RL Environment
RL environment for clinical protocol-execution agents: a deterministic FHIR server paired with an auditable, rule-based verifier, built on MedAgentBench-v3 (508 corrected tasks across 20 task types — lab & vitals review, threshold decisions, FHIR order entry, referrals), with GRPO training and a frontier-model leaderboard.

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Environment specs
Persona / role
Problem
Hospitals run countless routine tasks like checking whether a lab value crosses a threshold, then placing the correct electronic order. AI agents could do these, and reinforcement learning (learning by trial and error) is appealing: a clinician writes the correctness rules once, and the AI practices endlessly without anyone grading each attempt. But the standard test for such agents was quietly broken: an agent that did nothing at all still "passed" 42% of cases, because many patients genuinely need no action, teaching the AI the worst lesson: stay idle.
Solution
We rebuilt the benchmark into 508 corrected tasks with a self-contained practice environment and automatic grader, then evaluated everything from a small open model to today's leading systems.
Impact
Even the strongest models top out near 78% - clinical protocol execution is genuinely hard, demanding exact medical codes and reliable act-or-wait judgment that current systems miss. Our contribution is a trustworthy way to measure and train these agents: a rigorously audited benchmark and environment that closes the "do-nothing" loophole; the first study of reinforcement learning from automated feedback in this clinical setting; a task taxonomy that predicts when such training helps; and an audit checklist so broken benchmarks stop inflating scores.
Security
Disciplined security and privacy practices aligned with global standards to protect sensitive data, intellectual property, and model assets throughout the AI lifecycle.
Centific applies rigorous security, access control, and auditability standards to safeguard enterprise data, human workflows, and AI systems at scale.
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