clinical-decision-support

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.

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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 \thispagestyle{empty} to remove page numbers from page 1
• All content must fit on page 1 (before \newpage)
• 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 \newpage before table of contents or detailed sections

Example First Page LaTeX Structure:

\maketitle
\thispagestyle{empty}

% Report Information Box
\begin{tcolorbox}[colback=blue!5!white, colframe=blue!75!black, title=Report Information]
\textbf{Document Type:} Patient Cohort Analysis\\
\textbf{Disease State:} HER2-Positive Metastatic Breast Cancer\\
\textbf{Analysis Date:} \today\\
\textbf{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 statu

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