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The GLP-1 workflow is one example of a broader principle inside Consensus Center: the engine may identify a possible clinical pathway, but it must never authorize treatment. Treatment-related logic is one of the highest-risk areas of any preventive health platform. A biomarker signal can suggest that a patient may benefit from clinical review, but it does not mean the patient should automatically receive a medication, supplement, hormone, peptide, procedure, or protocol. Consensus Engine handles this by separating signal detection, clinical pathway flagging, physician review, and treatment execution.

The core boundary

The system preserves a clear boundary between decision support and medical action. AI agents flag, interpret as decision support, and route. They never prescribe and never make a clinical decision. Treatment eligibility logic produces clinician-required flags, not authorizations.

What a treatment flag means

A treatment flag is not a recommendation to treat. It is a structured signal that a clinician may need to review a possible pathway. It indicates possible relevance (patient data matches a reviewed pathway condition), is structured (from deterministic schema logic), evidence-linked, safety-limited (suppressions, contraindications, and hardening rules may block or route the case), clinician-required, traceable, and not final. The GLP-1 flow uses this model directly: if conditions are met, a clinician-required flag is raised, never an authorization. The same pattern applies to every treatment-adjacent pathway.

General pathway structure

Every treatment pathway follows a controlled sequence. This keeps the engine useful without letting it become autonomous.
1

Collect intake facts

Avoids pathway evaluation from incomplete context.
2

Normalize clinical data

Prevents unit, alias, or format errors.
3

Compute derived values

Supports structured interpretation.
4

Apply calibrations if relevant

Prevents misclassification where context changes meaning.
5

Evaluate pathway criteria

Produces an advisory flag only.
6

Apply suppressions

Blocks interpretation when context makes it unsafe.
7

Apply hardening rules

Prevents over-diagnosis or premature conclusions.
8

Screen contraindications

Surfaces safety blockers.
9

Assign review state

Routes ambiguity to a clinician.
10

Clinician reviews

The medical decision remains human.
11

Record decision trace

Creates auditability.
12

Monitor follow-up

Supports ongoing safety.

Pathway categories

Consensus Engine can support multiple pathway types, but each remains under physician governance. The engine may help identify which pathway deserves attention; it does not decide which treatment the patient receives.

Protocol linkage

A protocol is different from a rule. A rule detects a condition; a protocol defines how clinicians evaluate and manage a pathway. The architecture identifies treatment and protocol linkage tables such as TREATMENTS, TREATMENT_*, and PROTOCOL_*.
Rules can route. Protocols can guide. Clinicians decide.

Required pathway inputs

No pathway should run on incomplete data if missing facts materially change interpretation. Each pathway defines required, optional, blocking, review, and longitudinal inputs.

Contraindication governance

Treatment-adjacent pathways must include contraindication handling. A contraindication screen is a clinical safety input, not a simple form field. The GLP-1 workflow includes contraindication screening for conditions such as MTC, MEN2, and pancreatitis before any clinician-required advisory flag can be evaluated safely. Other pathways follow the same pattern: collect safety context first, then route to clinician review.

Patient-facing language

Treatment-related language must be especially cautious. The patient should never see “You are eligible,” “The AI recommends this medication,” “You should start this protocol,” “Your treatment is approved,” or “Your result means you need this intervention.” Patients never see “eligible.” They see “possible candidate, requires medical evaluation.” That wording standard is generalized across all treatment pathways.

Clinician-facing view

The clinician sees the full reasoning behind any pathway flag: raw values, normalized values, derived values, runtime facts, criteria matched, criteria not met, the safety screen, suppression rules, hardening rules, the calibration trace, the evidence trace, lifecycle status, a patient-language draft, and the decision ID. For treatment pathways, this trace protects both the clinician and the patient.

Pathway approval lifecycle

Treatment-related logic moves through the same clinical lifecycle as other rules, but with stricter review. Treatment pathways should be among the most tightly controlled objects in the schema.
Additional requirements before activation: a protocol owner assigned, evidence source attached, contraindication logic reviewed, patient language approved, clinician workflow tested, decision trace validated, golden test cases passed, rollback path defined, and Medical Director sign-off recorded.

Pathway validation

Every pathway needs validation before activation, and for treatment pathways validation failures should be release blockers.

Monitoring after activation

Treatment pathways require monitoring after release to answer whether the pathway is clinically useful, too sensitive, too narrow, or unsafe in practice. Suggested metrics: advisory flags generated, flags reviewed by clinicians, flags rejected by clinicians, missing-data blocks, contraindication blocks, patient-language incidents, treatment starts after review, adverse event review triggers, protocol deviations, rule override frequency, and outcome follow-up completeness.

Handling clinician disagreement

Clinician disagreement is a signal, not a failure. If clinicians frequently reject a pathway flag, the rule may be too broad, missing context, or using weak criteria. If clinicians often override a negative decision, the rule may be too narrow. Because decisions are traceable, these patterns can be studied rather than guessed.

Pathway retirement

A pathway should be retired when it is no longer clinically appropriate, safe, or supported. Triggers include evidence changes, protocol changes, an emerging safety issue, high clinician rejection, poor monitoring performance, changed regulatory guidance, changed partner requirements, or a better pathway replacing it. The lifecycle states DEPRECATED and RETIRED ensure outdated logic does not remain active indefinitely.

AI-agent role in treatment pathways

AI agents can make treatment pathways easier to operate, but only within strict boundaries. This keeps AI operational, not clinical-authoritative.

Decision trace for pathways

Every pathway evaluation produces a structured trace that proves the system did not silently prescribe or authorize care: pathway ID, patient facts, biomarker values, derived values, criteria result, safety gates, calibration, evidence or protocol source, lifecycle status, output (advisory flag, insufficient data, blocked, or review state), visibility, clinician decision and rationale, and follow-up plan. Every treatment pathway should meet the following standard before patient exposure. This turns treatment governance into an auditable checklist.
Deterministic criteria, evidence source, protocol owner, Medical Director review, contraindication screen, suppression and hardening rules, patient-language approval, clinician-required review, golden test cases, boot validator pass, runtime trace, monitoring plan, and rollback plan — all present.