CDSS

AI-powered systems assist clinicians with timely, accurate healthcare decision-making support.

Business impact

• Patient outcomes — Improved diagnosis and treatment lead to better health results
• Time to clinical decision — Faster data analysis shortens decision-making time
• Healthcare staff efficiency — Automation reduces manual tasks and cognitive load
• Operational efficiency — Streamlined workflows reduce resource waste and delays
• Diagnostic accuracy — AI reduces errors by integrating multiple data sources

Data requirements

• Electronic Health Records (EHR) (Structured) — Provide structured patient history and clinical data
• Medical imaging (Image) — Supply diagnostic images for AI analysis
• Wearable device data (Numeric) — Offer real-time physiological monitoring
• Laboratory test results (Numeric) — Deliver quantitative clinical measurements
• Clinical guidelines and literature (Text) — Inform AI with up-to-date medical knowledge

AI methods and techniques

• Predictive AI — Forecast patient condition progression and risk factors
• Generative AI — Generate clinical recommendations and explanations
• Symbolic AI — Apply rule-based logic for guideline adherence and alerts

AI models and model families

GPT-4, Claude, Infermedica Bayesian engine, Custom ML models

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: Healthdirect, Massachusetts General Hospital, NASA, Oxford University Clinical Research Unit OUCRU, Potentia Metrics.