DIME PROJECT

Implementing AI in Healthcare

SECTION 3 | IMPLEMENT AI

Scaling AI across your health system

Turn deployments into system-wide impact

Scaling is where AI ambitions succeed or collapse. What works in a single pilot rarely translates seamlessly across diverse sites, populations, and workflows. Scaling isn’t “copy-paste” — it’s a series of new deployments requiring intentional design, local adaptation, and enterprise-level orchestration. Without evidence and structured governance, scaling fast becomes scaling risk.

During scaling, you will…

Choose your scaling path
Institutionalize lessons learned
Develop an enterprise scaling plan
Establish vendor and governance frameworks
Prepare your workforce for adoption
Monitor ecosystem-wide performance

Choose your scaling path

Health systems scale in two fundamentally different ways. Knowing which path you’re on — or when you’re doing both — defines how you plan, govern, and resource:

Path A: Expand Usage

Definition
Deploy an existing AI tool to more sites, clinicians, or patient populations

Example
Expanding a predictive sepsis alert from one hospital → ten

Risks
Alert fatigue, inconsistent outcomes, broken workflows

Focus
Replicate success without replicating risk

Path B: Expand the Stack

Definition
Introduce additional AI tools alongside your current deployments

Example
Adding a generative documentation assistant to your predictive risk platform

Risks
Governance overload, interoperability failures, conflicting outputs

Focus
Integrate intelligently without overwhelming staff or infrastructure

Path A: Expanding Usage

Scaling an existing AI tool means widening adoption — more clinicians, more facilities, more patients — while maintaining performance, safety, and trust.

Institutionalize what works

Before expanding, lock in your success:

Capture lessons from your pilot: adoption barriers, workflow challenges, unexpected wins.
Refine deployment playbooks, checklists, and training guides based on frontline feedback.
Build a reference implementation package — a reusable toolkit so new sites start from proven processes.

Develop a structured scaling plan

Avoid fragmentation by anchoring expansion to explicit decision points:

Prioritize rollout sites based on strategic value, operational readiness, and patient need.
Tailor workflows to local contexts without undermining clinical fidelity.

Set pause points tied to safety KPIs, adoption thresholds, and governance reviews. Don’t assume success will translate automatically.

Strengthen vendor partnerships

Your vendor relationship evolves as you scale:

Update SLAs to reflect increased volume, broader reach, and defined response times.
Align retraining cadence, monitoring, and escalation protocols across all sites.
Include vendor reps in governance reviews to co-own safety and performance outcomes.

Expand training and support infrastructure

Scaling is a people challenge before it’s a technical one:

Customize training to local workflows, specialties, and digital literacy levels.
Deploy clinical champions as peer educators and rapid problem solvers.
Reinforce consistent messaging on tool purpose, benefits, and escalation paths.

Monitor performance in new contexts

Scaling multiplies risk. Re-establish your minimum monitoring stack at every new site:

Compare pilot vs. scaled performance side by side.
Automate alerts tied to clinical thresholds and escalate anomalies early.
Feed results back to governance and continuously update training and processes.

Path B: Expanding the Stack

Adding new AI tools to your system introduces ecosystem-level complexity. Success depends on standardization, interoperability, and avoiding governance fragmentation.

Institutionalize multi-tool learnings

Each new tool should strengthen your enterprise AI capability:

Centralize lessons learned across tools — integration pitfalls, adoption strategies, and workflow collisions.
Use standardized evaluation frameworks to ensure all tools are compared on consistent metrics.
Share integration learnings internally to prevent duplicated effort.

Develop a coordinated scaling plan

Introduce new AI tools deliberately:

Establish a formal intake and evaluation process for approving new tools.
Validate interoperability early to prevent conflicting insights.
Sequence deployments intentionally — avoid overlapping go-lives that overwhelm clinicians and IT.

Standardize vendor and governance frameworks

With more vendors, oversight must scale too:

Require transparent reporting of safety metrics, drift triggers, and performance benchmarks.
Define escalation protocols across all vendors, especially when tools overlap.
Negotiate shared-risk agreements tied to clinical outcomes and operational ROI.

Expand workforce enablement

The cognitive load rises when multiple tools coexist:

Develop cross-tool enablement plans so clinicians know when and how to use each tool.
Provide resources explaining integration points and how to resolve conflicting outputs.
Maintain a centralized resource hub — FAQs, escalation pathways, and workflow guides in one place.

Monitor performance at the ecosystem level

Multiple tools demand cross-model oversight:

Extend monitoring dashboards to track performance and safety across all deployed models.
Create ecosystem-level alerts to detect contradictory recommendations and equity gaps.
Empower your AI Safety & Performance Board to adjudicate conflicts and set retraining priorities.

Scaling with intention

Implementing AI isn’t the goal. Sustaining safe, effective, and equitable performance at scale is.

The leap from pilot to enterprise isn’t about technology — it’s about governance, integration, and trust. Scaling AI well requires:

Proven evidence of safety and impact.
Playbooks for consistent deployment.
Workforce enablement and clinical championing.
Continuous, cross-model monitoring.

Health systems that succeed treat scaling not as a one-off rollout but as an organizational capability — a shift toward operating as a learning health system. When done right, scaling accelerates operational efficiency, improves patient outcomes, and positions your organization as a leader in responsible, evidence-driven innovation. And the cycle doesn’t stop here: each new deployment, model update, or enterprise tool should take you back to Plan — reassessing priorities, refreshing guardrails, and aligning stakeholders to keep AI safe, strategic, and sustainable.