOUT

A big bang rollout involves deploying the AI tool to the entire organization or system simultaneously. This rollout method should be reserved for tools that are simple to integrate, urgency and clinical need is high, and organizational readiness is strong.

Advantages

Faster deployment: The tool becomes available to all users at once, potentially accelerating the realization of benefits.
Unified experience: All users receive the same version and functionality at the same time, ensuring consistency.

Risks

Disruption: Any unforeseen issues or bugs can impact the entire system immediately and broadly.
Resource demands: Requires significant resources (IT, training, support) to be available concurrently for the launch.
Learning curve: Users are faced with a significant change all at once, potentially leading to frustration and resistance.
Error recovery: Major errors or system failures can have widespread consequences and be challenging to remediate quickly.

Finalize technical integration

Move the model from testing to production. This is your last technical gate—ensure the tool is integrated, functioning, and observable in the live environment.

Migrate the model to the production infrastructure (cloud, on-prem, edge).
Integrate with the EHR or workflow systems (e.g., Epic, Cerner, PACS).
Configure failover protocols and fallback logic.
Run final pre-launch testing (connectivity, triggers, alerts, logging).
Confirm data flow monitoring and security compliance.

Activate stakeholder communication

Clear, consistent messaging reinforces trust, alignment, and engagement—before, during, and after launch.

Share the “why now” story: problem, solution, value.
Provide FAQs and support channels for feedback.
Tailor comms by audience: leadership, clinical users, operations, IT.
Use existing communication channels (intranet, Slack, town halls, huddles).
Reinforce alignment with org values (e.g., patient safety, innovation, efficiency).

Audiences	Clinical end-users, IT staff, operations, executives
Key messages	What’s changing and what’s not What to expect at go-live How to get help Where to give feedback
Channels	Town halls, newsletters, intranet, in-app alerts
Cadence	Plan (awareness), Deploy (activation), monitor (reinforcement)
Feedback loops	Daily huddles, suggestion boxes, anonymous surveys

Train and onboard end-users

Even the best AI tools fail if users aren’t ready or confident. Training must go beyond the “how” to include the “why.”

Develop targeted training by role (e.g., physicians, nurses, coordinators, IT).
Emphasize safety, clinical decision support, and intended use.
Clarify what to do when the AI conflicts with clinician judgment.
Offer flexible formats: live sessions, embedded job aids, e-learning modules.
Designate internal champions for real-time support and credibility.
Help your workforce build foundational AI skills in the Health AI Essentials course offered by Digital Medicine Academy

Prepare the clinical or operational environment

Technical readiness is only half of the equation. Clinical and operational fit is what determines real-world usability.

Validate AI triggers and user interface within clinical systems.
Map and rehearse workflows that now include the AI tool by conducting end-user testing in a staging environment.
Update SOPs and job aids to reflect new roles or decisions informed by the tool.
Identify escalation paths for users encountering problems.

Activate monitoring infrastructure

You can’t fix what you can’t see. Monitoring is an extension of your evaluation and validation. It’s the continuous process that begins now and continues throughout a tool’s life cycle.

Turn on monitoring for system uptime, error rates, and data flow.
Begin collecting data for defined KPIs and alert thresholds.
Monitor early user interactions for signs of confusion or misalignment.
If relevant, conduct early post-deployment safety checks (e.g., silent mode audits).

Finalize safeguards and contingency plans

Assume that something will break. Establish real-time support and escalation pathways that help assuage user confusion, system issues, or trust breakdowns that stall momentum fast.

Establish clear rollback procedures or safety switches.
Identify clinical or operational thresholds that would trigger intervention.
Define the process for incident reporting and remediation.
Stand up a support line or channel for the first few weeks.
Assign on-call staff from clinical informatics, IT, and analytics.
Monitor for confusion, misuse, or unexpected safety issues.
Escalate and resolve early problems fast—momentum depends on it.

A final note on deployment

Deployment is the moment where technical readiness meets clinical reality, and where execution determines trust. The decisions you make here set the trajectory for adoption, safety, and eventual scale.

Successful deployment is measured by whether a tool works reliably, integrates seamlessly into workflows, supports clinicians without slowing them down, and begins delivering value without disruption. That means keeping eyes on three things:

“Even if bedside responsibility falls on the clinician, we have an institutional responsibility to make sure the AI tools clinicians are using to inform and support their decisions are safe.”

– Danton Char, MD, MSA, Stanford University

People

Are clinicians confident and supported? Are patients informed and protected?

Processes

Are workflows running as designed, or are unexpected gaps emerging?

Performance

Is the AI operating safely and consistently under real-world conditions.

Next steps

Deploying AI in healthcare is never “set it and forget it.” It’s the beginning of a continuous learning cycle, where feedback fuels refinement, governance keeps risks in check, and early insights pave the way to scaling. The next phase — Monitor — ensures your AI investment delivers on its promise: safe, trusted, and effective care at scale.

Monitor for value