clinical-decision-support

Clinical Decision-Support Research and Evaluation

Hard Safety Boundary

This skill produces research, evaluation, documentation, and governance artifacts only.

Never use it to:

• diagnose or classify a person;
• recommend, select, sequence, start, stop, or modify treatment;
• calculate or communicate a patient-specific dose;
• triage, prioritize, alarm, alert, or determine urgency;
• make or automate a patient-specific clinical decision;
• support bedside, point-of-care, or live clinical operation;
• replace professional judgment or a validated, authorized clinical system;
• claim FDA authorization, regulatory conformity, HIPAA compliance, or legal compliance.

If a request could affect care for a person, stop the workflow and route the matter to a licensed healthcare professional using locally validated and appropriately authorized systems. Do not redirect to another skill for patient-specific care.

In Scope

• Intended-use and limitation statements for research artifacts
• Aggregate cohort table shells with disclosure controls
• Statistical analysis plans and survival-analysis plan review
• Aggregate model or biomarker performance evaluation
• Transparent GRADE evidence-profile checklists
• Evidence-source and decision-logic traceability
• De-identification process checklists
• Fairness, subgroup, calibration, uncertainty, external-validation, monitoring, change-control, audit, and human-factors documentation

Outputs remain drafts until qualified humans approve them. Reporting guidance improves transparency; it does not establish study quality, clinical utility, safety, effectiveness, authorization, or compliance.

Data Gate

Before any script:

1. Have the responsible human reviewer confirm and document that input is synthetic or aggregate. Scripts cannot establish this from field names or declared metadata.
2. Reject patient rows, records, narratives, identifiers, free text, dates tied to people, images, waveforms, or genomic sequences.
3. Keep source files local. Do not fetch URLs, call APIs, read environment variables, or send data to a model.
4. Set disclosure thresholds before producing tables.
5. Record provenance, data cut date, population, exclusions, missingness, and transformations.