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Clinical Decision Support Documents

Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development:

Claude Code Knowledge Pack7/10/2026

Overview

Clinical Decision Support Documents

Description

Generate professional clinical decision support (CDS) documents for pharmaceutical companies, clinical researchers, and medical decision-makers. This skill specializes in analytical, evidence-based documents that inform treatment strategies and drug development:

  1. Patient Cohort Analysis - Biomarker-stratified group analyses with statistical outcome comparisons
  2. Treatment Recommendation Reports - Evidence-based clinical guidelines with GRADE grading and decision algorithms

All documents are generated as publication-ready LaTeX/PDF files optimized for pharmaceutical research, regulatory submissions, and clinical guideline development.

Note: For individual patient treatment plans at the bedside, use the treatment-plans skill instead. This skill focuses on group-level analyses and evidence synthesis for pharmaceutical/research settings.

Writing Style: For publication-ready documents targeting medical journals, consult the venue-templates skill's medical_journal_styles.md for guidance on structured abstracts, evidence language, and CONSORT/STROBE compliance.

Capabilities

Document Types

Patient Cohort Analysis

  • Biomarker-based patient stratification (molecular subtypes, gene expression, IHC)
  • Molecular subtype classification (e.g., GBM mesenchymal-immune-active vs proneural, breast cancer subtypes)
  • Outcome metrics with statistical analysis (OS, PFS, ORR, DOR, DCR)
  • Statistical comparisons between subgroups (hazard ratios, p-values, 95% CI)
  • Survival analysis with Kaplan-Meier curves and log-rank tests
  • Efficacy tables and waterfall plots
  • Comparative effectiveness analyses
  • Pharmaceutical cohort reporting (trial subgroups, real-world evidence)

Treatment Recommendation Reports

  • Evidence-based treatment guidelines for specific disease states
  • Strength of recommendation grading (GRADE system: 1A, 1B, 2A, 2B, 2C)
  • Quality of evidence assessment (high, moderate, low, very low)
  • Treatment algorithm flowcharts with TikZ diagrams
  • Line-of-therapy sequencing based on biomarkers
  • Decision pathways with clinical and molecular criteria
  • Pharmaceutical strategy documents
  • Clinical guideline development for medical societies

Clinical Features

  • Biomarker Integration: Genomic alterations (mutations, CNV, fusions), gene expression signatures, IHC markers, PD-L1 scoring
  • Statistical Analysis: Hazard ratios, p-values, confidence intervals, survival curves, Cox regression, log-rank tests
  • Evidence Grading: GRADE system (1A/1B/2A/2B/2C), Oxford CEBM levels, quality of evidence assessment
  • Clinical Terminology: SNOMED-CT, LOINC, proper medical nomenclature, trial nomenclature
  • Regulatory Compliance: HIPAA de-identification, confidentiality headers, ICH-GCP alignment
  • Professional Formatting: Compact 0.5in margins, color-coded recommendations, publication-ready, suitable for regulatory submissions

Pharmaceutical and Research Use Cases

This skill is specifically designed for pharmaceutical and clinical research applications:

Drug Development

  • Phase 2/3 Trial Analyses: Biomarker-stratified efficacy and safety analyses
  • Subgroup Analyses: Forest plots showing treatment effects across patient subgroups
  • Companion Diagnostic Development: Linking biomarkers to drug response
  • Regulatory Submissions: IND/NDA documentation with evidence summaries

Medical Affairs

  • KOL Education Materials: Evidence-based treatment algorithms for thought leaders
  • Medical Strategy Documents: Competitive landscape and positioning strategies
  • Advisory Board Materials: Cohort analyses and treatment recommendation frameworks
  • Publication Planning: Manuscript-ready analyses for peer-reviewed journals

Clinical Guidelines

  • Guideline Development: Evidence synthesis with GRADE methodology for specialty societies
  • Consensus Recommendations: Multi-stakeholder treatment algorithm development
  • Practice Standards: Biomarker-based treatment selection criteria
  • Quality Measures: Evidence-based performance metrics

Real-World Evidence

  • RWE Cohort Studies: Retrospective analyses of patient cohorts from EMR data
  • Comparative Effectiveness: Head-to-head treatment comparisons in real-world settings
  • Outcomes Research: Long-term survival and safety in clinical practice
  • Health Economics: Cost-effectiveness analyses by biomarker subgroup

When to Use

Use this skill when you need to:

  • Analyze patient cohorts stratified by biomarkers, molecular subtypes, or clinical characteristics
  • Generate treatment recommendation reports with evidence grading for clinical guidelines or pharmaceutical strategies
  • Compare outcomes between patient subgroups with statistical analysis (survival, response rates, hazard ratios)
  • Produce pharmaceutical research documents for drug development, clinical trials, or regulatory submissions
  • Develop clinical practice guidelines with GRADE evidence grading and decision algorithms
  • Document biomarker-guided therapy selection at the population level (not individual patients)
  • Synthesize evidence from multiple trials or real-world data sources
  • Create clinical decision algorithms with flowcharts for treatment sequencing

Do NOT use this skill for:

  • Individual patient treatment plans (use treatment-plans skill)
  • Bedside clinical care documentation (use treatment-plans skill)
  • Simple patient-specific treatment protocols (use treatment-plans skill)

Visual Enhancement with Scientific Schematics

⚠️ MANDATORY: Every clinical decision support document MUST include at least 1-2 AI-generated figures using the scientific-schematics skill.

This is not optional. Clinical decision documents require clear visual algorithms. Before finalizing any document:

  1. Generate at minimum ONE schematic or diagram (e.g., clinical decision algorithm, treatment pathway, or biomarker stratification tree)
  2. For cohort analyses: include patient flow diagram
  3. For treatment recommendations: include decision flowchart

How to generate figures:

  • Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
  • Simply describe your desired diagram in natural language
  • Nano Banana Pro will automatically generate, review, and refine the schematic

How to generate schematics:

python scripts/generate_schematic.py "your diagram description" -o figures/output.png

The AI will automatically:

  • Create publication-quality images with proper formatting
  • Review and refine through multiple iterations
  • Ensure accessibility (colorblind-friendly, high contrast)
  • Save outputs in the figures/ directory

When to add schematics:

  • Clinical decision algorithm flowcharts
  • Treatment pathway diagrams
  • Biomarker stratification trees
  • Patient cohort flow diagrams (CONSORT-style)
  • Survival curve visualizations
  • Molecular mechanism diagrams
  • Any complex concept that benefits from visualization

For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.

Document Structure

CRITICAL REQUIREMENT: All clinical decision support documents MUST begin with a complete executive summary on page 1 that spans the entire first page before any table of contents or detailed sections.

Page 1 Executive Summary Structure

The first page of every CDS document should contain ONLY the executive summary with the following components:

Required Elements (all on page 1):

  1. Document Title and Type

    • Main title (e.g., "Biomarker-Stratified Cohort Analysis" or "Evidence-Based Treatment Recommendations")
    • Subtitle with disease state and focus

  2. Report Information Box (using colored tcolorbox)

    • Document type and purpose
    • Date of analysis/report
    • Disease state and patient population
    • Author/institution (if applicable)
    • Analysis framework or methodology

  3. Key Findings Boxes (3-5 colored boxes using tcolorbox)

    • Primary Results (blue box): Main efficacy/outcome findings
    • Biomarker Insights (green box): Key molecular subtype findings
    • Clinical Implications (yellow/orange box): Actionable treatment implications
    • Statistical Summary (gray box): Hazard ratios, p-values, key statistics
    • Safety Highlights (red box, if applicable): Critical adverse events or warnings

Visual Requirements:

  • Use \ hispagestyle{empty} to remove page numbers from page 1
  • All content must fit on page 1 (before \ ewpage)
  • Use colored tcolorbox environments with different colors for visual hierarchy
  • Boxes should be scannable and highlight most critical information
  • Use bullet points, not narrative paragraphs
  • End page 1 with \ ewpage before table of contents or detailed sections

Example First Page LaTeX Structure:

\\maketitle
\	hispagestyle{empty}

% Report Information Box
\\begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Report Information]
\	extbf{Document Type:} Patient Cohort Analysis\\\\
\	extbf{Disease State:} HER2-Positive Metastatic Breast Cancer\\\\
\	extbf{Analysis Date:} \	oday\\\\
\	extbf{Population:} 60 patients, biomarker-stratified by HR status
\\end{tcolorbox}

\\vspace{0.3cm}

% Key Finding #1: Primary Results
\\begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Primary Efficacy Results]
\\begin{itemize}
    \\item Overall ORR: 72\\% (95\\% CI: 59-83\\%)
    \\item Median PFS: 18.5 months (95\\% CI: 14.2-22.8)
    \\item Median OS: 35.2 months (95\\% CI: 28.1-NR)
\\end{itemize}
\\end{tcolorbox}

\\vspace{0.3cm}

% Key Finding #2: Biomarker Insights
\\begin{tcolorbox}[colback=green!5!white, colframe=green!75!black, title=Biomarker Stratification Findings]
\\begin{itemize}
    \\item HR+/HER2+: ORR 68\\%, median PFS 16.2 months
    \\item HR-/HER2+: ORR 78\\%, median PFS 22.1 months
    \\item HR status significantly associated with outcomes (p=0.041)
\\end{itemize}
\\end{tcolorbox}

\\vspace{0.3cm}

% Key Finding #3: Clinical Implications
\\begin{tcolorbox}[colback=orange!5!white, colframe=orange!75!black, title=Clinical Recommendations]
\\begin{itemize}
    \\item Strong efficacy observed regardless of HR status (Grade 1A)
    \\item HR-/HER2+ patients showed numerically superior outcomes
    \\item Treatment recommended for all HER2+ MBC patients
\\end{itemize}
\\end{tcolorbox}

\
ewpage
\	ableofcontents  % TOC on page 2
\
ewpage  % Detailed content starts page 3

Patient Cohort Analysis (Detailed Sections - Page 3+)

  • Cohort Characteristics: Demographics, baseline features, patient selection criteria
  • Biomarker Stratification: Molecular subtypes, genomic alterations, IHC profiles
  • Treatment Exposure: Therapies received, dosing, treatment duration by subgroup
  • Outcome Analysis: Response rates (ORR, DCR), survival data (OS, PFS), DOR
  • Statistical Methods: Kaplan-Meier survival curves, hazard ratios, log-rank tests, Cox regression
  • Subgroup Comparisons: Biomarker-stratified efficacy, forest plots, statistical significance
  • Safety Profile: Adverse events by subgroup, dose modifications, discontinuations
  • Clinical Recommendations: Treatment implications based on biomarker profiles
  • Figures: Waterfall plots, swimmer plots, survival curves, forest plots
  • Tables: Demographics table, biomarker frequency, outcomes by subgroup

Treatment Recommendation Reports (Detailed Sections - Page 3+)

Page 1 Executive Summary for Treatment Recommendations should include:

  1. Report Information Box: Disease state, guideline version/date, target population
  2. Key Recommendations Box (green): Top 3-5 GRADE-graded recommendations by line of therapy
  3. Biomarker Decision Criteria Box (blue): Key molecular markers influencing treatment selection
  4. Evidence Summary Box (gray): Major trials supporting recommendations (e.g., KEYNOTE-189, FLAURA)
  5. Critical Monitoring Box (orange/red): Essential safety monitoring requirements

Detailed Sections (Page 3+):

  • Clinical Context: Disease state, epidemiology, current treatment landscape
  • Target Population: Patient characteristics, biomarker criteria, staging
  • Evidence Review: Systematic literature synthesis, guideline summary, trial data
  • Treatment Options: Available therapies with mechanism of action
  • Evidence Grading: GRADE assessment for each recommendation (1A, 1B, 2A, 2B, 2C)
  • Recommendations by Line: First-line, second-line, subsequent therapies
  • Biomarker-Guided Selection: Decision criteria based on molecular profiles
  • Treatment Algorithms: TikZ flowcharts showing decision pathways
  • Monitoring Protocol: Safety assessments, efficacy monitoring, dose modifications
  • Special Populations: Elderly, renal/hepatic impairment, comorbidities
  • References: Full bibliography with trial names and citations

Output Format

MANDATORY FIRST PAGE REQUIREMENT:

  • Page 1: Full-page executive summary with 3-5 colored tcolorbox elements
  • Page 2: Table of contents (optional)
  • Page 3+: Detailed sections with methods, results, figures, tables

Document Specifications:

  • Primary: LaTeX/PDF with 0.5in margins for compact, data-dense presentation
  • Length: Typically 5-15 pages (1 page executive summary + 4-14 pages detailed content)
  • Style: Publication-ready, pharmaceutical-grade, suitable for regulatory submissions
  • First Page: Always a complete executive summary spanning entire page 1 (see Document Structure section)

Visual Elements:

  • Colors:
    • Page 1 boxes: blue=data/information, green=biomarkers/recommendations, yellow/orange=clinical implications, red=warnings
    • Recommendation boxes (green=strong recommendation, yellow=conditional, blue=research needed)
    • Biomarker stratification (color-coded molecular subtypes)
    • Statistical significance (color-coded p-values, hazard ratios)
  • Tables:
    • Demographics with baseline characteristics
    • Biomarker frequency by subgroup
    • Outcomes table (ORR, PFS, OS, DOR by molecular subtype)
    • Adverse events by cohort
    • Evidence summary tables with GRADE ratings
  • Figures: