clinical-decision-support
This Claude Code skill generates publication-ready clinical decision support documents in LaTeX/PDF format for pharmaceutical and clinical research settings. Use it to create patient cohort analyses with biomarker stratification and statistical comparisons, or treatment recommendation reports featuring GRADE evidence grading and clinical decision algorithms. It produces group-level analyses optimized for drug development and regulatory submissions rather than individual patient bedside care.
git clone --depth 1 https://github.com/K-Dense-AI/claude-scientific-writer /tmp/clinical-decision-support && cp -r /tmp/clinical-decision-support/skills/clinical-decision-support ~/.claude/skills/clinical-decision-supportSKILL.md
# 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 exploratory findings from confirmatory analyses. ### 2. Select the Artifact | Need | Asset | Script | |---|---|---| | Intended-use/governance review | `assets/artifact_intended_use_template.json` | `scripts/validate_cds_artifact.py` | | GRADE evidence profile | `assets/evidence_profile_template.json` | `scripts/evidence_profile_check.py` | | Aggregate model/biomarker evaluation | `assets/aggregate_model_evaluation_template.json` | `scripts/model_biomarker_evaluation.py` | | Aggregate cohort table | `assets/aggregate_cohort_table_template.json` | `scripts/cohort_table_generator.py` | | Survival analysis plan | `assets/survival_analysis_plan_template.json` | `scripts/survival_plan_validator.py` | | Logic traceability matrix | `assets/decision_logic_traceability_template.json` | `scripts/decision_logic_traceability.py` | | De-identification process review | `assets/deidentification_checklist_template.json` | `scripts/deidentification_checklist.py` | ### 3. Run Locally All helpers are dependency-free: ```bash python3 scripts/validate_cds_artifact.py --help python3 scripts/evidence_profile_check.py --help python3 scripts/model_biomarker_evaluation.py --help python3 scripts/cohort_table_generator.py --help python3 scripts/survival_plan_validator.py --help python3 scripts/decision_logic_traceability.py --help python3 scripts/deidentification_checklist.py --help ``` Write outputs only to a reviewed local directory. Never place generated reports in an EHR, alerting system, clinical portal, or device workflow. ### 4. Human Review Require review proportionate to the artifact: - methodologist/statistician for design and analysis; - domain expert for clinical-scientific context; - privacy officer or qualified expert for disclosure decisions; - regulatory or legal counsel for jurisdiction-specific interpretations; - human-factors specialist for user studies; - authorized governance owner for release and change control. Script success means only that declared fields and internal consistency checks passed. ## GRADE Evidence Profiles Do not infer a certainty rating from article text, study design alone, p-values, or keywords. Do not use the legacy `1A/2B` shorthand as if it were universal GRADE output. For each important outcome, a human panel must document: - risk of bias; - inconsistency; - indirectness; - imprecision; - publication bias; - any applicable upgrading considerations; - effect estimate and uncertainty; - rationale and source IDs for every judgment; - final certainty judgment and named review role. The checker
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
Use this skill any time a .pptx or .potx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx or .potx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates (.potx), layouts, speaker notes, or comments. Trigger whenever the user mentions \"deck,\" \"slides,\" \"presentation,\" or references a .pptx or .potx filename, regardless of what they plan to do with the content afterward. If a .pptx or .potx file needs to be opened, created, or touched, use this skill.
Create, edit, analyze, or convert Excel spreadsheets (.xlsx, .xlsm, .xltx) where the workbook file is the primary deliverable. Use for formulas, formatting, financial models, multi-sheet workbooks, and tabular cleanup exported to Excel. Also applies to .csv/.tsv when the user wants spreadsheet output. Do NOT use for Word documents, HTML reports, standalone Python scripts, database pipelines, or Google Sheets API work.
Generate or edit images with AI models through the OpenRouter Image API (Gemini, FLUX, Seedream, Recraft, GPT-Image). Use for photos, illustrations, artwork, concept art, visual assets, logos, and image editing or compositing from reference images. For flowcharts, circuits, pathways, and other technical diagrams, use the scientific-schematics skill instead.
Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.