clinical-decision-support

The saved excerpt is a snapshot from when this skill was listed. The external source remains the complete and most current version.

---
name: clinical-decision-support
description: Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.
license: MIT
compatibility: Python 3.11+; local files only; bundled scripts use the standard library and require no network, credentials, API keys, LLMs, or image services.
metadata:
  version: "2.2"
  skill-author: K-Dense Inc.
---

# 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. Confirm input is synthetic or aggregate.
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.

The scripts cap file size, groups, rows, and text length. They reject URL-like paths and common row-level keys. These controls reduce accidental misuse; they are not a privacy determination.

## Required Artifact Header

Every artifact must visibly include:

- `artifact_type`, title, version, status, owner, date, and change summary;
- intended purpose, intended users, aggregate population scope, and decision role;
- all prohibited uses from the hard boundary;
- data level and confirmation that no PHI or raw rows were supplied;
- limitations, uncertainty, and foreseeable failure modes;
- external-validation and subgroup applicability status;
- human-review roles, completion status, and approval boundary;
- source citations with versions or dates;
- monitoring, change-control, retirement, and audit expectations;
- the statement: **Not for patient care or live clinical use.**

Start from `assets/artifact_intended_use_template.json`.

## Workflow

### 1. Frame the Research Question

- Define the estimand or evaluation target before viewing results.
- Distinguish descriptive, prognostic, predictive, diagnostic-accuracy, and causal questions.
- Pre-specify outcomes, time origin, horizon, subgroups, cut points, missing-data handling, multiplicity, and sensitivity analyses.
- Separate explorato