Lecture 3.0.9: Rule Based vs ML CDS Architectures, FHIR R4, CDS Hooks, SMART on FHIR
Why It Matters
Standardized, interoperable CDS delivers real‑time, evidence‑based guidance at the point of care, improving safety and unlocking AI‑driven outcomes.
Key Takeaways
- •Rule‑based CDS uses deterministic if‑then logic, hard to scale.
- •ML‑based CDS provides probabilistic risk scores, learns from data.
- •FHIR R4 standardizes health data exchange like a universal USB plug.
- •CDS Hooks trigger external decision‑support services via JSON payloads.
- •SMART on FHIR enables rich, interactive apps within EHR workflows.
Summary
The lecture introduces modern clinical decision support (CDS) architectures, covering rule‑based versus machine‑learning models, and the enabling standards of FHIR R4, CDS Hooks, and SMART on FHIR.
It outlines the evolution from paper‑based guidelines (Gen 1) to hard‑coded EHR logic (Gen 2), interoperable external services (Gen 3), and AI‑driven predictive models (Gen 4). Rule‑based systems rely on deterministic thresholds, while ML models generate probabilistic risk scores that require large training datasets.
A Python demo shows a FHIR Observation for blood pressure triggering a CDS Hook; when systolic exceeds 140 mmHg the service returns a warning card with a recommendation. The example illustrates JSON bundles, card responses, and how SMART on FHIR can launch richer, interactive applications.
Adopting these standards promises scalable, transparent decision support that can be updated centrally, reducing clinician overload and enabling predictive analytics across institutions.
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