Case Study|Artificial Intelligence
Caystard Group built a HIPAA-compliant AI diagnostic assistance platform that helps radiologists prioritize critical cases, reducing average diagnosis turnaround time by 45%.
A multi-hospital healthcare network needed a way to help its radiology teams manage rising imaging volumes without compromising diagnostic accuracy or patient safety. Caystard developed an AI-assisted triage and diagnostic support system integrated directly into the network's existing PACS environment.
Radiology departments across the network were processing thousands of scans daily with a shortage of specialist radiologists. Critical findings (e.g., strokes, fractures, pulmonary embolisms) were sometimes delayed in the review queue simply due to volume, not urgency. The client needed an AI layer that could flag high-priority cases without replacing clinical judgment, while meeting strict healthcare data privacy requirements.
Caystard Group built a machine learning triage engine that analyzes incoming imaging studies and re-prioritizes the radiologist worklist based on likelihood of critical findings. The system integrates with the hospital's existing PACS/RIS infrastructure via HL7/FHIR interfaces and runs on a HIPAA-compliant private cloud environment with full audit logging.
Working closely with the network's clinical informatics team, Caystard's AI engineers trained and validated diagnostic triage models on de-identified historical imaging data, under a strict data governance and IRB-aligned review process. The models were tuned to flag time-sensitive conditions for immediate radiologist attention while leaving all final diagnostic decisions to clinicians.
Integration was one of the most delicate parts of the project: the platform had to slot into existing PACS/RIS workflows without disrupting radiologists' established review habits. Caystard built a lightweight worklist plugin that simply reordered study priority and added a visual flag, rather than introducing a new standalone tool clinicians would have to learn.
The platform was piloted at two hospitals before network-wide rollout, with continuous monitoring of false-positive/false-negative rates by the clinical team throughout. Post-rollout, the network reported significantly faster turnaround on critical cases and improved radiologist workload distribution.
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