By Contributor,Sahar Hashmi
Copyright forbes
From pilot to enterprise scale, AI in healthcare succeeds only when it proves reliability and delivers measurable outcomes. Evals and KPIs are the twin pillars driving trust, adoption and ROI.
In healthcare, the promise of artificial intelligence is no longer theoretical. AI is already transforming diagnosis, streamlining workflows and improving patient outcomes. Yet most pilots never scale. Why? Because in healthcare, capability alone is never enough. For AI to move beyond pilots and achieve scalable enterprise-wide adoption in EMR platforms like Epic or Cerner, it must prove both technical reliability and measurable value. That is why rigorous AI evaluations (evals) and well-defined Key Performance Indicators (KPIs) are the non-negotiable pillars for success.
AI Evals: The Proof Before Deployment
AI evals are the “test drives” of healthcare AI. They confirm that a system delivers accurate results, performs consistently, avoids harmful errors and identifies situations where it may struggle. Without these evaluations, hospitals cannot trust AI with patient care. Just as no new drug reaches patients without phase-based clinical trials, no AI solution should be scaled without rigorous validation.
A notable example comes from Moorfields Eye Hospital, which collaborated with DeepMind to develop an AI system capable of diagnosing over 50 eye diseases with 94% accuracy. The system underwent rigorous validation on thousands of retinal scans before any clinical deployment, demonstrating reliability and safety in real-world conditions. This case underscores a critical point. No matter how advanced or promising an AI model appears, it must be supported by rigorous evidence to earn the trust of clinicians, regulators and healthcare organizations.
KPIs: Measuring Impact and ROI
While evals prove capability, KPIs quantify value. Hospitals need evidence that AI improves patient outcomes, reduces time to diagnosis, enhances guideline adherence and boosts satisfaction. Clinical leaders are less concerned with technical novelty than with measurable results that align with institutional priorities such as improving quality metrics, reducing costs and advancing equity of care.
The University Hospital Grenoble AI assistant demonstrates this principle. Evaluated across eight hospitals with data from 50,000 admissions, it safely and reliably improved triage speed and diagnostic accuracy for trauma patients. This combination of technical readiness and measurable impact enabled full-scale integration in clinical workflows. It also showed how KPIs can be designed to reflect both clinical performance and operational efficiency, bridging the gap between frontline care and executive decision-making.
Driving ROI Through Evals and KPIs
AI evals and KPIs work together to drive return on investment in healthcare. Evals reduce risk by confirming that AI is safe, reliable and ready for deployment, while KPIs translate technical performance into measurable clinical and financial outcomes. Hospitals that apply this dual framework capture both hard ROI such as reduced readmissions, shorter wait times and greater staff efficiency and soft ROI which includes improved patient satisfaction, better clinician decision-making and reduced burnout. These combined outcomes are critical because they demonstrate not only financial sustainability but also alignment with broader missions of patient-centered care and workforce resilience.
Non-Negotiable for EMR Integration
Leading EMR platforms like Epic and Cerner treat AI evals and KPIs as essential. They ensure models perform reliably across diverse patient populations, meet regulatory and ethical standards and deliver measurable improvements in care and workflow. Without these safeguards, health systems risk deploying AI that may work in controlled pilots but fail in the complexity of real-world practice. In healthcare, capability alone is never enough—impact drives adoption.
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Strategic Imperative Insights
AI solutions that combine rigorous evals with clear KPIs are the only tools capable of moving from experimental pilot to fully integrated EMR feature. For hospital executives and clinical leaders, they provide a roadmap to safe, scalable, measurable and financially responsible AI adoption.
Moving forward, regulatory frameworks are likely to demand standardized AI evaluation protocols, much like clinical trial phases in drug development. At the same time, value-based care models will push hospitals to tie KPIs more directly to patient outcomes, equity benchmarks and cost savings. The institutions that establish disciplined AI evaluation and measurement strategies today will be the ones shaping tomorrow’s healthcare standards—and leading the next wave of innovation.
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