Pharma & Clinical Ops: Trial Decisions With Auditable Evidence¶
Case study → pharma / clinical ops
Clinical decisions require provenance, protocol constraints, and abstention.
Clinical operations are full of non-negotiable constraints: protocol, consent, safety reporting, and regulatory requirements. A decision-grade system must surface evidence paths and refuse when the chain is incomplete.
The question
Can AI support trial operations (eligibility checks, deviations, safety signals, vendor coordination) while enforcing protocol and producing artifacts that withstand audit and inspection?
Failure modes to avoid
Protocol drift
Recommendations that ignore inclusion/exclusion criteria or exception procedures.
Evidence ambiguity
Summaries that merge incompatible sources across versions and sites.
Unsafe overconfidence
Fluent outputs that hide missing data or unverified assumptions.
Non-auditable workflows
If you can’t show provenance and rules applied, you can’t defend decisions.
What changes with constraint-gated evidence paths
flowchart TB;
Q["Operational question"] --> E["Retrieve evidence"];
E --> P["Causal path candidates"];
P --> G["Protocol + safety constraints"];
G -->|"Pass"| O["Recommendation + trace"];
G -->|"Fail"| X["Abstain + request missing data"];Diagram: provenance-first decision artifact
flowchart TB;
S["Sources</br>(protocol, logs, reports)"] --> C["Claims"];
C --> R["Rules applied"];
R --> D["Decision"];
D --> T["Trace + inspection bundle"];Outputs
Protocol-safe recommendations
Decisions bounded by eligibility, consent, and safety reporting rules.
Inspection-ready traces
Evidence, versions, rules applied, and justification captured as artifacts.
Faster deviation triage
Non-local dependencies across sites and vendors become navigable structures.
Deterministic escalation
When evidence is insufficient, the system refuses and specifies what is required.