HEALTHCARE
From concept to production across 120 hospital sites in 9 months — an AI system that helps clinicians make faster, more accurate diagnostic decisions.
A national healthcare network was losing critical time in diagnostic workflows. Clinicians were spending 40% of their time on administrative documentation rather than patient care. Diagnostic variability across their 120 sites was leading to inconsistent patient outcomes and suboptimal resource allocation. They had attempted two prior AI initiatives with different vendors — both stalled at pilot stage. The organization needed a partner who could take AI from research to production at enterprise scale, while meeting the rigorous regulatory and safety requirements of clinical environments.
Fleet Studio applied the FRETA framework to assess organizational readiness across clinical, technical, and operational dimensions. The assessment revealed that prior failures weren't technology problems — they were integration and governance problems. Previous vendors had built impressive AI models in isolation, then tried to bolt them onto clinical workflows. Clinicians rightly rejected systems that required them to change how they worked.
We designed a fundamentally different approach: embed AI assistance directly into existing clinical workflows rather than requiring clinicians to change. The system would augment clinical judgment, never replace it. And it would live inside the EHR systems clinicians already used every day.
Diagnosed why prior initiatives failed: governance gaps, lack of clinical validation, poor workflow integration.
Embedded AI assistance directly into existing EHR workflows. Clinicians don't change how they work.
Validated models against board-certified specialist panels, not just statistical benchmarks.
We built a clinical NLP engine trained on 2M+ anonymized medical records, combining structured clinical data with unstructured notes. The real innovation wasn't the model — it was the integration and governance around it.
Trained on 2M+ anonymized medical records. Understands both structured data and unstructured clinical notes.
Models deliver diagnostic suggestions validated against board-certified specialist diagnoses. 98.6% accuracy benchmark.
Integrates directly into Epic EHR systems via FHIR APIs. No workflow disruption, no separate systems.
AI suggests, clinicians decide. Every diagnostic recommendation includes supporting evidence. Clinicians maintain full control.
Continuous accuracy tracking, bias detection across demographics, real-time drift alerting for model degradation.
Quarterly model review boards with clinicians, regulators, and data scientists. Audit trails for every recommendation.
The system was designed to earn trust through transparency. Every diagnostic suggestion included the evidence supporting it — which findings drove the recommendation, confidence scores, and alternative diagnoses to consider.
But the most important outcome was something harder to quantify: clinicians trusted the system. Because it was designed for them, integrated into their workflows, and delivered evidence with every recommendation, AI stopped feeling like something imposed on clinical practice and started feeling like a trusted colleague.
Clinicians have deep domain expertise and accountability. Systems designed to augment that judgment are far more likely to be adopted and trusted than systems that try to replace clinical judgment.
Our FRETA assessment caught this critical insight. The vendors before us had built good models but failed on integration and governance. We focused on fixing the real problems.
By building FHIR-native EHR integration, we eliminated the need for clinicians to learn a new system or change their workflows. The AI became invisible — it was just there, helping.
Board-certified specialists validating model accuracy built far more trust than any technical demo could. Clinicians care about clinical outcomes, not machine learning papers.
Every recommendation includes supporting evidence. Clinicians need to understand why the system is suggesting something before they'll use it in patient care decisions.
The clinical governance framework — quarterly review boards, bias monitoring, audit trails — scaled across 120 sites better than the technology itself. Governance built organizational muscle memory around responsible AI use.
Healthcare AI is complex — it requires deep domain expertise, clinical validation, and governance. Let's discuss how to take your healthcare AI initiative from research to production responsibly. We'll help you avoid the pitfalls that derailed your competitors.